System

Abstract

An object of a system according to an embodiment is to propose an optimal meal plan based on a health condition and a request of an employee.SOLUTION: A system includes a health data acquisition unit, a request data acquisition unit, an analysis unit, and a proposal unit. The health data acquisition unit acquires health data of employees. The request data acquisition unit acquires employee request data. The analyzer analyzes the data acquired by the health data acquirer and the demand data acquirer. The proposing section proposes a meal plan based on a result of the analysis by the analyzing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately propose meal plans based on employees' health status and requests, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal meal plans based on employees' health conditions and requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data acquisition unit, a desired data acquisition unit, an analysis unit, and a proposal unit. The health data acquisition unit acquires health data of employees. The desired data acquisition unit acquires desired data of employees. The analysis unit analyzes the data acquired by the health data acquisition unit and the desired data acquisition unit. The proposal unit proposes a meal plan based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal meal plans based on employees' health conditions and requests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The health support system according to an embodiment of the present invention uses AI to determine the health status and needs of employees, and then uses an app to suggest food combinations that employees should eat in the company cafeteria. This allows the health support system to support employee health and promote health management in the company.

[0029] A health support system according to an embodiment includes a health data acquisition unit, a request data acquisition unit, an analysis unit, and a proposal unit. The health data acquisition unit acquires health data of employees. For example, the health data acquisition unit collects data such as the employee's blood pressure, heart rate, and weight. The health data acquisition unit can also acquire the employee's health checkup results. The health data acquisition unit can also collect lifestyle data of employees. For example, the health data acquisition unit collects data such as the employee's exercise level, dietary habits, and sleep duration. The request data acquisition unit acquires request data of employees. For example, the request data acquisition unit collects data such as the employee's food preferences, allergy information, and health goals. The request data acquisition unit can also collect feedback on the employee's diet. The request data acquisition unit can also collect questionnaire data on the employee's diet. For example, the request data acquisition unit collects data on the employee's favorite dishes and new menu items. The analysis unit analyzes the data acquired by the health data acquisition unit and the request data acquisition unit. For example, the analysis unit uses data mining technology to analyze the employee's health condition and needs. The analysis unit can also use statistical analysis technology to evaluate the employee's health risks. Furthermore, the analysis unit can predict the employee's health condition and needs using a machine learning algorithm. For example, the analysis unit predicts future health risks based on the employee's health data and proposes preventive measures. The proposal unit proposes a meal plan based on the results of the analysis by the analysis unit. For example, the proposal unit calculates calories and proposes a meal plan suitable for the employee. The proposal unit can also propose a meal plan suitable for the employee taking nutritional balance into consideration. Furthermore, the proposal unit can select ingredients and propose a meal plan suitable for the employee. For example, the proposal unit proposes a balanced meal plan based on the employee's health condition and needs. As a result, the health support system according to the embodiment can propose an optimal meal plan based on the employee's health condition and needs.

[0030] The analysis unit can predict individual health risks based on health data and suggest preventive measures. For example, the analysis unit collects employee health data and uses AI to analyze that data to predict individual health risks. For example, based on blood pressure and blood sugar data, it evaluates the future risk of high blood pressure and diabetes and suggests preventive measures. The analysis unit also uses AI to analyze health checkup results and provide specific dietary and exercise advice to employees with specific health risks. For example, it suggests a low-fat diet for employees with high cholesterol levels. The analysis unit also uses AI to predict health risks based on employees' lifestyle data and suggest individual preventive measures. For example, it suggests a smoking cessation program or limiting alcohol intake for employees who smoke or drink alcohol. This makes it possible to predict individual health risks and suggest appropriate preventive measures.

[0031] The analysis unit can analyze stress levels and suggest meals and breaks to reduce stress. For example, to measure employees' stress levels, AI analyzes heart rate and sleep data and suggests meals with a relaxing effect to employees with high stress levels. For example, it could recommend herbal tea or meals containing omega-3 fatty acids. To analyze stress levels, AI also collects employee questionnaire data and suggests break times and relaxation methods to employees with high stress levels. For example, it could recommend short periods of meditation or deep breathing. The analysis unit also monitors employees' stress levels in real time, and when stress rises, AI suggests meals and breaks with a relaxing effect. For example, it could offer a relaxing smoothie when stress reaches its peak. This allows the system to analyze employees' stress levels and suggest appropriate meals and breaks.

[0032] The analysis unit can propose exercise plans based on health data and balance diet and exercise. For example, the analysis unit uses AI to propose individual exercise plans based on employees' health data. For example, it can propose appropriate exercise amounts and types based on weight and body fat percentage. The analysis unit also analyzes health data and proposes plans to balance diet and exercise. For example, it can provide meal menus based on calorie consumption. The analysis unit also analyzes employees' exercise history and makes suggestions to optimize the balance between diet and exercise. For example, it can suggest post-exercise recovery menus. This makes it possible to propose exercise plans based on employees' health data and balance diet and exercise.

[0033] The analysis unit can analyze the sleep data and make dietary suggestions to improve sleep quality. For example, the analysis unit analyzes the sleep data of employees and makes dietary suggestions to improve sleep quality. For example, it can suggest meals using ingredients containing melatonin. The analysis unit also uses AI to suggest a meal plan to improve sleep quality based on the sleep data. For example, it can advise employees to limit their caffeine intake. The analysis unit also analyzes the sleep patterns of employees and makes dietary suggestions to improve sleep quality. For example, it can provide meals using ingredients containing tryptophan. This makes it possible to make dietary suggestions to improve sleep quality based on the sleep data of employees.

[0034] The proposal unit can propose optimal meal plans according to the season, taking into account seasonal variations in nutrients. For example, the proposal unit analyzes seasonal variations in nutrients and proposes optimal meal plans according to the season. For example, in winter, it provides menus using ingredients that are rich in vitamin D. The proposal unit also considers the nutritional value of seasonal ingredients and proposes optimal meal plans. For example, in summer, it provides menus that emphasize hydration. The proposal unit also proposes meal plans that match the health status of employees based on seasonal variations in nutrients. For example, in spring, it provides menus using ingredients that have a detoxifying effect. In this way, it is possible to propose optimal meal plans that take into account seasonal variations in nutrients.

[0035] The suggestion unit can analyze the meal history and make meal suggestions aimed at maintaining long-term health. For example, the suggestion unit analyzes the employee's past meal history and makes meal suggestions aimed at maintaining long-term health. For example, it suggests menus that supplement nutrients that were lacking in the past. The suggestion unit also uses AI to propose meal plans aimed at maintaining long-term health based on the past meal history. For example, it provides balanced meals. The suggestion unit also analyzes the employee's meal history and makes meal suggestions that take into account nutrients necessary for maintaining health. For example, it suggests menus that adjust nutrients that were consumed in large amounts in the past. This makes it possible to make meal suggestions aimed at maintaining long-term health based on the employee's past meal history.

[0036] The proposal department can incorporate local specialties into the menu and work to revitalize the local economy. For example, the proposal department can incorporate local specialties into the menu of a company cafeteria and work to revitalize the local economy. For example, the proposal department can provide a menu using local agricultural products. The proposal department can also propose a menu using local specialties and work to revitalize the local economy. For example, the proposal department can provide dishes using fish caught in local fishing industries. The proposal department can also incorporate local specialties into the menu of a company cafeteria and strengthen ties with the local community. For example, the proposal department can hold a local specialties fair and provide a menu using local ingredients. In this way, by incorporating local specialties into the menu of a company cafeteria, it is possible to revitalize the local economy.

[0037] The suggestion unit can make home meal suggestions, taking into account the health conditions of family members. For example, the suggestion unit makes home meal suggestions, taking into account the health conditions of employees' family members. For example, it can suggest a balanced meal plan that will help the whole family stay healthy. The suggestion unit also uses AI to make home meal suggestions based on family health data. For example, it can provide menus that take into account allergy information for family members. The suggestion unit also analyzes the health conditions of employees' family members and makes home meal suggestions. For example, it can suggest healthy menus that the whole family can enjoy. This makes it possible to make home meal suggestions, taking into account the health conditions of employees' family members.

[0038] The analysis department can assess the health risks of the entire company based on health data and propose risk management measures. For example, the analysis department collects employee health data and uses AI to assess the health risks of the entire company. For example, it identifies departments or groups with high specific health risks and proposes risk management measures. The analysis department also uses AI to assess the health risks of the entire company based on the results of health checkups and proposes preventive measures. For example, it proposes regular exercise programs and health seminars. The analysis department also analyzes employee lifestyle data to assess the health risks of the entire company. For example, it proposes smoking cessation programs and alcohol restrictions for departments with high smoking and drinking rates. This makes it possible to assess the health risks of the entire company and propose appropriate risk management measures.

[0039] The analysis department can quantitatively evaluate the effects of health and productivity management and reflect them in management strategies. For example, after implementing health and productivity management measures, the analysis department collects employee health data and quantitatively evaluates their effectiveness. For example, it measures the reduction in the number of sick leave days and medical expenses. The analysis department also quantitatively evaluates the effects of health and productivity management and reflects the results in management strategies. For example, it plans new health measures based on successful health and productivity management cases. The analysis department also uses AI to analyze data to quantitatively evaluate the effects of health and productivity management and reflect the results in management strategies. For example, it measures improvements in employee productivity and reductions in turnover rates. This allows the effects of health and productivity management to be quantitatively evaluated and reflected in management strategies.

[0040] The analysis department can introduce a mental healthcare program as part of health management. For example, as part of health management, the analysis department uses AI to suggest a mental healthcare program for employees. For example, the analysis department could hold seminars to teach stress management and relaxation techniques. The analysis department could also monitor employees' mental health status and introduce a mental healthcare program as needed. For example, the analysis department could provide counseling sessions or mental health apps. The analysis department could also introduce a mental healthcare program as part of health management to support employees' mental health. For example, the analysis department could conduct regular mental health checks and stress relief activities. This allows the analysis department to introduce a mental healthcare program for employees as part of health management.

[0041] The analysis department can disseminate the results of health and productivity management to the public and improve the company's brand value. The analysis department, for example, disseminates the results of health and productivity management to the public and improve the company's brand value. For example, it disseminates successful cases of health and productivity management through press releases and social media. The analysis department also plans marketing strategies to improve the company's brand value based on the results of health and productivity management. For example, it holds events and seminars related to health and productivity management. The analysis department also disseminates the results of health and productivity management to the public and improve the company's brand value. For example, it obtains health and productivity management awards and certifications, thereby increasing the company's credibility. In this way, the results of health and productivity management can be disseminated to the public and improve the company's brand value.

[0042] The proposal department can collect feedback in real time and make instantaneous menu improvements. For example, the proposal department can build a system that collects employee feedback in real time and makes instantaneous menu improvements. For example, it can allow employees to post their opinions about menus through an app. The proposal department can then use AI to suggest menu improvements based on the feedback. For example, it can propose new menu items that take into account employee preferences and allergy information. The proposal department can also analyze employee feedback in real time and make instantaneous menu improvements. For example, it can increase the number of popular menu items based on employee opinions. This makes it possible to collect employee feedback in real time and make instant menu improvements.

[0043] The proposal department can periodically survey employee satisfaction with meals and propose measures to improve satisfaction. For example, the proposal department can periodically survey employee satisfaction with meals and propose measures to improve satisfaction based on the results. For example, it can conduct a questionnaire survey to collect employee opinions. The proposal department can also analyze the results of the meal satisfaction survey and have AI propose measures to improve satisfaction. For example, it can propose increasing the number of popular menu items. The proposal department can also periodically survey employee satisfaction with meals and improve the menu based on the results. For example, it can introduce new menu items in response to employee requests. In this way, it is possible to periodically survey employee satisfaction with meals and propose measures to improve satisfaction.

[0044] The suggestion unit makes meal suggestions that also target the family, thereby improving the satisfaction of the entire household. For example, the suggestion unit takes into account the health status of the employee's family and makes meal suggestions to improve the satisfaction of the entire household. For example, it proposes a balanced meal plan that the whole family can enjoy. The suggestion unit also uses AI to make meal suggestions for the home based on the family's health data. For example, it provides menus that take into account the family's allergy information. The suggestion unit also analyzes the health status of the employee's family and makes meal suggestions to improve the satisfaction of the entire household. For example, it proposes healthy menus that the whole family can enjoy. In this way, it is possible to make meal suggestions that also target the employee's family and improve the satisfaction of the entire household.

[0045] The suggestion unit can suggest special menus that suit hobbies and interests. For example, the suggestion unit suggests special menus by taking into consideration the hobbies and interests of employees. For example, for an employee who likes a particular dish, a menu incorporating that dish is provided. The suggestion unit also uses AI to suggest special menus based on hobbies and interests. For example, a vegetarian menu is provided for a vegetarian employee. The suggestion unit also analyzes employees' hobbies and interests to suggest special menus. For example, for an employee who likes cuisine from a particular country, a menu incorporating cuisine from that country is provided. In this way, it is possible to suggest special menus that suit employees' hobbies and interests.

[0046] The analysis unit can analyze the meal history and optimize the amount of ingredients used. The analysis unit, for example, analyzes the meal history of employees and optimizes the amount of ingredients used. For example, based on past data, it predicts the amount of ingredients needed and reduces waste. The analysis unit also uses AI to optimize the amount of ingredients used based on the meal history. For example, it procures more ingredients for popular menu items and reduces waste. The analysis unit also analyzes the meal history of employees and optimizes the amount of ingredients used. For example, it predicts the amount of ingredients needed and reduces waste based on past data. In this way, it is possible to analyze the meal history of employees and optimize the amount of ingredients used.

[0047] The analysis unit can monitor the storage conditions of ingredients and make suggestions to prevent deterioration. The analysis unit, for example, monitors the storage conditions of ingredients and makes suggestions to prevent deterioration. For example, it manages temperature and humidity to keep ingredients fresh. The analysis unit also uses AI to make suggestions to prevent deterioration based on the storage conditions. For example, it improves storage methods to keep ingredients fresh. The analysis unit also monitors the storage conditions of ingredients and makes suggestions to prevent deterioration. For example, it manages temperature and humidity to keep ingredients fresh. This makes it possible to monitor the storage conditions of ingredients and make suggestions to prevent deterioration.

[0048] The analysis unit can donate surplus ingredients to a local food bank in order to reduce food waste. The analysis unit, for example, builds a system for donating surplus ingredients to a local food bank. For example, surplus ingredients from a company cafeteria are periodically provided to the food bank. The analysis unit also donates surplus ingredients to a local food bank in order to reduce food waste. For example, ingredients that are close to their expiration date are provided to the food bank. The analysis unit also reduces food waste by donating surplus ingredients to a local food bank. For example, surplus ingredients from a company cafeteria are periodically provided to the food bank. In this way, surplus ingredients can be donated to a local food bank in order to reduce food waste.

[0049] The analysis unit can implement an educational program to raise awareness of food waste reduction. The analysis unit, for example, implements an educational program for employees to raise awareness of food waste reduction. For example, it holds seminars on the current state of food waste and reduction methods. The analysis unit also implements an educational program for employees to raise awareness of food waste reduction. For example, it introduces specific actions for reducing food waste. The analysis unit also implements an educational program for employees to raise awareness of food waste reduction. For example, it introduces specific actions for reducing food waste. In this way, it is possible to implement an educational program to raise awareness of food waste reduction for employees.

[0050] The analysis unit allows the AI ​​to automatically schedule the staff required to operate the company cafeteria and optimally allocate staff. For example, the analysis unit allows the AI ​​to automatically schedule the staff required to operate the company cafeteria and optimally allocate staff. For example, staff are allocated according to peak times. The analysis unit also allows the AI ​​to analyze employee schedules and optimally allocate staff required to operate the company cafeteria. For example, staff are allocated according to employee break times. The analysis unit also allows the AI ​​to automatically schedule the staff required to operate the company cafeteria and optimally allocate staff. For example, staff are allocated according to peak times. This allows the AI ​​to automatically schedule the staff required to operate the company cafeteria and optimally allocate staff.

[0051] The analysis unit can predict peak meal times and propose efficient staffing allocation. For example, the analysis unit uses AI to predict peak meal times for employees and propose efficient staffing allocation. For example, staffing is allocated to coincide with peak lunch time. The analysis unit can also use AI to predict peak meal times for employees and propose efficient staffing allocation. For example, staffing is allocated to coincide with peak lunch time. The analysis unit can also use AI to predict peak meal times for employees and propose efficient staffing allocation. For example, staffing is allocated to coincide with peak lunch time. This makes it possible to predict peak meal times for employees and propose efficient staffing allocation.

[0052] The analysis unit can introduce an automatic cooking system that utilizes AI in the operation of a company cafeteria. The analysis unit, for example, introduces an automatic cooking system that utilizes AI in the operation of a company cafeteria. For example, AI automates the cooking process and provides meals efficiently. The analysis unit also introduces an automatic cooking system to make the operation of a company cafeteria more efficient. For example, AI automates the cooking process and provides meals efficiently. The analysis unit also introduces an automatic cooking system that utilizes AI in the operation of a company cafeteria. For example, AI automates the cooking process and provides meals efficiently. This makes it possible to introduce an automatic cooking system that utilizes AI in the operation of a company cafeteria.

[0053] The analysis unit can introduce a self-service system in which employees choose their own meals, thereby reducing the burden on staff. The analysis unit, for example, introduces a self-service system in which employees choose their own meals, thereby reducing the burden on staff. For example, an employee chooses their own meal and places it on a tray. The analysis unit can also introduce a self-service system in which employees choose their own meals, thereby reducing the burden on staff. For example, an employee chooses their own meal and places it on a tray. The analysis unit can also introduce a self-service system in which employees choose their own meals, thereby reducing the burden on staff. For example, an employee chooses their own meal and places it on a tray. This allows the analysis unit to introduce a self-service system in which employees choose their own meals, thereby reducing the burden on staff.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The health support system can also collect employees' exercise data and make meal suggestions based on the amount of exercise. For example, it can suggest recovery menus after exercise to promote muscle recovery. It can also suggest meals with the appropriate calorie content if energy needs to be replenished before exercise. Furthermore, for employees who are not used to exercising, it can make meal suggestions to encourage light exercise. This makes it possible to provide more personalized health support based on employees' exercise data.

[0056] The analysis unit not only predicts individual health risks based on employees' health data, but also takes into account the health data of employees' families to evaluate health risks for the entire household and suggest preventative measures. For example, it can suggest a balanced meal plan that will help the whole family stay healthy. It can also provide specific advice on diet and exercise for family members with specific health risks based on the results of their health checkups. This makes it possible to support the health of not only employees, but also their families.

[0057] The analysis unit can not only analyze stress levels, but also suggest relaxation methods based on employees' hobbies and interests. For example, for employees who like music, it can recommend music with a relaxing effect. For employees who like reading, it can also provide a relaxing reading space. Furthermore, for employees who like outdoor activities, it can suggest relaxation methods in nature. In this way, it is possible to provide relaxation methods that suit each employee's individual hobbies and interests.

[0058] The analysis unit not only proposes exercise plans based on health data, but can also analyze employees' sleep data and propose exercise plans to improve sleep quality. For example, for employees with poor sleep quality, it can suggest relaxing stretching or yoga. It can also suggest light exercise to promote recovery after exercise. It can also propose plans that take into account the balance of exercise and diet to improve sleep quality. This makes it possible to provide more personalized health support based on employees' sleep data.

[0059] In addition to analyzing sleep data, the analysis unit can collect data on employees' work environment and make suggestions for improving it. For example, it can monitor the lighting, temperature, and noise levels in the workplace and suggest the optimal environment. It can also improve the workplace layout and provide spaces where employees can relax. Furthermore, based on the data on the work environment, it can suggest specific improvements to reduce employees' stress levels. This can improve employees' work environment and improve their overall health.

[0060] The proposal department can not only take into account seasonal nutritional fluctuations, but also propose meal plans that suit employees' lifestyles and working hours. For example, for employees who work many night shifts, it can provide meal menus suitable for the evening. For employees who travel frequently, it can also suggest healthy menus that are easy to choose when eating out. Furthermore, for employees who frequently work from home, it can propose balanced meal plans that can be easily prepared at home. This makes it possible to provide optimal meal plans that suit employees' lifestyles and working hours.

[0061] The Proposal Department not only analyzes meal history and makes meal suggestions aimed at maintaining long-term health, but also periodically surveys employees' meal satisfaction and can improve menus based on the results. For example, they can conduct questionnaire surveys to collect employees' opinions. They can also analyze the results of meal satisfaction surveys and make suggestions to increase the number of popular menu items. They can also introduce new menu items in response to employee requests. This makes it possible to propose measures to improve employee meal satisfaction.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The health data acquisition unit acquires employee health data. For example, the health data acquisition unit collects data such as the employee's blood pressure, heart rate, and weight. The health data acquisition unit can also collect the employee's health checkup results and lifestyle data (such as the amount of exercise, diet, and sleep time). Step 2: The request data acquisition unit acquires employee request data. For example, the request data acquisition unit collects data such as employees' food preferences, allergy information, and health goals. The request data acquisition unit can also collect employee feedback and survey data about their meals (data about favorite dishes and new menu items). Step 3: The analysis unit analyzes the data acquired by the health data acquisition unit and the need data acquisition unit. For example, the analysis unit uses data mining technology and statistical analysis technology to analyze the employee's health status and needs and evaluate health risks. Furthermore, the analysis unit uses machine learning algorithms to predict the employee's health status and needs and predict future health risks. Step 4: The proposal unit proposes a meal plan based on the results of the analysis by the analysis unit. For example, the proposal unit proposes a meal plan suitable for the employee, taking into account calorie calculations and nutritional balance. The proposal unit also selects ingredients and proposes a balanced meal plan based on the employee's health condition and requests.

[0064] (Example 2) The health support system according to an embodiment of the present invention uses AI to determine the health status and needs of employees, and then uses an app to suggest food combinations that employees should eat in the company cafeteria. This allows the health support system to support employee health and promote health management in the company.

[0065] A health support system according to an embodiment includes a health data acquisition unit, a request data acquisition unit, an analysis unit, and a proposal unit. The health data acquisition unit acquires health data of employees. For example, the health data acquisition unit collects data such as the employee's blood pressure, heart rate, and weight. The health data acquisition unit can also acquire the employee's health checkup results. The health data acquisition unit can also collect lifestyle data of employees. For example, the health data acquisition unit collects data such as the employee's exercise level, dietary habits, and sleep duration. The request data acquisition unit acquires request data of employees. For example, the request data acquisition unit collects data such as the employee's food preferences, allergy information, and health goals. The request data acquisition unit can also collect feedback on the employee's diet. The request data acquisition unit can also collect questionnaire data on the employee's diet. For example, the request data acquisition unit collects data on the employee's favorite dishes and new menu items. The analysis unit analyzes the data acquired by the health data acquisition unit and the request data acquisition unit. For example, the analysis unit uses data mining technology to analyze the employee's health condition and needs. The analysis unit can also use statistical analysis technology to evaluate the employee's health risks. Furthermore, the analysis unit can predict the employee's health condition and needs using a machine learning algorithm. For example, the analysis unit predicts future health risks based on the employee's health data and proposes preventive measures. The proposal unit proposes a meal plan based on the results of the analysis by the analysis unit. For example, the proposal unit calculates calories and proposes a meal plan suitable for the employee. The proposal unit can also propose a meal plan suitable for the employee taking nutritional balance into consideration. Furthermore, the proposal unit can select ingredients and propose a meal plan suitable for the employee. For example, the proposal unit proposes a balanced meal plan based on the employee's health condition and needs. As a result, the health support system according to the embodiment can propose an optimal meal plan based on the employee's health condition and needs.

[0066] The analysis unit can predict individual health risks based on health data and suggest preventive measures. For example, the analysis unit collects employee health data and uses AI to analyze that data to predict individual health risks. For example, based on blood pressure and blood sugar data, it evaluates the future risk of high blood pressure and diabetes and suggests preventive measures. The analysis unit also uses AI to analyze health checkup results and provide specific dietary and exercise advice to employees with specific health risks. For example, it suggests a low-fat diet for employees with high cholesterol levels. The analysis unit also uses AI to predict health risks based on employees' lifestyle data and suggest individual preventive measures. For example, it suggests a smoking cessation program or limiting alcohol intake for employees who smoke or drink alcohol. This makes it possible to predict individual health risks and suggest appropriate preventive measures.

[0067] The analysis unit can analyze stress levels and suggest meals and breaks to reduce stress. For example, to measure employees' stress levels, AI analyzes heart rate and sleep data and suggests meals with a relaxing effect to employees with high stress levels. For example, it could recommend herbal tea or meals containing omega-3 fatty acids. To analyze stress levels, AI also collects employee questionnaire data and suggests break times and relaxation methods to employees with high stress levels. For example, it could recommend short periods of meditation or deep breathing. The analysis unit also monitors employees' stress levels in real time, and when stress rises, AI suggests meals and breaks with a relaxing effect. For example, it could offer a relaxing smoothie when stress reaches its peak. This allows the system to analyze employees' stress levels and suggest appropriate meals and breaks.

[0068] The analysis unit can analyze the emotional state using the emotion estimation function and make meal suggestions according to the emotion. For example, the analysis unit can use the emotion estimation function to analyze the employee's facial expressions and voice to understand the emotional state. For example, if the employee is tired, the analysis unit can suggest a meal suitable for replenishing energy. The analysis unit can also analyze the employee's emotional state and suggest a meal that will bring out positive emotions. For example, if the employee is feeling stressed, the analysis unit can provide a meal that uses ingredients that have a relaxing effect. The analysis unit can also use the emotion estimation function to monitor the employee's emotional state in real time and make meal suggestions according to the emotion. For example, if the employee is feeling depressed, the analysis unit can suggest a meal that has a mood-boosting effect. This makes it possible to make meal suggestions according to the employee's emotional state.

[0069] The analysis unit can propose exercise plans based on health data and balance diet and exercise. For example, the analysis unit uses AI to propose individual exercise plans based on employees' health data. For example, it can propose appropriate exercise amounts and types based on weight and body fat percentage. The analysis unit also analyzes health data and proposes plans to balance diet and exercise. For example, it can provide meal menus based on calorie consumption. The analysis unit also analyzes employees' exercise history and makes suggestions to optimize the balance between diet and exercise. For example, it can suggest post-exercise recovery menus. This makes it possible to propose exercise plans based on employees' health data and balance diet and exercise.

[0070] The analysis unit can analyze the sleep data and make dietary suggestions to improve sleep quality. For example, the analysis unit analyzes the sleep data of employees and makes dietary suggestions to improve sleep quality. For example, it can suggest meals using ingredients containing melatonin. The analysis unit also uses AI to suggest a meal plan to improve sleep quality based on the sleep data. For example, it can advise employees to limit their caffeine intake. The analysis unit also analyzes the sleep patterns of employees and makes dietary suggestions to improve sleep quality. For example, it can provide meals using ingredients containing tryptophan. This makes it possible to make dietary suggestions to improve sleep quality based on the sleep data of employees.

[0071] The analysis unit can use the emotion estimation function to suggest a relaxation method according to the emotional state. For example, the analysis unit uses the emotion estimation function to analyze the emotional state of an employee and suggest a relaxation method. For example, it may suggest meditation or yoga for an employee who is highly stressed. The analysis unit also monitors the emotional state of an employee in real time and suggests a relaxation method. For example, it may recommend music that has a relaxing effect. The analysis unit also uses the emotion estimation function to suggest a relaxation method according to the emotional state of the employee. For example, it may suggest aromatherapy for an employee who is emotionally unstable. In this way, it is possible to suggest a relaxation method according to the emotional state of the employee.

[0072] The proposal unit can propose optimal meal plans according to the season, taking into account seasonal variations in nutrients. For example, the proposal unit analyzes seasonal variations in nutrients and proposes optimal meal plans according to the season. For example, in winter, it provides menus using ingredients that are rich in vitamin D. The proposal unit also considers the nutritional value of seasonal ingredients and proposes optimal meal plans. For example, in summer, it provides menus that emphasize hydration. The proposal unit also proposes meal plans that match the health status of employees based on seasonal variations in nutrients. For example, in spring, it provides menus using ingredients that have a detoxifying effect. In this way, it is possible to propose optimal meal plans that take into account seasonal variations in nutrients.

[0073] The suggestion unit can analyze the meal history and make meal suggestions aimed at maintaining long-term health. For example, the suggestion unit analyzes the employee's past meal history and makes meal suggestions aimed at maintaining long-term health. For example, it suggests menus that supplement nutrients that were lacking in the past. The suggestion unit also uses AI to propose meal plans aimed at maintaining long-term health based on the past meal history. For example, it provides balanced meals. The suggestion unit also analyzes the employee's meal history and makes meal suggestions that take into account nutrients necessary for maintaining health. For example, it suggests menus that adjust nutrients that were consumed in large amounts in the past. This makes it possible to make meal suggestions aimed at maintaining long-term health based on the employee's past meal history.

[0074] The suggestion unit can use the emotion estimation function to suggest food combinations that match the mood. For example, the suggestion unit uses the emotion estimation function to analyze the mood of an employee and suggest food combinations that match that mood. For example, a meal that has the effect of lifting a mood is provided to an employee who is feeling down. The suggestion unit also monitors the emotional state of an employee in real time and suggests food combinations that match the mood. For example, a meal that has a relaxing effect is provided to an employee who is under a lot of stress. The suggestion unit also uses the emotion estimation function to suggest food combinations that match the mood of an employee. For example, a meal that is suitable for replenishing energy is provided to an employee who is in a good mood. In this way, food combinations that match the mood of an employee can be suggested.

[0075] The proposal department can incorporate local specialties into the menu and work to revitalize the local economy. For example, the proposal department can incorporate local specialties into the menu of a company cafeteria and work to revitalize the local economy. For example, the proposal department can provide a menu using local agricultural products. The proposal department can also propose a menu using local specialties and work to revitalize the local economy. For example, the proposal department can provide dishes using fish caught in local fishing industries. The proposal department can also incorporate local specialties into the menu of a company cafeteria and strengthen ties with the local community. For example, the proposal department can hold a local specialties fair and provide a menu using local ingredients. In this way, by incorporating local specialties into the menu of a company cafeteria, it is possible to revitalize the local economy.

[0076] The suggestion unit can make home meal suggestions, taking into account the health conditions of family members. For example, the suggestion unit makes home meal suggestions, taking into account the health conditions of employees' family members. For example, it can suggest a balanced meal plan that will help the whole family stay healthy. The suggestion unit also uses AI to make home meal suggestions based on family health data. For example, it can provide menus that take into account allergy information for family members. The suggestion unit also analyzes the health conditions of employees' family members and makes home meal suggestions. For example, it can suggest healthy menus that the whole family can enjoy. This makes it possible to make home meal suggestions, taking into account the health conditions of employees' family members.

[0077] The suggestion unit can use the emotion estimation function to suggest desserts and drinks according to the emotion. For example, the suggestion unit uses the emotion estimation function to analyze the emotional state of an employee and suggest desserts and drinks according to the emotion. For example, a chocolate dessert is provided for an employee who is feeling down. The suggestion unit also monitors the emotional state of an employee in real time and suggests desserts and drinks according to the emotion. For example, a herbal tea with a relaxing effect is provided for an employee who is experiencing high stress. The suggestion unit also uses the emotion estimation function to suggest desserts and drinks according to the emotion of the employee. For example, a smoothie suitable for replenishing energy is provided for an employee in a good mood. In this way, desserts and drinks can be suggested according to the emotion of the employee.

[0078] The analysis department can assess the health risks of the entire company based on health data and propose risk management measures. For example, the analysis department collects employee health data and uses AI to assess the health risks of the entire company. For example, it identifies departments or groups with high specific health risks and proposes risk management measures. The analysis department also uses AI to assess the health risks of the entire company based on the results of health checkups and proposes preventive measures. For example, it proposes regular exercise programs and health seminars. The analysis department also analyzes employee lifestyle data to assess the health risks of the entire company. For example, it proposes smoking cessation programs and alcohol restrictions for departments with high smoking and drinking rates. This makes it possible to assess the health risks of the entire company and propose appropriate risk management measures.

[0079] The analysis department can quantitatively evaluate the effects of health and productivity management and reflect them in management strategies. For example, after implementing health and productivity management measures, the analysis department collects employee health data and quantitatively evaluates their effectiveness. For example, it measures the reduction in the number of sick leave days and medical expenses. The analysis department also quantitatively evaluates the effects of health and productivity management and reflects the results in management strategies. For example, it plans new health measures based on successful health and productivity management cases. The analysis department also uses AI to analyze data to quantitatively evaluate the effects of health and productivity management and reflect the results in management strategies. For example, it measures improvements in employee productivity and reductions in turnover rates. This allows the effects of health and productivity management to be quantitatively evaluated and reflected in management strategies.

[0080] The analysis unit can monitor the emotional state using the emotion estimation function and propose health measures based on the emotions. The analysis unit, for example, uses the emotion estimation function to monitor the emotional state of employees in real time and propose health measures based on the emotions. For example, it proposes a relaxation program for employees with high stress. The analysis unit also analyzes the emotional state of employees and proposes health measures based on the emotions. For example, it proposes a mental health care program for employees with unstable emotions. The analysis unit also uses the emotion estimation function to monitor the emotional state of employees and propose health measures based on the emotions. For example, it proposes team building activities to elicit positive emotions. In this way, it is possible to monitor the emotional state of employees and propose health measures based on the emotions.

[0081] The analysis department can introduce a mental healthcare program as part of health management. For example, as part of health management, the analysis department uses AI to suggest a mental healthcare program for employees. For example, the analysis department could hold seminars to teach stress management and relaxation techniques. The analysis department could also monitor employees' mental health status and introduce a mental healthcare program as needed. For example, the analysis department could provide counseling sessions or mental health apps. The analysis department could also introduce a mental healthcare program as part of health management to support employees' mental health. For example, the analysis department could conduct regular mental health checks and stress relief activities. This allows the analysis department to introduce a mental healthcare program for employees as part of health management.

[0082] The analysis department can disseminate the results of health and productivity management to the public and improve the company's brand value. The analysis department, for example, disseminates the results of health and productivity management to the public and improve the company's brand value. For example, it disseminates successful cases of health and productivity management through press releases and social media. The analysis department also plans marketing strategies to improve the company's brand value based on the results of health and productivity management. For example, it holds events and seminars related to health and productivity management. The analysis department also disseminates the results of health and productivity management to the public and improve the company's brand value. For example, it obtains health and productivity management awards and certifications, thereby increasing the company's credibility. In this way, the results of health and productivity management can be disseminated to the public and improve the company's brand value.

[0083] The analysis unit can use the emotion estimation function to propose an emotion-based employee benefit program. For example, the analysis unit uses the emotion estimation function to analyze the emotional state of employees and propose an emotion-based employee benefit program. For example, the analysis unit recommends the use of relaxation facilities for employees with high stress levels. The analysis unit also monitors the emotional state of employees in real time and proposes an emotion-based employee benefit program. For example, it provides a mental health care program for emotionally unstable employees. The analysis unit also uses the emotion estimation function to analyze the emotional state of employees and proposes an emotion-based employee benefit program. For example, it suggests recreational activities to elicit positive emotions. In this way, it is possible to propose an employee benefit program based on the employee's emotions.

[0084] The proposal department can collect feedback in real time and make instantaneous menu improvements. For example, the proposal department can build a system that collects employee feedback in real time and makes instantaneous menu improvements. For example, it can allow employees to post their opinions about menus through an app. The proposal department can then use AI to suggest menu improvements based on the feedback. For example, it can propose new menu items that take into account employee preferences and allergy information. The proposal department can also analyze employee feedback in real time and make instantaneous menu improvements. For example, it can increase the number of popular menu items based on employee opinions. This makes it possible to collect employee feedback in real time and make instant menu improvements.

[0085] The proposal department can periodically survey employee satisfaction with meals and propose measures to improve satisfaction. For example, the proposal department can periodically survey employee satisfaction with meals and propose measures to improve satisfaction based on the results. For example, it can conduct a questionnaire survey to collect employee opinions. The proposal department can also analyze the results of the meal satisfaction survey and have AI propose measures to improve satisfaction. For example, it can propose increasing the number of popular menu items. The proposal department can also periodically survey employee satisfaction with meals and improve the menu based on the results. For example, it can introduce new menu items in response to employee requests. In this way, it is possible to periodically survey employee satisfaction with meals and propose measures to improve satisfaction.

[0086] The suggestion unit can improve the menu based on emotions using the emotion estimation function. The suggestion unit, for example, uses the emotion estimation function to analyze the emotional state of employees and improve the menu based on emotions. For example, a meal that has a relaxing effect is provided to an employee who is highly stressed. The suggestion unit also monitors the emotional state of employees in real time and improves the menu based on emotions. For example, a meal that has a mood-boosting effect is provided to an employee who is feeling depressed. The suggestion unit also uses the emotion estimation function to analyze the emotional state of employees and improve the menu based on emotions. For example, a meal that elicits positive emotions is provided. In this way, the menu can be improved based on the emotions of employees.

[0087] The suggestion unit makes meal suggestions that also target the family, thereby improving the satisfaction of the entire household. For example, the suggestion unit takes into account the health status of the employee's family and makes meal suggestions to improve the satisfaction of the entire household. For example, it proposes a balanced meal plan that the whole family can enjoy. The suggestion unit also uses AI to make meal suggestions for the home based on the family's health data. For example, it provides menus that take into account the family's allergy information. The suggestion unit also analyzes the health status of the employee's family and makes meal suggestions to improve the satisfaction of the entire household. For example, it proposes healthy menus that the whole family can enjoy. In this way, it is possible to make meal suggestions that also target the employee's family and improve the satisfaction of the entire household.

[0088] The suggestion unit can suggest special menus that suit hobbies and interests. For example, the suggestion unit suggests special menus by taking into consideration the hobbies and interests of employees. For example, for an employee who likes a particular dish, a menu incorporating that dish is provided. The suggestion unit also uses AI to suggest special menus based on hobbies and interests. For example, a vegetarian menu is provided for a vegetarian employee. The suggestion unit also analyzes employees' hobbies and interests to suggest special menus. For example, for an employee who likes cuisine from a particular country, a menu incorporating cuisine from that country is provided. In this way, it is possible to suggest special menus that suit employees' hobbies and interests.

[0089] The suggestion unit can use the emotion estimation function to plan events and campaigns based on emotions. For example, the suggestion unit uses the emotion estimation function to analyze the emotional state of employees and plan events and campaigns based on their emotions. For example, a relaxation event is held for employees with high stress levels. The suggestion unit also monitors the emotional state of employees in real time and plans events and campaigns based on their emotions. For example, an event to lift the mood of employees who are feeling depressed is held. The suggestion unit also uses the emotion estimation function to analyze the emotional state of employees and plan events and campaigns based on their emotions. For example, recreational activities to elicit positive emotions are suggested. In this way, events and campaigns based on employees' emotions can be planned.

[0090] The analysis unit can analyze the meal history and optimize the amount of ingredients used. The analysis unit, for example, analyzes the meal history of employees and optimizes the amount of ingredients used. For example, based on past data, it predicts the amount of ingredients needed and reduces waste. The analysis unit also uses AI to optimize the amount of ingredients used based on the meal history. For example, it procures more ingredients for popular menu items and reduces waste. The analysis unit also analyzes the meal history of employees and optimizes the amount of ingredients used. For example, it predicts the amount of ingredients needed and reduces waste based on past data. In this way, it is possible to analyze the meal history of employees and optimize the amount of ingredients used.

[0091] The analysis unit can monitor the storage conditions of ingredients and make suggestions to prevent deterioration. The analysis unit, for example, monitors the storage conditions of ingredients and makes suggestions to prevent deterioration. For example, it manages temperature and humidity to keep ingredients fresh. The analysis unit also uses AI to make suggestions to prevent deterioration based on the storage conditions. For example, it improves storage methods to keep ingredients fresh. The analysis unit also monitors the storage conditions of ingredients and makes suggestions to prevent deterioration. For example, it manages temperature and humidity to keep ingredients fresh. This makes it possible to monitor the storage conditions of ingredients and make suggestions to prevent deterioration.

[0092] The analysis unit can use the emotion estimation function to procure ingredients based on dietary preferences. The analysis unit, for example, uses the emotion estimation function to analyze employees' dietary preferences and optimize ingredient procurement. For example, it procures more ingredients for popular menu items. The analysis unit also monitors employees' emotional states in real time and procures ingredients based on their dietary preferences. For example, it procures ingredients suitable for replenishing energy for employees who are in a good mood. The analysis unit also uses the emotion estimation function to analyze employees' dietary preferences and optimize ingredient procurement. For example, it procures more ingredients for popular menu items. This makes it possible to procure ingredients based on employees' dietary preferences.

[0093] The analysis unit can donate surplus ingredients to a local food bank in order to reduce food waste. The analysis unit, for example, builds a system for donating surplus ingredients to a local food bank. For example, surplus ingredients from a company cafeteria are periodically provided to the food bank. The analysis unit also donates surplus ingredients to a local food bank in order to reduce food waste. For example, ingredients that are close to their expiration date are provided to the food bank. The analysis unit also reduces food waste by donating surplus ingredients to a local food bank. For example, surplus ingredients from a company cafeteria are periodically provided to the food bank. In this way, surplus ingredients can be donated to a local food bank in order to reduce food waste.

[0094] The analysis unit can implement an educational program to raise awareness of food waste reduction. The analysis unit, for example, implements an educational program for employees to raise awareness of food waste reduction. For example, it holds seminars on the current state of food waste and reduction methods. The analysis unit also implements an educational program for employees to raise awareness of food waste reduction. For example, it introduces specific actions for reducing food waste. The analysis unit also implements an educational program for employees to raise awareness of food waste reduction. For example, it introduces specific actions for reducing food waste. In this way, it is possible to implement an educational program to raise awareness of food waste reduction for employees.

[0095] The analysis unit can use the emotion estimation function to implement a food waste reduction campaign based on emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotional state of employees and implement a food waste reduction campaign based on emotions. For example, a campaign is implemented to elicit positive emotions. The analysis unit also monitors the emotional state of employees in real time and implements a food waste reduction campaign based on emotions. For example, specific actions to reduce food waste are suggested to employees who are in a good mood. The analysis unit also uses the emotion estimation function to analyze the emotional state of employees and implement a food waste reduction campaign based on emotions. For example, a campaign is implemented to elicit positive emotions. This makes it possible to implement a food waste reduction campaign based on employees' emotions.

[0096] The analysis unit allows the AI ​​to automatically schedule the staff required to operate the company cafeteria and optimally allocate staff. For example, the analysis unit allows the AI ​​to automatically schedule the staff required to operate the company cafeteria and optimally allocate staff. For example, staff are allocated according to peak times. The analysis unit also allows the AI ​​to analyze employee schedules and optimally allocate staff required to operate the company cafeteria. For example, staff are allocated according to employee break times. The analysis unit also allows the AI ​​to automatically schedule the staff required to operate the company cafeteria and optimally allocate staff. For example, staff are allocated according to peak times. This allows the AI ​​to automatically schedule the staff required to operate the company cafeteria and optimally allocate staff.

[0097] The analysis unit can predict peak meal times and propose efficient staffing allocation. For example, the analysis unit uses AI to predict peak meal times for employees and propose efficient staffing allocation. For example, staffing is allocated to coincide with peak lunch time. The analysis unit can also use AI to predict peak meal times for employees and propose efficient staffing allocation. For example, staffing is allocated to coincide with peak lunch time. The analysis unit can also use AI to predict peak meal times for employees and propose efficient staffing allocation. For example, staffing is allocated to coincide with peak lunch time. This makes it possible to predict peak meal times for employees and propose efficient staffing allocation.

[0098] The analysis unit can use the emotion estimation function to perform personnel allocation based on emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotional state of employees and perform personnel allocation based on emotions. For example, an environment that has a relaxing effect is provided for an employee who is highly stressed. The analysis unit also monitors the emotional state of employees in real time and performs personnel allocation based on emotions. For example, an environment that has a mood-boosting effect is provided for an employee who is feeling depressed. The analysis unit also uses the emotion estimation function to analyze the emotional state of employees and perform personnel allocation based on emotions. For example, an environment that elicits positive emotions is provided. This makes it possible to perform personnel allocation based on employees' emotions.

[0099] The analysis unit can introduce an automatic cooking system that utilizes AI in the operation of a company cafeteria. The analysis unit, for example, introduces an automatic cooking system that utilizes AI in the operation of a company cafeteria. For example, AI automates the cooking process and provides meals efficiently. The analysis unit also introduces an automatic cooking system to make the operation of a company cafeteria more efficient. For example, AI automates the cooking process and provides meals efficiently. The analysis unit also introduces an automatic cooking system that utilizes AI in the operation of a company cafeteria. For example, AI automates the cooking process and provides meals efficiently. This makes it possible to introduce an automatic cooking system that utilizes AI in the operation of a company cafeteria.

[0100] The analysis unit can introduce a self-service system in which employees choose their own meals, thereby reducing the burden on staff. The analysis unit, for example, introduces a self-service system in which employees choose their own meals, thereby reducing the burden on staff. For example, an employee chooses their own meal and places it on a tray. The analysis unit can also introduce a self-service system in which employees choose their own meals, thereby reducing the burden on staff. For example, an employee chooses their own meal and places it on a tray. The analysis unit can also introduce a self-service system in which employees choose their own meals, thereby reducing the burden on staff. For example, an employee chooses their own meal and places it on a tray. This allows the analysis unit to introduce a self-service system in which employees choose their own meals, thereby reducing the burden on staff.

[0101] The analysis unit can use the emotion estimation function to consider introducing emotion-based self-service. The analysis unit, for example, uses the emotion estimation function to analyze the emotional state of employees and consider introducing emotion-based self-service. For example, an environment that has a relaxing effect is provided for employees who are highly stressed. The analysis unit also monitors the emotional state of employees in real time and considers introducing emotion-based self-service. For example, an environment that has a mood-boosting effect is provided for employees who are feeling depressed. The analysis unit also uses the emotion estimation function to analyze the emotional state of employees and considers introducing emotion-based self-service. For example, an environment that elicits positive emotions is provided. This makes it possible to consider introducing emotion-based self-service.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The health support system can also collect employees' exercise data and make meal suggestions based on the amount of exercise. For example, it can suggest recovery menus after exercise to promote muscle recovery. It can also suggest meals with the appropriate calorie content if energy needs to be replenished before exercise. Furthermore, for employees who are not used to exercising, it can make meal suggestions to encourage light exercise. This makes it possible to provide more personalized health support based on employees' exercise data.

[0104] The analysis unit not only predicts individual health risks based on employees' health data, but also takes into account the health data of employees' families to evaluate health risks for the entire household and suggest preventative measures. For example, it can suggest a balanced meal plan that will help the whole family stay healthy. It can also provide specific advice on diet and exercise for family members with specific health risks based on the results of their health checkups. This makes it possible to support the health of not only employees, but also their families.

[0105] The analysis unit can not only analyze stress levels, but also suggest relaxation methods based on employees' hobbies and interests. For example, for employees who like music, it can recommend music with a relaxing effect. For employees who like reading, it can also provide a relaxing reading space. Furthermore, for employees who like outdoor activities, it can suggest relaxation methods in nature. In this way, it is possible to provide relaxation methods that suit each employee's individual hobbies and interests.

[0106] The analysis unit uses the emotion estimation function to analyze the employee's emotional state and can suggest exercise plans that correspond to their emotions. For example, for employees experiencing high stress, it can suggest yoga or meditation, which have a relaxing effect. For employees who are feeling down, it can also suggest light jogging or walking to lift their spirits. Furthermore, for employees with positive emotions, it can suggest active exercise to release energy. This makes it possible to provide exercise plans that correspond to the employee's emotional state.

[0107] The analysis unit not only proposes exercise plans based on health data, but can also analyze employees' sleep data and propose exercise plans to improve sleep quality. For example, for employees with poor sleep quality, it can suggest relaxing stretching or yoga. It can also suggest light exercise to promote recovery after exercise. It can also propose plans that take into account the balance of exercise and diet to improve sleep quality. This makes it possible to provide more personalized health support based on employees' sleep data.

[0108] In addition to analyzing sleep data, the analysis unit can collect data on employees' work environment and make suggestions for improving it. For example, it can monitor the lighting, temperature, and noise levels in the workplace and suggest the optimal environment. It can also improve the workplace layout and provide spaces where employees can relax. Furthermore, based on the data on the work environment, it can suggest specific improvements to reduce employees' stress levels. This can improve employees' work environment and improve their overall health.

[0109] The analysis unit uses the emotion estimation function to suggest relaxation methods according to the employee's emotional state, and can also suggest improvements to the work environment based on their emotions. For example, for employees experiencing high levels of stress, it can provide relaxing lighting and music. For employees feeling depressed, it can provide colorful decorations and plants to lift their spirits. Furthermore, it can provide employees with positive emotions with an active space to release their energy. This makes it possible to improve the work environment according to the employee's emotional state.

[0110] The proposal department can not only take into account seasonal nutritional fluctuations, but also propose meal plans that suit employees' lifestyles and working hours. For example, for employees who work many night shifts, it can provide meal menus suitable for the evening. For employees who travel frequently, it can also suggest healthy menus that are easy to choose when eating out. Furthermore, for employees who frequently work from home, it can propose balanced meal plans that can be easily prepared at home. This makes it possible to provide optimal meal plans that suit employees' lifestyles and working hours.

[0111] The Proposal Department not only analyzes meal history and makes meal suggestions aimed at maintaining long-term health, but also periodically surveys employees' meal satisfaction and can improve menus based on the results. For example, they can conduct questionnaire surveys to collect employees' opinions. They can also analyze the results of meal satisfaction surveys and make suggestions to increase the number of popular menu items. They can also introduce new menu items in response to employee requests. This makes it possible to propose measures to improve employee meal satisfaction.

[0112] The suggestion unit uses the emotion estimation function to suggest food combinations that match the employee's mood, and can also suggest desserts and drinks based on the employee's emotions. For example, a chocolate dessert can be offered to an employee who is feeling down. A relaxing herbal tea can be offered to an employee who is under a lot of stress. Furthermore, a smoothie that is perfect for replenishing energy can be offered to an employee who is in a good mood. This makes it possible to suggest desserts and drinks that match the employee's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The health data acquisition unit acquires employee health data. For example, the health data acquisition unit collects data such as the employee's blood pressure, heart rate, and weight. The health data acquisition unit can also collect the employee's health checkup results and lifestyle data (such as the amount of exercise, diet, and sleep time). Step 2: The request data acquisition unit acquires employee request data. For example, the request data acquisition unit collects data such as employees' food preferences, allergy information, and health goals. The request data acquisition unit can also collect employee feedback and survey data about their meals (data about favorite dishes and new menu items). Step 3: The analysis unit analyzes the data acquired by the health data acquisition unit and the need data acquisition unit. For example, the analysis unit uses data mining technology and statistical analysis technology to analyze the employee's health status and needs and evaluate health risks. Furthermore, the analysis unit uses machine learning algorithms to predict the employee's health status and needs and predict future health risks. Step 4: The proposal unit proposes a meal plan based on the results of the analysis by the analysis unit. For example, the proposal unit proposes a meal plan suitable for the employee, taking into account calorie calculations and nutritional balance. The proposal unit also selects ingredients and proposes a balanced meal plan based on the employee's health condition and requests.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a health data acquisition department that acquires employee health data; a request data acquisition unit that acquires employee request data; an analysis unit that analyzes the data acquired by the health data acquisition unit and the request data acquisition unit; a proposal unit that proposes a meal plan based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Predicting individual health risks based on the health data and suggesting preventative measures 2. The system of claim 1.

3. The analysis unit Analyzes stress levels and suggests meals and breaks to reduce stress 2. The system of claim 1.

4. The analysis unit Analyzes emotional state and makes meal suggestions based on emotions 2. The system of claim 1.

5. The analysis unit Based on the health data, we propose an exercise plan to help you balance your diet and exercise.

2. The system of claim 1.

6. The analysis unit Analyzes sleep data and provides dietary suggestions to improve sleep quality 2. The system of claim 1.

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