system

Abstract

The system according to this embodiment aims to suggest the optimal dietary content based on individual body composition and dietary preferences. [Solution] The system according to the embodiment comprises a collection unit, a reception unit, a proposal unit, and a provision unit. The collection unit collects data confirmed by a body composition analyzer. The reception unit receives input on the ideal body shape and dietary preferences. The proposal unit analyzes the information collected by the collection unit and the reception unit and proposes the optimal diet. The provision unit provides the diet proposed by the proposal 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, including 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 as a 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 prior art, it has not been fully possible to propose an optimal diet based on individual body composition and dietary preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal diet based on individual body composition and dietary preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a reception unit, a proposal unit, and a serving unit. The data collection unit collects data confirmed by a body composition analyzer. The reception unit receives input regarding the ideal body shape and dietary preferences. The proposal unit analyzes the information collected by the data collection unit and the reception unit and proposes the optimal diet. The serving unit provides the diet proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal diet based on an individual's body composition and dietary preferences. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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), etc.

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 system according to an embodiment of the present invention is a system that proposes an optimal diet based on the user's current body composition, ideal body shape, body composition, and dietary preferences, as confirmed through a body composition analyzer. In this system, the user checks their own body composition using a body composition analyzer and inputs their ideal body shape, body composition, and dietary preferences, and the generating AI proposes an optimal diet. For example, the user uses a body composition analyzer to obtain data such as body fat percentage, muscle mass, and weight. This data indicates the user's current physical condition. Next, the user inputs their ideal body shape, body composition, and dietary preferences. For example, they input information such as "I want to reduce my body fat percentage," "I want to increase my muscle mass," or "I prefer a vegetable-centered diet." This information is input to the generating AI. The generating AI analyzes the input information and proposes an optimal diet to the user. For example, if the user inputs "I want to reduce my body fat percentage," the generating AI will propose a low-calorie, high-protein diet. Also, if the user inputs "I prefer a vegetable-centered diet," the generating AI will propose a meal plan that includes many vegetables. Through this mechanism, the user can obtain an optimal diet based on their current physical condition and ideals. This enables people striving for body shaping or weight loss to effectively achieve their goals. For example, if a user checks their body fat percentage with a body composition analyzer and inputs "I want to reduce my body fat percentage by 10%" as their ideal body shape, the generating AI will suggest a meal plan to achieve that goal. Specifically, it will suggest a meal plan that is low in calories and high in protein, or one that includes plenty of vegetables. In this way, users can obtain a meal plan that matches their goals. Furthermore, the generating AI also takes into account the user's dietary preferences, so it can suggest a meal plan that includes ingredients and dishes the user likes. This allows users to obtain a meal plan that they can easily stick to. This system provides the optimal meal program for people striving for body shaping or weight loss, and supports them in effectively achieving their goals. As a result, the system can suggest and provide the optimal meal plan based on the user's body composition data, ideal body shape, and dietary preferences.

[0029] The system according to the embodiment comprises a data collection unit, a reception unit, a proposal unit, and a provision unit. The data collection unit collects data confirmed by a body composition analyzer. The data collection unit can collect data such as body fat percentage, muscle mass, and weight. The data collection unit can, for example, automatically acquire data from the body composition analyzer and store it in a database. The data collection unit can also collect data manually entered by the user. For example, the user can enter data using a smartphone app. The reception unit accepts input of ideal body shape and dietary preferences. The reception unit can accept information such as the user's ideal body fat percentage, muscle mass, and dietary preferences. For example, the user can enter information using a smartphone app. The reception unit can also accept information using voice input or a touch panel. The proposal unit analyzes the information collected by the data collection unit and the reception unit and proposes the optimal diet. The proposal unit can, for example, use a generation AI to generate a meal plan based on the user's ideal body shape and dietary preferences. The suggestion unit can, for example, suggest meal plans that are low in calories and high in protein, or meal plans that include plenty of vegetables. The suggestion unit uses generative AI to generate meal plans based on the user's ideal body shape and dietary preferences. The generative AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. The delivery unit provides the meal content suggested by the suggestion unit. The delivery unit can, for example, display the meal plan to the user. The delivery unit can, for example, display the meal plan through a smartphone app. The delivery unit can also print and provide the meal plan. For example, if the user wishes, the meal plan can be downloaded in PDF format. In this way, the system can suggest and provide the optimal meal content based on the user's body composition data and ideal body shape and dietary preferences.

[0030] The data collection unit collects data confirmed by the body composition scale. For example, the unit can collect data such as body fat percentage, muscle mass, and weight. Specifically, the body composition scale measures body fat percentage, muscle mass, and weight when the user steps on it, and transmits this data to the data collection unit via Bluetooth® or Wi-Fi. The data collection unit automatically acquires this data and stores it in a database. The database is built on the cloud and has security measures in place, thus protecting user privacy. The data collection unit can also collect data manually entered by the user. For example, users can enter data using a smartphone app. The smartphone app is designed for easy data entry, and the entered data is immediately reflected in the database. Furthermore, the data collection unit can integrate with other health devices and applications. For example, it can collect data from smartwatches and fitness trackers and manage it as comprehensive health data. This allows the data collection unit to gain a multifaceted understanding of the user's health status and provide more accurate data.

[0031] The reception desk allows users to input their ideal body shape and dietary preferences. For example, the reception desk can input information such as the user's ideal body fat percentage, muscle mass, and dietary preferences. Specifically, users use a smartphone app to input their ideal body shape and dietary preferences. The app provides an intuitive interface to make it easy for users to input information. For example, sliders and checkboxes can be used to set ideal body fat percentage and muscle mass. Dietary preferences can also be selected from multiple options. Furthermore, the reception desk can also input information using voice input or a touch panel. Voice input allows users to input information simply by speaking, making it easy to use even when hands are occupied or there are visual impairments. The touch panel provides an intuitive method for visually inputting information, making it particularly easy to use for the elderly and tech-savvy users. This allows the reception desk to efficiently collect information on users' ideal body shape and dietary preferences, improving the overall accuracy and usability of the system.

[0032] The proposal department analyzes the information collected by the collection and reception departments and proposes the optimal meal plan. For example, the proposal department uses generative AI to generate a meal plan based on the user's ideal body shape and dietary preferences. The generative AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. Specifically, the generative AI receives collected body composition data and the user's ideal body shape and dietary preferences as input and analyzes this data. For example, if the user desires a low-calorie, high-protein diet, the generative AI will propose the optimal meal plan based on past data and nutritional knowledge. The generative AI can adjust the calories, nutritional balance, and ingredient selection of the meal according to the user's goals. Furthermore, the generative AI can receive user feedback and continuously improve the meal plan. For example, the user can evaluate their satisfaction with the proposed meal plan, and the next proposal will be improved based on that evaluation. In this way, the proposal department can always provide the user with the optimal meal plan and support them in achieving their health goals.

[0033] The service provider delivers the meal plans proposed by the proposal provider. The service provider can, for example, display meal plans to users. Specifically, it can display meal plans through a smartphone app. The app provides a visually intuitive interface so that users can easily check the meal plans. For example, the meal plans are displayed as detailed information including daily menus, ingredient lists, and cooking methods. The service provider can also provide printed meal plans. For example, if the user wishes, they can download and print the meal plan in PDF format. Furthermore, the service provider can send reminders and notifications regarding meal plans. For example, it can send a notification to the smartphone when mealtime is approaching, prompting the user to check the meal plan. This makes it easier for the service provider to actually follow the proposed meal plan. The service provider can also monitor the user's progress and adjust the meal plan in cooperation with the proposal provider as needed. This allows the service provider to support users in achieving their health goals and maximize the overall effectiveness of the system.

[0034] The suggestion unit includes a generation unit that generates specific meal plans. The generation unit uses a generation AI to create meal plans based on the user's ideal body shape and dietary preferences. For example, if the user inputs "I want to reduce my body fat percentage," the generation unit can generate a low-calorie, high-protein meal plan. Similarly, if the user inputs "I prefer a vegetable-centered diet," the generation unit can generate a meal plan that includes plenty of vegetables. The generation unit uses a generation AI to generate meal plans based on the user's ideal body shape and dietary preferences. The generation AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. This allows for more specific suggestions to be made to the user by generating concrete meal plans.

[0035] The generation unit uses a generation AI to generate meal plans based on the user's ideal body shape and dietary preferences. The generation AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. For example, if the user inputs "I want to reduce my body fat percentage," the generation unit can generate a low-calorie, high-protein meal plan. Also, if the user inputs "I prefer a vegetable-centered diet," the generation unit can generate a meal plan that includes plenty of vegetables. In this way, by using the generation AI, it is possible to generate meal plans that are based on the user's ideals.

[0036] The proposal unit includes a feedback unit that collects user feedback. The feedback unit collects user reactions to the proposed meal plans and provides feedback to the proposal unit. For example, users can input their satisfaction level and areas for improvement regarding the proposed meal plans. The feedback unit can collect user reactions through surveys or reviews, for example. The feedback unit can also collect user rating scores. For example, users can rate the proposed meal plans with stars. This allows for improvements to the proposals by collecting user feedback.

[0037] The feedback department collects user responses to the proposed meal plans and provides feedback to the proposal department. For example, users can input their satisfaction level and areas for improvement regarding the proposed meal plans. The feedback department can also collect user responses through surveys and reviews. Furthermore, the feedback department can collect user rating scores. For example, users can rate the proposed meal plans with stars. This allows for the collection of user feedback, which can then be used to improve the proposed plans.

[0038] The data collection unit optimizes the data collection method by referring to the user's past body composition data during collection. For example, the data collection unit can propose the optimal data collection method based on the user's past body composition data. It can also propose a method for collecting data at a specific time period based on the user's past data. Furthermore, the data collection unit can analyze the user's past data and propose the most efficient data collection method. This optimizes the data collection method by referring to past data, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past body composition data into a generating AI and have the generating AI propose the optimal data collection method.

[0039] The data collection unit supplements the data during collection, taking into account the user's lifestyle and exercise history. For example, the data collection unit can supplement the collected data based on the user's lifestyle. It can also supplement the collected data by considering the user's exercise history. Furthermore, the data collection unit can supplement the collected data by comprehensively considering the user's lifestyle and exercise history. This allows for more accurate data collection by considering lifestyle and exercise history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and exercise history into a generating AI and have the generating AI perform data supplementation.

[0040] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize collecting data related to that region. Furthermore, if the user is traveling, the data collection unit can also prioritize collecting data related to the environment of the travel destination. Additionally, if the user is at home, the data collection unit can prioritize collecting data related to the home environment. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit analyzes the user's social media activity and collects relevant data during the collection process. For example, the data collection unit can analyze posts related to health from the user's social media activity and collect relevant data. It can also analyze posts related to diet from the user's social media activity and collect relevant data. Furthermore, it can analyze posts related to exercise from the user's social media activity and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The reception desk optimizes the input method by referring to the user's past input history during registration. For example, the reception desk can automatically display as suggestions the ideal body shape and dietary preferences that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the ideal body shape and dietary preferences to be used at a specific time of day based on the user's past input history. This optimizes the input method by referring to past input history, enabling efficient input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0043] The reception unit completes the input content at the time of registration, taking into account the user's lifestyle and eating history. For example, the reception unit can complete the ideal body shape and dietary preferences based on the user's lifestyle. It can also complete the ideal body shape and dietary preferences by taking into account the user's eating history. Furthermore, the reception unit can complete the ideal body shape and dietary preferences by comprehensively considering the user's lifestyle and eating history. This allows for more accurate completion of input content by taking into account lifestyle and eating history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI. For example, the reception unit can input the user's lifestyle and eating history data into a generating AI and have the generating AI complete the input content.

[0044] The reception unit prioritizes receiving input content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit can prioritize receiving input content related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving input content related to the environment of their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving input content related to their home environment. This allows for the priority of receiving highly relevant input content by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI process the reception of highly relevant input content.

[0045] The reception unit analyzes the user's social media activity and receives relevant input content upon receiving the data. For example, the reception unit can analyze posts related to health from the user's social media activity and receive relevant input content. It can also analyze posts related to food from the user's social media activity and receive relevant input content. Furthermore, it can analyze posts related to exercise from the user's social media activity and receive relevant input content. This allows for the efficient reception of relevant input content by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant input content.

[0046] The suggestion unit optimizes its suggestions by referencing the user's past meal history. For example, the suggestion unit can provide optimal suggestions based on the user's past meal history. It can also provide suggestions that include specific ingredients based on the user's past meal history. Furthermore, the suggestion unit can analyze the user's past meal history and provide the most effective suggestions. This allows for the optimization of suggestions by referencing past meal history, enabling the provision of the best possible suggestions for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past meal history data into a generating AI and have the generating AI provide optimal suggestions.

[0047] The suggestion unit supplements the suggested content by considering the user's lifestyle and exercise history when making suggestions. For example, the suggestion unit can supplement the suggested content based on the user's lifestyle. It can also supplement the suggested content by considering the user's exercise history. Furthermore, the suggestion unit can supplement the suggested content by comprehensively considering the user's lifestyle and exercise history. This allows for the provision of more accurate suggestions by considering lifestyle and exercise history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle and exercise history data into a generating AI and have the generating AI perform the supplementation of the suggested content.

[0048] The suggestion unit prioritizes suggestion content that is highly relevant, taking into account the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit can prioritize suggestion content related to that region. Furthermore, if the user is traveling, the suggestion unit can provide suggestion content related to the environment of their travel destination. Additionally, if the user is at home, the suggestion unit can prioritize suggestion content related to their home environment. This allows for the prioritization of highly relevant suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI provide highly relevant suggestions.

[0049] The suggestion unit analyzes the user's social media activity and proposes relevant suggestions. For example, the suggestion unit can analyze posts related to health from the user's social media activity and provide relevant suggestions. It can also analyze posts related to diet from the user's social media activity and provide relevant suggestions. Furthermore, it can analyze posts related to exercise from the user's social media activity and provide relevant suggestions. This allows for the efficient provision of relevant suggestions by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI provide relevant suggestions.

[0050] The service provider optimizes the service content by referring to the user's past meal history at the time of service. For example, the service provider can select the most suitable service content based on the user's past meal history. It can also select a service content that includes specific ingredients based on the user's past meal history. Furthermore, the service provider can analyze the user's past meal history and select the most effective service content. In this way, by referring to past meal history, the service provider can optimize the service content and provide the best possible service for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past meal history data into a generating AI and have the generating AI select the optimal service content.

[0051] The service provider supplements the provided content by considering the user's lifestyle and exercise history at the time of delivery. For example, the service provider can supplement the provided content based on the user's lifestyle. It can also supplement the provided content by considering the user's exercise history. Furthermore, the service provider can supplement the provided content by comprehensively considering the user's lifestyle and exercise history. This allows for the provision of more accurate content by considering lifestyle and exercise history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's lifestyle and exercise history data into a generating AI and have the generating AI perform the supplementation of the provided content.

[0052] The service provider prioritizes providing highly relevant content, taking into account the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing content related to that region. Furthermore, if the user is traveling, the service provider can provide content related to the environment of their travel destination. Additionally, if the user is at home, the service provider can prioritize providing content related to their home environment. This allows for the prioritization of highly relevant content by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of highly relevant content.

[0053] The service provider analyzes the user's social media activity at the time of delivery and provides relevant content. For example, the service provider can analyze posts related to health from the user's social media activity and provide relevant content. It can also analyze posts related to food from the user's social media activity and provide relevant content. Furthermore, it can analyze posts related to exercise from the user's social media activity and provide relevant content. In this way, relevant content can be efficiently provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute the provision of relevant content.

[0054] The generation unit optimizes the generated content by referring to the user's past meal history during generation. For example, the generation unit can select the optimal generated content based on the user's past meal history. It can also select generated content containing specific ingredients from the user's past meal history. Furthermore, the generation unit can analyze the user's past meal history and select the most effective generated content. This optimizes the generated content by referring to past meal history, enabling the generation of the best possible meal for the user. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past meal history data into a generation AI and have the generation AI select the optimal generated content.

[0055] The generation unit supplements the generated content by considering the user's lifestyle and exercise history during the generation process. For example, the generation unit can supplement the generated content based on the user's lifestyle. It can also supplement the generated content by considering the user's exercise history. Furthermore, the generation unit can supplement the generated content by comprehensively considering the user's lifestyle and exercise history. This allows for the provision of more accurate generated content by considering lifestyle and exercise history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's lifestyle and exercise history data into a generation AI and have the generation AI perform the supplementation of the generated content.

[0056] The generation unit prioritizes generating highly relevant content by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit can prioritize providing content related to that region. Furthermore, if the user is traveling, the generation unit can provide content related to the environment of their travel destination. Additionally, if the user is at home, the generation unit can prioritize providing content related to their home environment. This allows for the prioritization of highly relevant content by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI provide highly relevant content.

[0057] The generation unit analyzes the user's social media activity during generation and generates relevant content. For example, the generation unit can analyze posts related to health from the user's social media activity and provide relevant content. It can also analyze posts related to food from the user's social media activity and provide relevant content. Furthermore, it can analyze posts related to exercise from the user's social media activity and provide relevant content. This allows for the efficient provision of relevant content by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the task of providing relevant content.

[0058] The feedback unit optimizes the feedback collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can propose the optimal collection method based on the user's past feedback history. It can also propose a method for collecting feedback at a specific time period based on the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and propose the most efficient collection method. This optimizes the collection method by referring to past feedback history, enabling efficient feedback collection. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history data into a generating AI and have the generating AI propose the optimal collection method.

[0059] The feedback unit prioritizes collecting highly relevant feedback by considering the user's geographical location information during feedback collection. For example, if the user is in a specific region, the feedback unit can prioritize collecting feedback related to that region. Furthermore, if the user is traveling, the feedback unit can collect feedback related to the environment of their travel destination. Additionally, if the user is at home, the feedback unit can prioritize collecting feedback related to their home environment. This allows for the priority collection of highly relevant feedback by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant feedback.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can refer to the user's past body composition data and optimize the data collection method. For example, it can suggest the optimal timing for data collection based on the user's past body composition data. It can also suggest a method for collecting data during specific time periods based on the user's past data. Furthermore, it can analyze the user's past data and suggest the most efficient data collection method. This allows for optimized data collection by referencing past data, enabling efficient data collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past body composition data into a generating AI and have the generating AI suggest the optimal data collection method.

[0062] The suggestion unit can supplement its suggestions by considering the user's lifestyle and exercise history. For example, it can supplement its suggestions based on the user's lifestyle. It can also supplement its suggestions by considering the user's exercise history. Furthermore, it can supplement its suggestions by comprehensively considering the user's lifestyle and exercise history. This allows for the provision of more accurate suggestions by considering lifestyle and exercise history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle and exercise history data into a generating AI and have the generating AI perform the supplementation of the suggestions.

[0063] The suggestion unit can optimize its suggestions by referring to the user's past meal history. For example, it can provide optimal suggestions based on the user's past meal history. Furthermore, the suggestion unit can provide suggestions that include specific ingredients based on the user's past meal history. In addition, the suggestion unit can analyze the user's past meal history to provide the most effective suggestions. This allows for the optimization of suggestions by referring to past meal history, enabling the provision of the most suitable suggestions for the user. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's past meal history data into a generating AI and have the generating AI provide optimal suggestions.

[0064] The data collection unit can supplement the data during collection by considering the user's lifestyle and exercise history. For example, it can supplement the collected data based on the user's lifestyle. It can also supplement the collected data by considering the user's exercise history. Furthermore, it can supplement the collected data by comprehensively considering the user's lifestyle and exercise history. This makes it possible to collect more accurate data by considering lifestyle and exercise history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and exercise history into a generating AI and have the generating AI perform data supplementation.

[0065] The service provider can optimize the meal offerings by referring to the user's past meal history at the time of service. For example, it can select the most suitable meal offerings based on the user's past meal history. Furthermore, the service provider can select meal offerings that include specific ingredients based on the user's past meal history. In addition, the service provider can analyze the user's past meal history and select the most effective meal offerings. This allows for the optimization of meal offerings by referring to past meal history, enabling the most optimal service for the user. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's past meal history data into a generating AI and have the generating AI select the optimal meal offerings.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data confirmed by the body composition analyzer. The data collection unit can collect data such as body fat percentage, muscle mass, and weight. The data collection unit automatically retrieves data from the body composition analyzer and stores it in a database. The data collection unit can also collect data manually entered by the user. For example, the user can enter data using a smartphone app. Step 2: The reception desk inputs information about the ideal body shape and dietary preferences. The reception desk can input information such as the user's ideal body fat percentage, muscle mass, and dietary preferences. The reception desk can accept information input from the user via a smartphone app. It can also accept information input via voice input or a touch panel. Step 3: The suggestion unit analyzes the information collected by the collection and reception units and proposes the optimal meal plan. The suggestion unit uses generative AI to generate a meal plan based on the user's ideal body shape and dietary preferences. For example, the suggestion unit can propose a low-calorie, high-protein meal plan or a meal plan that includes plenty of vegetables. The generative AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. Step 4: The service department provides the meal plan proposed by the proposal department. The service department can display the meal plan to the user. The service department can display the meal plan via a smartphone app. The service department can also print and provide the meal plan. For example, if the user wishes, they can download the meal plan in PDF format.

[0068] (Example of form 2) The system according to an embodiment of the present invention is a system that proposes an optimal diet based on the user's current body composition, ideal body shape, body composition, and dietary preferences, as confirmed through a body composition analyzer. In this system, the user checks their own body composition using a body composition analyzer and inputs their ideal body shape, body composition, and dietary preferences, and the generating AI proposes an optimal diet. For example, the user uses a body composition analyzer to obtain data such as body fat percentage, muscle mass, and weight. This data indicates the user's current physical condition. Next, the user inputs their ideal body shape, body composition, and dietary preferences. For example, they input information such as "I want to reduce my body fat percentage," "I want to increase my muscle mass," or "I prefer a vegetable-centered diet." This information is input to the generating AI. The generating AI analyzes the input information and proposes an optimal diet to the user. For example, if the user inputs "I want to reduce my body fat percentage," the generating AI will propose a low-calorie, high-protein diet. Also, if the user inputs "I prefer a vegetable-centered diet," the generating AI will propose a meal plan that includes many vegetables. Through this mechanism, the user can obtain an optimal diet based on their current physical condition and ideals. This enables people striving for body shaping or weight loss to effectively achieve their goals. For example, if a user checks their body fat percentage with a body composition analyzer and inputs "I want to reduce my body fat percentage by 10%" as their ideal body shape, the generating AI will suggest a meal plan to achieve that goal. Specifically, it will suggest a meal plan that is low in calories and high in protein, or one that includes plenty of vegetables. In this way, users can obtain a meal plan that matches their goals. Furthermore, the generating AI also takes into account the user's dietary preferences, so it can suggest a meal plan that includes ingredients and dishes the user likes. This allows users to obtain a meal plan that they can easily stick to. This system provides the optimal meal program for people striving for body shaping or weight loss, and supports them in effectively achieving their goals. As a result, the system can suggest and provide the optimal meal plan based on the user's body composition data, ideal body shape, and dietary preferences.

[0069] The system according to the embodiment comprises a data collection unit, a reception unit, a proposal unit, and a provision unit. The data collection unit collects data confirmed by a body composition analyzer. The data collection unit can collect data such as body fat percentage, muscle mass, and weight. The data collection unit can, for example, automatically acquire data from the body composition analyzer and store it in a database. The data collection unit can also collect data manually entered by the user. For example, the user can enter data using a smartphone app. The reception unit accepts input of ideal body shape and dietary preferences. The reception unit can accept information such as the user's ideal body fat percentage, muscle mass, and dietary preferences. For example, the user can enter information using a smartphone app. The reception unit can also accept information using voice input or a touch panel. The proposal unit analyzes the information collected by the data collection unit and the reception unit and proposes the optimal diet. The proposal unit can, for example, use a generation AI to generate a meal plan based on the user's ideal body shape and dietary preferences. The suggestion unit can, for example, suggest meal plans that are low in calories and high in protein, or meal plans that include plenty of vegetables. The suggestion unit uses generative AI to generate meal plans based on the user's ideal body shape and dietary preferences. The generative AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. The delivery unit provides the meal content suggested by the suggestion unit. The delivery unit can, for example, display the meal plan to the user. The delivery unit can, for example, display the meal plan through a smartphone app. The delivery unit can also print and provide the meal plan. For example, if the user wishes, the meal plan can be downloaded in PDF format. In this way, the system can suggest and provide the optimal meal content based on the user's body composition data and ideal body shape and dietary preferences.

[0070] The data collection unit collects data confirmed by the body composition scale. For example, the unit can collect data such as body fat percentage, muscle mass, and weight. Specifically, the body composition scale measures body fat percentage, muscle mass, and weight when the user steps on it, and transmits this data to the data collection unit via Bluetooth or Wi-Fi. The data collection unit automatically acquires this data and stores it in a database. The database is built on the cloud and has security measures in place, protecting user privacy. The data collection unit can also collect data manually entered by the user. For example, users can enter data using a smartphone app. The smartphone app is designed for easy data entry, and the entered data is immediately reflected in the database. Furthermore, the data collection unit can integrate with other health devices and applications. For example, it can collect data from smartwatches and fitness trackers and manage it as comprehensive health data. This allows the data collection unit to gain a multifaceted understanding of the user's health status and provide more accurate data.

[0071] The reception desk allows users to input their ideal body shape and dietary preferences. For example, the reception desk can input information such as the user's ideal body fat percentage, muscle mass, and dietary preferences. Specifically, users use a smartphone app to input their ideal body shape and dietary preferences. The app provides an intuitive interface to make it easy for users to input information. For example, sliders and checkboxes can be used to set ideal body fat percentage and muscle mass. Dietary preferences can also be selected from multiple options. Furthermore, the reception desk can also input information using voice input or a touch panel. Voice input allows users to input information simply by speaking, making it easy to use even when hands are occupied or there are visual impairments. The touch panel provides an intuitive method for visually inputting information, making it particularly easy to use for the elderly and tech-savvy users. This allows the reception desk to efficiently collect information on users' ideal body shape and dietary preferences, improving the overall accuracy and usability of the system.

[0072] The proposal department analyzes the information collected by the collection and reception departments and proposes the optimal meal plan. For example, the proposal department uses generative AI to generate a meal plan based on the user's ideal body shape and dietary preferences. The generative AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. Specifically, the generative AI receives collected body composition data and the user's ideal body shape and dietary preferences as input and analyzes this data. For example, if the user desires a low-calorie, high-protein diet, the generative AI will propose the optimal meal plan based on past data and nutritional knowledge. The generative AI can adjust the calories, nutritional balance, and ingredient selection of the meal according to the user's goals. Furthermore, the generative AI can receive user feedback and continuously improve the meal plan. For example, the user can evaluate their satisfaction with the proposed meal plan, and the next proposal will be improved based on that evaluation. In this way, the proposal department can always provide the user with the optimal meal plan and support them in achieving their health goals.

[0073] The service provider delivers the meal plans proposed by the proposal provider. The service provider can, for example, display meal plans to users. Specifically, it can display meal plans through a smartphone app. The app provides a visually intuitive interface so that users can easily check the meal plans. For example, the meal plans are displayed as detailed information including daily menus, ingredient lists, and cooking methods. The service provider can also provide printed meal plans. For example, if the user wishes, they can download and print the meal plan in PDF format. Furthermore, the service provider can send reminders and notifications regarding meal plans. For example, it can send a notification to the smartphone when mealtime is approaching, prompting the user to check the meal plan. This makes it easier for the service provider to actually follow the proposed meal plan. The service provider can also monitor the user's progress and adjust the meal plan in cooperation with the proposal provider as needed. This allows the service provider to support users in achieving their health goals and maximize the overall effectiveness of the system.

[0074] The suggestion unit includes a generation unit that generates specific meal plans. The generation unit uses a generation AI to create meal plans based on the user's ideal body shape and dietary preferences. For example, if the user inputs "I want to reduce my body fat percentage," the generation unit can generate a low-calorie, high-protein meal plan. Similarly, if the user inputs "I prefer a vegetable-centered diet," the generation unit can generate a meal plan that includes plenty of vegetables. The generation unit uses a generation AI to generate meal plans based on the user's ideal body shape and dietary preferences. The generation AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. This allows for more specific suggestions to be made to the user by generating concrete meal plans.

[0075] The generation unit uses a generation AI to generate meal plans based on the user's ideal body shape and dietary preferences. The generation AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. For example, if the user inputs "I want to reduce my body fat percentage," the generation unit can generate a low-calorie, high-protein meal plan. Also, if the user inputs "I prefer a vegetable-centered diet," the generation unit can generate a meal plan that includes plenty of vegetables. In this way, by using the generation AI, it is possible to generate meal plans that are based on the user's ideals.

[0076] The proposal unit includes a feedback unit that collects user feedback. The feedback unit collects user reactions to the proposed meal plans and provides feedback to the proposal unit. For example, users can input their satisfaction level and areas for improvement regarding the proposed meal plans. The feedback unit can collect user reactions through surveys or reviews, for example. The feedback unit can also collect user rating scores. For example, users can rate the proposed meal plans with stars. This allows for improvements to the proposals by collecting user feedback.

[0077] The feedback department collects user responses to the proposed meal plans and provides feedback to the proposal department. For example, users can input their satisfaction level and areas for improvement regarding the proposed meal plans. The feedback department can also collect user responses through surveys and reviews. Furthermore, the feedback department can collect user rating scores. For example, users can rate the proposed meal plans with stars. This allows for the collection of user feedback, which can then be used to improve the proposed plans.

[0078] The data collection unit estimates the user's emotions and adjusts the timing of body composition data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect body composition data when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can collect body composition data immediately. Furthermore, if the user is in a hurry, the data collection unit can collect body composition data in a short amount of time. By adjusting the collection timing according to the user's emotions, more accurate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit optimizes the data collection method by referring to the user's past body composition data during collection. For example, the data collection unit can propose the optimal data collection method based on the user's past body composition data. It can also propose a method for collecting data at a specific time period based on the user's past data. Furthermore, the data collection unit can analyze the user's past data and propose the most efficient data collection method. This optimizes the data collection method by referring to past data, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past body composition data into a generating AI and have the generating AI propose the optimal data collection method.

[0080] The data collection unit supplements the data during collection, taking into account the user's lifestyle and exercise history. For example, the data collection unit can supplement the collected data based on the user's lifestyle. It can also supplement the collected data by considering the user's exercise history. Furthermore, the data collection unit can supplement the collected data by comprehensively considering the user's lifestyle and exercise history. This allows for more accurate data collection by considering lifestyle and exercise history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and exercise history into a generating AI and have the generating AI perform data supplementation.

[0081] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting stress-related data. If the user is relaxed, the data collection unit can also collect overall body composition data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This ensures that important data is collected preferentially by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize collecting data related to that region. Furthermore, if the user is traveling, the data collection unit can also prioritize collecting data related to the environment of the travel destination. Additionally, if the user is at home, the data collection unit can prioritize collecting data related to the home environment. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0083] The data collection unit analyzes the user's social media activity and collects relevant data during the collection process. For example, the data collection unit can analyze posts related to health from the user's social media activity and collect relevant data. It can also analyze posts related to diet from the user's social media activity and collect relevant data. Furthermore, it can analyze posts related to exercise from the user's social media activity and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0084] The reception unit estimates the user's emotions and adjusts the display of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This reduces user stress and facilitates the input process by adjusting the interface display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 AI, or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reception desk optimizes the input method by referring to the user's past input history during registration. For example, the reception desk can automatically display as suggestions the ideal body shape and dietary preferences that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the ideal body shape and dietary preferences to be used at a specific time of day based on the user's past input history. This optimizes the input method by referring to past input history, enabling efficient input. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0086] The reception unit completes the input content at the time of registration, taking into account the user's lifestyle and eating history. For example, the reception unit can complete the ideal body shape and dietary preferences based on the user's lifestyle. It can also complete the ideal body shape and dietary preferences by taking into account the user's eating history. Furthermore, the reception unit can complete the ideal body shape and dietary preferences by comprehensively considering the user's lifestyle and eating history. This allows for more accurate completion of input content by taking into account lifestyle and eating history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI. For example, the reception unit can input the user's lifestyle and eating history data into a generating AI and have the generating AI complete the input content.

[0087] The reception unit estimates the user's emotions and prioritizes input based on the estimated emotions. For example, if the user is stressed, the reception unit can prioritize receiving important input. If the user is relaxed, the reception unit can also prioritize receiving general input. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving simple input. This allows important information to be prioritized by prioritizing input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 AI or not. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The reception unit prioritizes receiving input content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit can prioritize receiving input content related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving input content related to the environment of their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving input content related to their home environment. This allows for the priority of receiving highly relevant input content by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI process the reception of highly relevant input content.

[0089] The reception unit analyzes the user's social media activity and receives relevant input content upon receiving the data. For example, the reception unit can analyze posts related to health from the user's social media activity and receive relevant input content. It can also analyze posts related to food from the user's social media activity and receive relevant input content. Furthermore, it can analyze posts related to exercise from the user's social media activity and receive relevant input content. This allows for the efficient reception of relevant input content by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant input content.

[0090] The suggestion unit estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The suggestion unit optimizes its suggestions by referencing the user's past meal history. For example, the suggestion unit can provide optimal suggestions based on the user's past meal history. It can also provide suggestions that include specific ingredients based on the user's past meal history. Furthermore, the suggestion unit can analyze the user's past meal history and provide the most effective suggestions. This allows for the optimization of suggestions by referencing past meal history, enabling the provision of the best possible suggestions for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past meal history data into a generating AI and have the generating AI provide optimal suggestions.

[0092] The suggestion unit supplements the suggested content by considering the user's lifestyle and exercise history when making suggestions. For example, the suggestion unit can supplement the suggested content based on the user's lifestyle. It can also supplement the suggested content by considering the user's exercise history. Furthermore, the suggestion unit can supplement the suggested content by comprehensively considering the user's lifestyle and exercise history. This allows for the provision of more accurate suggestions by considering lifestyle and exercise history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle and exercise history data into a generating AI and have the generating AI perform the supplementation of the suggested content.

[0093] The suggestion unit estimates the user's emotions and prioritizes suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can prioritize important suggestions. If the user is relaxed, the suggestion unit can provide more general suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize simple suggestions. This allows for prioritizing important suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The suggestion unit prioritizes suggestion content that is highly relevant, taking into account the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit can prioritize suggestion content related to that region. Furthermore, if the user is traveling, the suggestion unit can provide suggestion content related to the environment of their travel destination. Additionally, if the user is at home, the suggestion unit can prioritize suggestion content related to their home environment. This allows for the prioritization of highly relevant suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI provide highly relevant suggestions.

[0095] The suggestion unit analyzes the user's social media activity and proposes relevant suggestions. For example, the suggestion unit can analyze posts related to health from the user's social media activity and provide relevant suggestions. It can also analyze posts related to diet from the user's social media activity and provide relevant suggestions. Furthermore, it can analyze posts related to exercise from the user's social media activity and provide relevant suggestions. This allows for the efficient provision of relevant suggestions by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI provide relevant suggestions.

[0096] The delivery unit estimates the user's emotions and adjusts the delivery method based on the estimated emotions. For example, if the user is relaxed, the delivery unit can select a delivery method that includes detailed explanations. If the user is in a hurry, the delivery unit can select a delivery method that includes concise explanations. Furthermore, if the user is excited, the delivery unit can select a visually stimulating delivery method. By adjusting the delivery method according to the user's emotions, the optimal delivery for the user can be achieved. 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 delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The service provider optimizes the service content by referring to the user's past meal history at the time of service. For example, the service provider can select the most suitable service content based on the user's past meal history. It can also select a service content that includes specific ingredients based on the user's past meal history. Furthermore, the service provider can analyze the user's past meal history and select the most effective service content. In this way, by referring to past meal history, the service provider can optimize the service content and provide the best possible service for the user. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past meal history data into a generating AI and have the generating AI select the optimal service content.

[0098] The service provider supplements the provided content by considering the user's lifestyle and exercise history at the time of delivery. For example, the service provider can supplement the provided content based on the user's lifestyle. It can also supplement the provided content by considering the user's exercise history. Furthermore, the service provider can supplement the provided content by comprehensively considering the user's lifestyle and exercise history. This allows for the provision of more accurate content by considering lifestyle and exercise history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's lifestyle and exercise history data into a generating AI and have the generating AI perform the supplementation of the provided content.

[0099] The service provider estimates the user's emotions and prioritizes the content offered based on those emotions. For example, if the user is stressed, the service provider can prioritize offering important content. If the user is relaxed, the service provider can also prioritize offering a general overview. Furthermore, if the user is in a hurry, the service provider can prioritize offering simpler content. This ensures that important content is prioritized by prioritizing the content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The service provider prioritizes providing highly relevant content, taking into account the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing content related to that region. Furthermore, if the user is traveling, the service provider can provide content related to the environment of their travel destination. Additionally, if the user is at home, the service provider can prioritize providing content related to their home environment. This allows for the prioritization of highly relevant content by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI execute the provision of highly relevant content.

[0101] The service provider analyzes the user's social media activity at the time of delivery and provides relevant content. For example, the service provider can analyze posts related to health from the user's social media activity and provide relevant content. It can also analyze posts related to food from the user's social media activity and provide relevant content. Furthermore, it can analyze posts related to exercise from the user's social media activity and provide relevant content. In this way, relevant content can be efficiently provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute the provision of relevant content.

[0102] The generation unit estimates the user's emotions and adjusts the generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can select a detailed generation method. If the user is in a hurry, the generation unit can select a concise generation method. Furthermore, if the user is excited, the generation unit can select a visually stimulating generation method. By adjusting the generation method according to the user's emotions, it is possible to generate the optimal output for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0103] The generation unit optimizes the generated content by referring to the user's past meal history during generation. For example, the generation unit can select the optimal generated content based on the user's past meal history. It can also select generated content containing specific ingredients from the user's past meal history. Furthermore, the generation unit can analyze the user's past meal history and select the most effective generated content. This optimizes the generated content by referring to past meal history, enabling the generation of the best possible meal for the user. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past meal history data into a generation AI and have the generation AI select the optimal generated content.

[0104] The generation unit supplements the generated content by considering the user's lifestyle and exercise history during the generation process. For example, the generation unit can supplement the generated content based on the user's lifestyle. It can also supplement the generated content by considering the user's exercise history. Furthermore, the generation unit can supplement the generated content by comprehensively considering the user's lifestyle and exercise history. This allows for the provision of more accurate generated content by considering lifestyle and exercise history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's lifestyle and exercise history data into a generation AI and have the generation AI perform the supplementation of the generated content.

[0105] The generation unit estimates the user's emotions and determines the priority of generated content based on the estimated emotions. For example, if the user is stressed, the generation unit can prioritize providing important generated content. If the user is relaxed, the generation unit can also provide general generated content. Furthermore, if the user is in a hurry, the generation unit can prioritize providing simple generated content. In this way, by prioritizing generated content according to the user's emotions, important content can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0106] The generation unit prioritizes generating highly relevant content by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit can prioritize providing content related to that region. Furthermore, if the user is traveling, the generation unit can provide content related to the environment of their travel destination. Additionally, if the user is at home, the generation unit can prioritize providing content related to their home environment. This allows for the prioritization of highly relevant content by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI provide highly relevant content.

[0107] The generation unit analyzes the user's social media activity during generation and generates relevant content. For example, the generation unit can analyze posts related to health from the user's social media activity and provide relevant content. It can also analyze posts related to food from the user's social media activity and provide relevant content. Furthermore, it can analyze posts related to exercise from the user's social media activity and provide relevant content. This allows for the efficient provision of relevant content by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the task of providing relevant content.

[0108] The feedback unit estimates the user's emotions and adjusts the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback unit can collect detailed feedback. If the user is in a hurry, the feedback unit can collect concise feedback. Furthermore, if the user is excited, the feedback unit can collect visually stimulating feedback. By adjusting the feedback collection method according to the user's emotions, it becomes possible to collect the most optimal feedback for the user. 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 feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0109] The feedback unit optimizes the feedback collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback unit can propose the optimal collection method based on the user's past feedback history. It can also propose a method for collecting feedback at a specific time period based on the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and propose the most efficient collection method. This optimizes the collection method by referring to past feedback history, enabling efficient feedback collection. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history data into a generating AI and have the generating AI propose the optimal collection method.

[0110] The feedback unit estimates the user's emotions and prioritizes the feedback content based on the estimated emotions. For example, if the user is stressed, the feedback unit can prioritize collecting important feedback content. It can also collect general feedback content if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can prioritize collecting simple feedback content. This allows for the prioritization of important content by determining the priority of feedback content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The feedback unit prioritizes collecting highly relevant feedback by considering the user's geographical location information during feedback collection. For example, if the user is in a specific region, the feedback unit can prioritize collecting feedback related to that region. Furthermore, if the user is traveling, the feedback unit can collect feedback related to the environment of their travel destination. Additionally, if the user is at home, the feedback unit can prioritize collecting feedback related to their home environment. This allows for the priority collection of highly relevant feedback by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant feedback.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. For example, if the user is stressed, the suggestion can be concise and a visually relaxing design can be used. If the user is relaxed, detailed suggestions can be provided and a visually rich design can be used. Furthermore, if the user is excited, the suggestion can be presented with a visually stimulating design. By adjusting the way the suggestion is presented according to the user's emotions, it becomes possible to provide the most suitable suggestion for the user. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, 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 AI or not using AI. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0114] The data collection unit can refer to the user's past body composition data and optimize the data collection method. For example, it can suggest the optimal timing for data collection based on the user's past body composition data. It can also suggest a method for collecting data during specific time periods based on the user's past data. Furthermore, it can analyze the user's past data and suggest the most efficient data collection method. This allows for optimized data collection by referencing past data, enabling efficient data collection. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past body composition data into a generating AI and have the generating AI suggest the optimal data collection method.

[0115] The reception unit can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is tense, a calm color scheme interface can be provided to reduce visual stress. If the user is enjoying themselves, a bright color scheme interface can be provided to make the input process more enjoyable. Furthermore, if the user is tired, a simple and highly visible interface can be provided to facilitate the input process. In this way, by adjusting the display of the interface according to the user's emotions, user stress is reduced and the input process is made easier. Emotion estimation is achieved, 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 AI, or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0116] The suggestion unit can supplement its suggestions by considering the user's lifestyle and exercise history. For example, it can supplement its suggestions based on the user's lifestyle. It can also supplement its suggestions by considering the user's exercise history. Furthermore, it can supplement its suggestions by comprehensively considering the user's lifestyle and exercise history. This allows for the provision of more accurate suggestions by considering lifestyle and exercise history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle and exercise history data into a generating AI and have the generating AI perform the supplementation of the suggestions.

[0117] The data collection unit can estimate the user's emotions and adjust the timing of body composition data collection based on the estimated emotions. For example, if the user is stressed, the data collection timing can be delayed to collect body composition data when the user is relaxed. Conversely, if the user is relaxed, body composition data can be collected immediately. Furthermore, if the user is in a hurry, body composition data can be collected in a short time. By adjusting the collection timing according to the user's emotions, more accurate data collection becomes possible. Emotion estimation is achieved using, 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 processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0118] The suggestion unit can optimize its suggestions by referring to the user's past meal history. For example, it can provide optimal suggestions based on the user's past meal history. Furthermore, the suggestion unit can provide suggestions that include specific ingredients based on the user's past meal history. In addition, the suggestion unit can analyze the user's past meal history to provide the most effective suggestions. This allows for the optimization of suggestions by referring to past meal history, enabling the provision of the most suitable suggestions for the user. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's past meal history data into a generating AI and have the generating AI provide optimal suggestions.

[0119] The reception unit can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, important input can be prioritized. If the user is relaxed, general input can be prioritized. Furthermore, if the user is in a hurry, simple input can be prioritized. This allows important information to be prioritized by prioritizing input according to the user's emotions. Emotion estimation is achieved using, 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 processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0120] The data collection unit can supplement the data during collection by considering the user's lifestyle and exercise history. For example, it can supplement the collected data based on the user's lifestyle. It can also supplement the collected data by considering the user's exercise history. Furthermore, it can supplement the collected data by comprehensively considering the user's lifestyle and exercise history. This makes it possible to collect more accurate data by considering lifestyle and exercise history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and exercise history into a generating AI and have the generating AI perform data supplementation.

[0121] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, important suggestions can be prioritized. If the user is relaxed, general suggestions can be provided. Furthermore, if the user is in a hurry, simple suggestions can be prioritized. This allows for prioritizing important suggestions according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0122] The service provider can optimize the meal offerings by referring to the user's past meal history at the time of service. For example, it can select the most suitable meal offerings based on the user's past meal history. Furthermore, the service provider can select meal offerings that include specific ingredients based on the user's past meal history. In addition, the service provider can analyze the user's past meal history and select the most effective meal offerings. This allows for the optimization of meal offerings by referring to past meal history, enabling the most optimal service for the user. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's past meal history data into a generating AI and have the generating AI select the optimal meal offerings.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects data confirmed by the body composition analyzer. The data collection unit can collect data such as body fat percentage, muscle mass, and weight. The data collection unit automatically retrieves data from the body composition analyzer and stores it in a database. The data collection unit can also collect data manually entered by the user. For example, the user can enter data using a smartphone app. Step 2: The reception desk inputs information about the ideal body shape and dietary preferences. The reception desk can input information such as the user's ideal body fat percentage, muscle mass, and dietary preferences. The reception desk can accept information input from the user via a smartphone app. It can also accept information input via voice input or a touch panel. Step 3: The suggestion unit analyzes the information collected by the collection and reception units and proposes the optimal meal plan. The suggestion unit uses generative AI to generate a meal plan based on the user's ideal body shape and dietary preferences. For example, the suggestion unit can propose a low-calorie, high-protein meal plan or a meal plan that includes plenty of vegetables. The generative AI uses technologies such as neural networks and reinforcement learning to generate the optimal meal plan for the user. Step 4: The service department provides the meal plan proposed by the proposal department. The service department can display the meal plan to the user. The service department can display the meal plan via a smartphone app. The service department can also print and provide the meal plan. For example, if the user wishes, they can download the meal plan in PDF format.

[0125] 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.

[0126] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, reception unit, proposal unit, provision unit, and feedback unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires data from the body composition analyzer using the camera 42 and communication I / F 44 of the smart device 14 and stores it in the database 24 by the specific processing unit 290 of the data processing unit 12. The reception unit inputs the user's ideal body shape and dietary preferences using the touch panel 38A and microphone 38B of the smart device 14. The proposal unit generates a meal plan using generated AI by the specific processing unit 290 of the data processing unit 12. The provision unit provides the meal plan to the user through the display 40A and speaker 40B of the smart device 14. The feedback unit collects user feedback using the touch panel 38A and microphone 38B of the smart device 14 and analyzes it by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[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 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.

[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 (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).

[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] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, reception unit, proposal unit, provision unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit acquires data from the body composition analyzer using the camera 42 and communication I / F 44 of the smart glasses 214 and stores it in the database 24 by the identification processing unit 290 of the data processing unit 12. The reception unit inputs the user's ideal body shape and dietary preferences using the microphone 238 of the smart glasses 214. The proposal unit generates a meal plan using generated AI by the identification processing unit 290 of the data processing unit 12. The provision unit provides the meal plan to the user through the speaker 240 of the smart glasses 214. The feedback unit collects user feedback using the microphone 238 of the smart glasses 214 and analyzes it by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the collection unit, reception unit, proposal unit, provision unit, and feedback unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires data from the body composition analyzer using the camera 42 and communication I / F 44 of the headset terminal 314 and stores it in the database 24 by the specific processing unit 290 of the data processing unit 12. The reception unit inputs the user's ideal body shape and dietary preferences using the microphone 238 of the headset terminal 314. The proposal unit generates a meal plan using generated AI by the specific processing unit 290 of the data processing unit 12. The provision unit provides the meal plan to the user through the display 343 and speaker 240 of the headset terminal 314. The feedback unit collects user feedback using the microphone 238 of the headset terminal 314 and analyzes it by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, reception unit, proposal unit, provision unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires data from the body composition analyzer using the camera 42 and communication I / F 44 of the robot 414 and stores it in the database 24 by the specific processing unit 290 of the data processing unit 12. The reception unit inputs the user's ideal body shape and dietary preferences using the microphone 238 of the robot 414. The proposal unit generates a meal plan using generated AI by the specific processing unit 290 of the data processing unit 12. The provision unit provides the meal plan to the user through the speaker 240 and display device of the robot 414. The feedback unit collects user feedback using the microphone 238 of the robot 414 and analyzes it by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A data collection unit that collects data confirmed by a body composition analyzer, The reception desk where you input your ideal body shape and dietary preferences, A proposal unit analyzes the information collected by the collection unit and the reception unit and proposes the optimal meal plan, The system comprises a serving unit that provides the meal contents proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It includes a generation unit that generates specific meal plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates meal plans based on the user's ideal body shape and dietary preferences. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned proposal section is, It includes a feedback unit for collecting user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Collect user feedback on proposed meal plans and provide it to the proposal team. The system described in Appendix 4, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of body composition data collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the collection method is optimized by referring to the user's past body composition data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the data is supplemented by considering the user's lifestyle and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is At the time of registration, the system optimizes the input method by referring to the user's past input history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is During registration, the system will complete the input information by considering the user's lifestyle and dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is During registration, the system prioritizes accepting input that is highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is At the time of registration, the system analyzes the user's social media activity and collects relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, we optimize the suggestions by referring to the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, supplement the proposal by considering the user's lifestyle and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making proposals, the system prioritizes suggesting highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and propose relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When serving, the menu is optimized by referring to the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the content will be supplemented by taking into account the user's lifestyle and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing content, we prioritize offerings that are highly relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, we analyze the user's social media activity and provide relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is It estimates the user's emotions and adjusts the generation method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The generating unit is During generation, the system optimizes the generated content by referencing the user's past meal history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The generating unit is During generation, the generated content is supplemented by considering the user's lifestyle and exercise history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The generating unit is It estimates the user's emotions and determines the priority of generated content based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The generating unit is During generation, the system prioritizes generating highly relevant content by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant content. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned feedback unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned feedback unit is When collecting feedback, we optimize the collection method by referring to the user's past feedback history. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned feedback unit is It estimates the user's emotions and prioritizes the content of the feedback based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned feedback unit is When collecting feedback, the system prioritizes collecting highly relevant feedback by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 data collection unit that collects data confirmed by a body composition analyzer, The reception desk where you input your ideal body shape and dietary preferences, A proposal unit analyzes the information collected by the collection unit and the reception unit and proposes the optimal meal plan, The system comprises a serving unit that provides the meal contents proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, It includes a generation unit that generates specific meal plans. The system according to feature 1.

3. The generating unit is The AI ​​generates meal plans based on the user's ideal body shape and dietary preferences. The system according to feature 2.

4. The aforementioned proposal section is, It includes a feedback unit for collecting user feedback. The system according to feature 1.

5. The aforementioned feedback unit is Collect user feedback on the proposed meal plan and provide it to the proposal unit. The system according to feature 4.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of body composition data collection based on those emotions. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the collection method is optimized by referring to the user's past body composition data. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the data is supplemented by considering the user's lifestyle and exercise history. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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