System

Abstract

An object of a system according to an embodiment is to provide an optimal diet plan based on a lifestyle and a health condition of a user and to maintain a continuous motivation.SOLUTION: A system includes a data collection part, an analysis part, a plan generation part, and a notification part. The data collection unit collects information on a lifestyle and a health condition of a user. The analysis unit analyzes the information collected by the data collection unit. The plan generation unit generates an optimal diet plan based on the information analyzed by the analysis unit. The notification unit notifies the user of the plan generated by the plan generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide users with optimal diet plans based on their lifestyle habits and health status, making it difficult to maintain ongoing motivation.

[0005] The system according to the embodiment aims to provide an optimal diet plan based on the user's lifestyle and health condition, and to maintain continuous motivation. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a plan generation unit, and a notification unit. The data collection unit collects information related to the user's lifestyle habits and health condition. The analysis unit analyzes the information collected by the data collection unit. The plan generation unit generates an optimal diet plan based on the information analyzed by the analysis unit. The notification unit notifies the user of the plan generated by the plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment provides an optimal diet plan based on the user's lifestyle and health condition, and can maintain continuous motivation. [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 diet plan proposal system according to the embodiment of the present invention utilizes a generation AI to propose an optimal diet plan to a user and utilizes gaming theory to continuously motivate the user, thereby enabling the user to continue dieting in an enjoyable and effective manner.

[0029] A diet plan proposal system according to an embodiment includes a data collection unit, an analysis unit, a plan generation unit, and a notification unit. The data collection unit collects information related to a user's lifestyle and health condition. For example, the data collection unit collects information such as the user's dietary habits, exercise history, and sleep patterns. The data collection unit can also collect data in real time using a wearable device or a smartphone app. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the user's health condition and lifestyle using statistical analysis or a machine learning algorithm. The analysis unit can also analyze the user's psychological state using an emotion estimation function. The plan generation unit generates an optimal diet plan based on the information analyzed by the analysis unit. For example, the plan generation unit generates a meal plan that takes into account calorie restriction and nutritional balance. The plan generation unit can also generate an exercise plan linked to the user's hobbies and interests by taking into account the user's hobbies and interests. The notification unit notifies the user of the plan generated by the plan generation unit. For example, the notification unit notifies the user of the plan using a smartphone push notification or email notification. The notification unit can also work with the user's calendar app to suggest optimal exercise times based on the user's schedule. This allows the diet plan suggestion system according to the embodiment to provide an optimal diet plan based on the user's lifestyle and health condition.

[0030] The data collection unit can collect the user's diet and exercise history in real time. The analysis unit can fine-tune the plan based on the data collected by the data collection unit in accordance with daily changes. The data collection unit, for example, collects the user's diet and exercise history entered daily in real time. For example, the data collection unit can collect the user's diet and exercise history in real time using a wearable device or a smartphone app. For example, if the user ate a high-calorie meal the previous day, the analysis unit presents a low-calorie meal plan for the next day. The analysis unit also analyzes the user's muscle fatigue level and recovery status based on the user's exercise history and suggests an appropriate exercise intensity. For example, if the user performed intense exercise the previous day, the analysis unit suggests light stretching or walking the next day. The analysis unit also analyzes the user's diet history and presents a meal plan that takes nutritional balance into consideration. For example, if the user was deficient in protein the previous day, the analysis unit suggests a high-protein meal the next day. This allows the plan to be fine-tuned based on the user's diet and exercise history.

[0031] The data collection unit can collect the user's genetic information. The analysis unit can analyze the genetic information to generate a genetically optimized diet plan. The data collection unit, for example, collects the user's genetic information. For example, the data collection unit can collect the genetic information based on DNA testing or family history. The analysis unit, for example, analyzes the user's genetic information to generate a genetically optimized diet plan. For example, if a specific gene affects fat metabolism, the analysis unit can suggest a diet suitable for that gene. The analysis unit can also present a plan that maximizes the effectiveness of exercise based on the user's genetic information. For example, if a user has high genetic endurance, the analysis unit can suggest a plan centered on running or cycling. The analysis unit can also analyze the genetic information to present a diet plan including ingredients that have a high absorption efficiency for specific nutrients. For example, if a user has a gene that promotes vitamin D absorption, the analysis unit can suggest ingredients that are high in vitamin D. This allows the system to provide an optimal diet plan based on the user's genetic information.

[0032] The plan generation unit can generate an exercise plan linked to a user's hobbies and interests based on the user's hobbies and interests. For example, if the user's hobby is dancing, the plan generation unit will propose an exercise plan that incorporates dancing. For example, the plan generation unit can incorporate dance lessons into daily exercise. If the user enjoys outdoor activities, the plan generation unit will propose an exercise plan that includes hiking and camping. For example, weekend activities in nature can be planned. If the user's hobby is music, the plan generation unit will propose exercises that are timed with music. For example, the plan generation unit can present a plan that includes aerobics and rhythmic exercises done while listening to favorite music. This makes it possible to provide an exercise plan based on the user's hobbies and interests.

[0033] The data collection unit can also collect data on the user's family and friends. The plan generation unit can generate a plan for collaborative work based on the data collected by the data collection unit. The data collection unit, for example, collects data on the user's family and friends. For example, the data collection unit can collect health data and lifestyle habit data of the family and friends. The plan generation unit, for example, proposes a collaborative exercise plan based on the data of the user's family and friends. For example, it plans walking or cycling that the whole family can participate in. The plan generation unit also proposes exercise plans that can be done with friends. For example, it proposes a plan to go to the gym with friends or an online group exercise. The plan generation unit also proposes a meal plan that can be enjoyed with family and friends. For example, it plans healthy recipes to make together or a collaborative meal challenge. This makes it possible to provide plans for collaborative work with the user's family and friends.

[0034] The notification unit can work with the user's calendar app to suggest optimal exercise times based on the user's schedule. The notification unit, for example, works with the user's calendar app to suggest optimal exercise times based on the user's schedule. For example, it can incorporate short exercise sessions between meetings or appointments. The notification unit also analyzes data from the calendar app to identify the time of day when it is easiest for the user to exercise. For example, it can suggest exercise before commuting in the morning or during a lunch break. The notification unit can also adjust the type and intensity of exercise to suit the user's schedule. For example, it can suggest light exercise on busy days and high-intensity exercise on days when the user has more time. This allows the system to provide optimal exercise times based on the user's schedule.

[0035] The data collection unit can collect the user's sleep patterns. The analysis unit can analyze the data collected by the data collection unit and suggest an optimal exercise time slot. The data collection unit, for example, collects the user's sleep patterns. For example, the data collection unit can collect the user's sleep patterns using a sleep tracker or self-reporting. The analysis unit, for example, analyzes the user's sleep patterns and suggests an optimal exercise time slot. For example, it suggests exercising during the time when the user is most energetic. The analysis unit also adjusts the timing of exercise based on the sleep data. For example, it suggests light exercise the morning after staying up late, and suggests high-intensity exercise on days when the user has had enough sleep. The analysis unit also selects the type of exercise based on the user's sleep patterns. For example, it suggests relaxing yoga or stretching on days when the user has not had enough sleep. This makes it possible to provide an optimal exercise time slot based on the user's sleep patterns.

[0036] The data collection unit can collect the user's movement data. The analysis unit can analyze the data collected by the data collection unit and propose an exercise plan that utilizes commuting time. The data collection unit, for example, collects the user's movement data. For example, the data collection unit can collect the user's movement data using GPS data and movement history. The analysis unit, for example, analyzes the user's movement data and proposes an exercise plan that utilizes commuting time. For example, incorporating walking or cycling during the commute. The analysis unit also proposes exercises that can be done while traveling based on the movement data. For example, it proposes stretching or light exercises that can be done on the train or bus. The analysis unit also analyzes the user's movement route and proposes gyms or parks that can be stopped at along the way. For example, it presents a plan to use fitness facilities on the commuting route. In this way, an exercise plan that utilizes commuting time, etc. can be provided based on the user's movement data.

[0037] The notification unit can suggest group exercise plans in conjunction with the user's work or school schedule. The notification unit, for example, can suggest group exercise plans in conjunction with the user's work or school schedule. For example, it can plan exercises to do with colleagues or classmates during a lunch break. The notification unit can also suggest group exercise plans taking into account work or school event schedules. For example, it can plan sporting events or fitness challenges. The notification unit can also adjust the timing of group exercises based on the user's work or school schedule. For example, it can suggest short exercises to do between meetings or classes. This makes it possible to provide group exercise plans based on the user's work or school schedule.

[0038] The notification unit can suggest portable exercise equipment that can be used when the user is traveling or on a business trip. The notification unit can suggest, for example, portable exercise equipment that can be used when the user is traveling or on a business trip. For example, it can recommend a foldable yoga mat or lightweight dumbbells. The notification unit can also suggest exercise plans that can be done while traveling or on a business trip. For example, it can present a plan that includes stretching and bodyweight training that can be done in a hotel room. The notification unit can also provide exercise videos that use portable exercise equipment. For example, it can introduce videos of exercises that can be easily done while traveling. This makes it possible to provide exercise equipment that can be used when the user is traveling or on a business trip.

[0039] The plan generation unit can provide virtual rewards and titles according to the user's level of achievement. The plan generation unit provides virtual rewards and titles according to, for example, the user's level of exercise and diet achievement. For example, a virtual medal is awarded when a certain amount of exercise is achieved. The plan generation unit also provides virtual titles each time the user achieves a goal set by the user. For example, a title such as "Fitness Champion" is awarded according to the number of consecutive days of exercise. The plan generation unit also displays virtual rewards on the user's profile to enhance the user's sense of achievement. For example, the profile page displays achieved goals and earned titles. This makes it possible to provide virtual rewards and titles according to the user's level of achievement.

[0040] The plan generation unit prepares a customizable avatar according to the user's preferences and allows the avatar to grow according to the progress of the exercise. For example, the plan generation unit prepares a customizable avatar according to the user's preferences and allows the avatar to grow according to the progress of the exercise. For example, the avatar's appearance and equipment can be upgraded by continuing to exercise. The plan generation unit also provides the user with new challenges and missions according to the growth of the avatar. For example, when the avatar reaches a certain level, a new exercise plan or meal menu is suggested. The plan generation unit also introduces a system that allows the user to acquire items to customize the avatar. For example, the avatar's costumes and accessories can be acquired according to the level of exercise and diet achievement. This allows the user to provide a customizable avatar according to the user's preferences and allows the avatar to grow according to the progress of the exercise.

[0041] The plan generation unit can introduce a game element in which users form teams and work together to achieve a goal. The plan generation unit, for example, introduces a game element in which users form teams and work together to achieve a goal. For example, a reward can be earned if all members of a team achieve a certain amount of exercise. The plan generation unit also builds a system in which team members encourage each other to achieve a goal. For example, they can send each other messages of encouragement using a message function. The plan generation unit can also plan team challenges to increase competitive spirit. For example, teams can compete against each other to see who can do the most exercise, and the winning team can be given a prize. This makes it possible to provide a game element in which users work together to achieve a goal.

[0042] The plan generation unit can provide an exercise game using AR (augmented reality) based on the user's exercise data. The plan generation unit provides an exercise game using AR (augmented reality) based on the user's exercise data, for example. For example, the actual exercise is reflected in the movements of a character in the game. The plan generation unit also uses AR technology to build a system that allows the user to exercise together with a virtual training partner. For example, a virtual trainer provides exercise instruction in real time. The plan generation unit also provides missions and challenges in the AR game based on the user's exercise data. For example, in-game items can be earned by performing a specific exercise a certain number of times. In this way, an exercise game using AR can be provided based on the user's exercise data.

[0043] The plan generation unit can analyze user feedback in real time and instantly revise the plan. The plan generation unit, for example, builds a system that analyzes user feedback in real time and instantly revise the plan. For example, if a user provides feedback that "this exercise is too hard," the exercise intensity is immediately adjusted. The plan generation unit also revise the meal plan in real time based on user feedback. For example, if feedback is provided that "I don't like this meal," a different menu is suggested. The plan generation unit also analyzes feedback in real time and instantly provides a plan tailored to the user's preferences. For example, new exercises and meal menus are added in response to feedback such as "I want more variety." This allows the plan to be instantly revised based on user feedback.

[0044] The plan generation unit allows the AI ​​to automatically generate new exercise and meal menus based on user feedback. For example, the plan generation unit automatically generates a new exercise menu based on user feedback. For example, it suggests new exercises in response to feedback such as "I'm bored of this exercise." The plan generation unit also analyzes user feedback and automatically generates new meal menus based on the AI. For example, it suggests new recipes in response to feedback such as "I want more variety." The plan generation unit also builds a system in which the AI ​​automatically customizes exercise and meal plans based on feedback. For example, it generates a new plan tailored to the user's preferences and health condition. This allows the AI ​​to automatically generate new exercise and meal menus based on user feedback.

[0045] The plan generation unit can extract common improvements that can be applied to other users based on user feedback. The plan generation unit, for example, analyzes user feedback and extracts common improvements that can be applied to other users. For example, if many users provide feedback that "this exercise is too hard," the plan generation unit adjusts the overall exercise intensity. The plan generation unit also extracts common improvements based on feedback and builds a system that applies to all users. For example, a new menu is added in response to feedback such as "I would like more variety." The plan generation unit also identifies common improvements based on user feedback and improves the quality of the overall plan. For example, the timing of exercise is adjusted in response to feedback such as "exercising at this time of day is not effective." In this way, common improvements that can be applied to other users can be extracted based on user feedback.

[0046] The plan generation unit can propose plans according to the season and weather based on user feedback. For example, the plan generation unit proposes exercise plans according to the season and weather based on user feedback. For example, outdoor walking or running is proposed in summer, and indoor exercise is proposed in winter. The plan generation unit also analyzes the feedback and proposes meal plans according to the season and weather. For example, hot soup or hot pot dishes are proposed in winter, and cold salads and fruit are proposed in summer. The plan generation unit also builds a system that customizes plans according to the season and weather based on user feedback. For example, indoor exercises are proposed on rainy days. In this way, plans according to the season and weather can be provided based on user feedback.

[0047] The data collection unit can monitor the user's health data in real time and immediately issue an alert if an abnormality is detected. The data collection unit, for example, builds a system that monitors the user's health data in real time and immediately issues an alert if an abnormality is detected. For example, an alert is sent if the heart rate becomes abnormally high. The data collection unit also suggests specific actions to take based on the health data when an abnormality is detected. For example, it may suggest reviewing the meal plan if weight increases rapidly. The data collection unit also analyzes the user's health data in real time and encourages the user to visit a medical institution if an abnormality is detected. For example, it may recommend a doctor's examination if blood pressure is abnormally high. This makes it possible to monitor the user's health data in real time and immediately issue an alert if an abnormality is detected.

[0048] The data collection unit can predict individual health risks based on the user's health data and suggest preventive measures. The data collection unit, for example, builds a system that predicts individual health risks based on the user's health data and suggests preventive measures. For example, future health risks are evaluated based on blood pressure and blood sugar level data. The data collection unit also analyzes the health data and suggests preventive measures according to the individual health risks. For example, if the risk of heart disease is high, a heart-friendly diet and exercise plan is suggested. The data collection unit also predicts individual health risks based on the user's health data and encourages regular health checks and visits to medical institutions. For example, if a specific risk is high, regular health checks are recommended. In this way, individual health risks can be predicted based on the user's health data and preventive measures can be suggested.

[0049] The data collection unit can provide a function for managing the health of the entire family based on the user's health data. The data collection unit, for example, builds a system that provides a function for managing the health of the entire family based on the user's health data. For example, the data collection unit centrally manages the health data of all family members and sets common health goals. The data collection unit also analyzes the health data of all family members, identifies common health risks, and suggests preventive measures. For example, it provides exercise plans and meal plans for the entire family to work on. The data collection unit also develops an app that supports health management of the entire family based on the user's health data. For example, it shares the health data of all family members and monitors their health status in real time. This makes it possible to provide a function for managing the health of the entire family based on the user's health data.

[0050] The data collection unit can analyze health trends across an entire region based on the user's health data and propose health measures for each region. The data collection unit, for example, builds a system that analyzes health trends across an entire region based on the user's health data and proposes health measures for each region. For example, it identifies health risks for each region and proposes preventive measures. The data collection unit also analyzes health data for the entire region, identifies common health risks, and proposes health measures for each region. For example, it proposes measures to improve lack of exercise and diet in a specific region. The data collection unit also analyzes health trends across an entire region based on the user's health data and plans health events and campaigns for each region. For example, it holds health fairs and fitness challenges for each region. This makes it possible to analyze health trends across an entire region based on the user's health data and propose health measures for each region.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The data collection unit can also collect data on the user's family and friends. The plan generation unit can generate a plan for collaborative efforts based on the data collected by the data collection unit. For example, it can plan a walking or cycling trip that the whole family can participate in. The plan generation unit also suggests exercise plans that can be done with friends. For example, it can suggest a plan to go to the gym with friends or an online group exercise. The plan generation unit also suggests meal plans that can be enjoyed with family and friends. For example, it can plan healthy recipes to make together or a meal challenge to participate in together. This makes it possible to provide plans for collaborative efforts with the user's family and friends.

[0053] The plan generation unit can generate an exercise plan linked to a user's hobbies and interests based on the user's hobbies and interests. For example, if the user's hobby is dancing, the plan generation unit can suggest an exercise plan that incorporates dancing. For example, dance lessons can be incorporated into daily exercise. If the user likes outdoor activities, the plan generation unit can suggest an exercise plan that includes hiking and camping. For example, weekend activities in nature can be planned. If the user's hobby is music, the plan generation unit can suggest exercises that are timed with music. For example, the plan generation unit can present a plan that includes aerobics and rhythmic exercises done while listening to favorite music. This makes it possible to provide an exercise plan based on the user's hobbies and interests.

[0054] The data collection unit can collect the user's genetic information. The analysis unit can analyze the genetic information to generate a genetically optimized diet plan. For example, the genetic information can be collected based on DNA testing or family history. For example, if a specific gene affects fat metabolism, the analysis unit can suggest a diet appropriate for that gene. The analysis unit also presents a plan that maximizes the effectiveness of exercise based on the user's genetic information. For example, if a user has a genetically high level of endurance, the analysis unit can suggest a plan centered on running or cycling. The analysis unit also analyzes the genetic information to present a meal plan that includes ingredients that have a high absorption efficiency for specific nutrients. For example, if a user has a gene that promotes vitamin D absorption, the analysis unit can suggest ingredients that are high in vitamin D. This allows the system to provide an optimal diet plan based on the user's genetic information.

[0055] The notification unit works in conjunction with the user's calendar app to suggest optimal exercise times based on the user's schedule. For example, it can schedule short exercise sessions between meetings or appointments. The notification unit also analyzes data from the calendar app to identify the times when it is easiest for the user to exercise. For example, it can suggest exercising before commuting in the morning or during lunch breaks. The notification unit also adjusts the type and intensity of exercise to suit the user's schedule. For example, it can suggest light exercise on busy days and high-intensity exercise on days when the user has more time. This allows the device to provide optimal exercise times based on the user's schedule.

[0056] The data collection unit can collect the user's sleep patterns. The analysis unit can analyze the data collected by the data collection unit and suggest optimal exercise times. For example, the user's sleep patterns can be collected using a sleep tracker or self-reporting. The analysis unit, for example, suggests exercise during times when the user is most energetic. The analysis unit also adjusts the timing of exercise based on the sleep data. For example, it suggests light exercise the morning after staying up late, and high-intensity exercise on days when the user has had enough sleep. The analysis unit also selects the type of exercise based on the user's sleep patterns. For example, it suggests relaxing yoga or stretching on days when the user has not had enough sleep. This makes it possible to suggest optimal exercise times based on the user's sleep patterns.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data collection unit collects information about the user's lifestyle and health status. For example, the data collection unit collects information such as the user's diet, exercise history, and sleep patterns. The data collection unit can also collect data in real time using wearable devices or smartphone apps. Step 2: The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the user's health condition and lifestyle habits using statistical analysis and machine learning algorithms. The analysis unit can also analyze the user's psychological state using an emotion estimation function. Step 3: The plan generation unit generates an optimal diet plan based on the information analyzed by the analysis unit. For example, the plan generation unit generates a meal plan that takes into account calorie restriction and nutritional balance. The plan generation unit can also generate an exercise plan that takes into account the user's hobbies and interests and is linked to those hobbies. Step 4: The notification unit notifies the user of the plan generated by the plan generation unit. For example, the notification unit notifies the user of the plan using a push notification on a smartphone or an email notification. The notification unit can also work with the user's calendar app to suggest optimal exercise times based on the user's schedule.

[0059] (Example 2) The diet plan proposal system according to the embodiment of the present invention utilizes a generation AI to propose an optimal diet plan to a user and utilizes gaming theory to continuously motivate the user, thereby enabling the user to continue dieting in an enjoyable and effective manner.

[0060] A diet plan proposal system according to an embodiment includes a data collection unit, an analysis unit, a plan generation unit, and a notification unit. The data collection unit collects information related to a user's lifestyle and health condition. For example, the data collection unit collects information such as the user's dietary habits, exercise history, and sleep patterns. The data collection unit can also collect data in real time using a wearable device or a smartphone app. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the user's health condition and lifestyle using statistical analysis or a machine learning algorithm. The analysis unit can also analyze the user's psychological state using an emotion estimation function. The plan generation unit generates an optimal diet plan based on the information analyzed by the analysis unit. For example, the plan generation unit generates a meal plan that takes into account calorie restriction and nutritional balance. The plan generation unit can also generate an exercise plan linked to the user's hobbies and interests by taking into account the user's hobbies and interests. The notification unit notifies the user of the plan generated by the plan generation unit. For example, the notification unit notifies the user of the plan using a smartphone push notification or email notification. The notification unit can also work with the user's calendar app to suggest optimal exercise times based on the user's schedule. This allows the diet plan suggestion system according to the embodiment to provide an optimal diet plan based on the user's lifestyle and health condition.

[0061] The data collection unit can collect the user's diet and exercise history in real time. The analysis unit can fine-tune the plan based on the data collected by the data collection unit in accordance with daily changes. The data collection unit, for example, collects the user's diet and exercise history entered daily in real time. For example, the data collection unit can collect the user's diet and exercise history in real time using a wearable device or a smartphone app. For example, if the user ate a high-calorie meal the previous day, the analysis unit presents a low-calorie meal plan for the next day. The analysis unit also analyzes the user's muscle fatigue level and recovery status based on the user's exercise history and suggests an appropriate exercise intensity. For example, if the user performed intense exercise the previous day, the analysis unit suggests light stretching or walking the next day. The analysis unit also analyzes the user's diet history and presents a meal plan that takes nutritional balance into consideration. For example, if the user was deficient in protein the previous day, the analysis unit suggests a high-protein meal the next day. This allows the plan to be fine-tuned based on the user's diet and exercise history.

[0062] The analysis unit can analyze the user's psychological state using the emotion estimation function and present a plan based on the user's stress level. For example, the analysis unit can analyze the user's psychological state using the emotion estimation function and, if the stress level is high, suggest exercises and meals that have a relaxing effect. For example, a plan incorporating yoga or herbal tea can be presented. The analysis unit can also suggest a challenging exercise plan based on the user's emotional data when stress is low. For example, a plan including running and strength training can be presented. The analysis unit can also use the emotion estimation function to present a meal plan based on the user's stress level. For example, if stress is high, a recipe using ingredients that have a relaxing effect can be proposed. This makes it possible to provide a plan based on the user's psychological state.

[0063] The data collection unit can collect the user's genetic information. The analysis unit can analyze the genetic information to generate a genetically optimized diet plan. The data collection unit, for example, collects the user's genetic information. For example, the data collection unit can collect the genetic information based on DNA testing or family history. The analysis unit, for example, analyzes the user's genetic information to generate a genetically optimized diet plan. For example, if a specific gene affects fat metabolism, the analysis unit can suggest a diet suitable for that gene. The analysis unit can also present a plan that maximizes the effectiveness of exercise based on the user's genetic information. For example, if a user has high genetic endurance, the analysis unit can suggest a plan centered on running or cycling. The analysis unit can also analyze the genetic information to present a diet plan including ingredients that have a high absorption efficiency for specific nutrients. For example, if a user has a gene that promotes vitamin D absorption, the analysis unit can suggest ingredients that are high in vitamin D. This allows the system to provide an optimal diet plan based on the user's genetic information.

[0064] The plan generation unit can generate an exercise plan linked to a user's hobbies and interests based on the user's hobbies and interests. For example, if the user's hobby is dancing, the plan generation unit will propose an exercise plan that incorporates dancing. For example, the plan generation unit can incorporate dance lessons into daily exercise. If the user enjoys outdoor activities, the plan generation unit will propose an exercise plan that includes hiking and camping. For example, weekend activities in nature can be planned. If the user's hobby is music, the plan generation unit will propose exercises that are timed with music. For example, the plan generation unit can present a plan that includes aerobics and rhythmic exercises done while listening to favorite music. This makes it possible to provide an exercise plan based on the user's hobbies and interests.

[0065] The data collection unit can also collect data on the user's family and friends. The plan generation unit can generate a plan for collaborative work based on the data collected by the data collection unit. The data collection unit, for example, collects data on the user's family and friends. For example, the data collection unit can collect health data and lifestyle habit data of the family and friends. The plan generation unit, for example, proposes a collaborative exercise plan based on the data of the user's family and friends. For example, it plans walking or cycling that the whole family can participate in. The plan generation unit also proposes exercise plans that can be done with friends. For example, it proposes a plan to go to the gym with friends or an online group exercise. The plan generation unit also proposes a meal plan that can be enjoyed with family and friends. For example, it plans healthy recipes to make together or a collaborative meal challenge. This makes it possible to provide plans for collaborative work with the user's family and friends.

[0066] The plan generation unit can use the emotion estimation function to suggest exercises and meals that the user will enjoy most. The plan generation unit, for example, uses the emotion estimation function to suggest exercises that the user will enjoy most. For example, it identifies exercises that the user feels they enjoy and creates a plan centered around those exercises. The plan generation unit also suggests a meal plan that the user will enjoy most based on the user's emotion data. For example, it provides recipes that incorporate the user's favorite ingredients and dishes. The plan generation unit also uses the emotion estimation function to suggest exercises for times of day that the user feels they enjoy. For example, it suggests yoga or stretching in the evening when the user is relaxed. This makes it possible to provide the user with exercises and meals that they will enjoy most.

[0067] The notification unit can work with the user's calendar app to suggest optimal exercise times based on the user's schedule. The notification unit, for example, works with the user's calendar app to suggest optimal exercise times based on the user's schedule. For example, it can incorporate short exercise sessions between meetings or appointments. The notification unit also analyzes data from the calendar app to identify the time of day when it is easiest for the user to exercise. For example, it can suggest exercise before commuting in the morning or during a lunch break. The notification unit can also adjust the type and intensity of exercise to suit the user's schedule. For example, it can suggest light exercise on busy days and high-intensity exercise on days when the user has more time. This allows the system to provide optimal exercise times based on the user's schedule.

[0068] The data collection unit can collect the user's sleep patterns. The analysis unit can analyze the data collected by the data collection unit and suggest an optimal exercise time slot. The data collection unit, for example, collects the user's sleep patterns. For example, the data collection unit can collect the user's sleep patterns using a sleep tracker or self-reporting. The analysis unit, for example, analyzes the user's sleep patterns and suggests an optimal exercise time slot. For example, it suggests exercising during the time when the user is most energetic. The analysis unit also adjusts the timing of exercise based on the sleep data. For example, it suggests light exercise the morning after staying up late, and suggests high-intensity exercise on days when the user has had enough sleep. The analysis unit also selects the type of exercise based on the user's sleep patterns. For example, it suggests relaxing yoga or stretching on days when the user has not had enough sleep. This makes it possible to provide an optimal exercise time slot based on the user's sleep patterns.

[0069] The data collection unit can collect the user's movement data. The analysis unit can analyze the data collected by the data collection unit and propose an exercise plan that utilizes commuting time. The data collection unit, for example, collects the user's movement data. For example, the data collection unit can collect the user's movement data using GPS data and movement history. The analysis unit, for example, analyzes the user's movement data and proposes an exercise plan that utilizes commuting time. For example, incorporating walking or cycling during the commute. The analysis unit also proposes exercises that can be done while traveling based on the movement data. For example, it proposes stretching or light exercises that can be done on the train or bus. The analysis unit also analyzes the user's movement route and proposes gyms or parks that can be stopped at along the way. For example, it presents a plan to use fitness facilities on the commuting route. In this way, an exercise plan that utilizes commuting time, etc. can be provided based on the user's movement data.

[0070] The notification unit can suggest group exercise plans in conjunction with the user's work or school schedule. The notification unit, for example, can suggest group exercise plans in conjunction with the user's work or school schedule. For example, it can plan exercises to do with colleagues or classmates during a lunch break. The notification unit can also suggest group exercise plans taking into account work or school event schedules. For example, it can plan sporting events or fitness challenges. The notification unit can also adjust the timing of group exercises based on the user's work or school schedule. For example, it can suggest short exercises to do between meetings or classes. This makes it possible to provide group exercise plans based on the user's work or school schedule.

[0071] The notification unit can suggest portable exercise equipment that can be used when the user is traveling or on a business trip. The notification unit can suggest, for example, portable exercise equipment that can be used when the user is traveling or on a business trip. For example, it can recommend a foldable yoga mat or lightweight dumbbells. The notification unit can also suggest exercise plans that can be done while traveling or on a business trip. For example, it can present a plan that includes stretching and bodyweight training that can be done in a hotel room. The notification unit can also provide exercise videos that use portable exercise equipment. For example, it can introduce videos of exercises that can be easily done while traveling. This makes it possible to provide exercise equipment that can be used when the user is traveling or on a business trip.

[0072] The notification unit can use the emotion estimation function to suggest exercises for times when the user is most relaxed. For example, the notification unit uses the emotion estimation function to suggest exercises for times when the user is most relaxed. For example, yoga or stretching is suggested for the evening when the user is relaxed. The notification unit also suggests exercises that have a relaxing effect based on the user's emotion data. For example, exercises that have a relaxing effect are performed during times when the user's emotions are stable. The notification unit also uses the emotion estimation function to present an exercise plan that the user can do during times when they are relaxed. For example, exercises to be done while listening to music that has a relaxing effect are suggested. This allows the user to exercise during times when they are most relaxed.

[0073] The plan generation unit can provide virtual rewards and titles according to the user's level of achievement. The plan generation unit provides virtual rewards and titles according to, for example, the user's level of exercise and diet achievement. For example, a virtual medal is awarded when a certain amount of exercise is achieved. The plan generation unit also provides virtual titles each time the user achieves a goal set by the user. For example, a title such as "Fitness Champion" is awarded according to the number of consecutive days of exercise. The plan generation unit also displays virtual rewards on the user's profile to enhance the user's sense of achievement. For example, the profile page displays achieved goals and earned titles. This makes it possible to provide virtual rewards and titles according to the user's level of achievement.

[0074] The plan generation unit prepares a customizable avatar according to the user's preferences and allows the avatar to grow according to the progress of the exercise. For example, the plan generation unit prepares a customizable avatar according to the user's preferences and allows the avatar to grow according to the progress of the exercise. For example, the avatar's appearance and equipment can be upgraded by continuing to exercise. The plan generation unit also provides the user with new challenges and missions according to the growth of the avatar. For example, when the avatar reaches a certain level, a new exercise plan or meal menu is suggested. The plan generation unit also introduces a system that allows the user to acquire items to customize the avatar. For example, the avatar's costumes and accessories can be acquired according to the level of exercise and diet achievement. This allows the user to provide a customizable avatar according to the user's preferences and allows the avatar to grow according to the progress of the exercise.

[0075] The notification unit can use the emotion estimation function to send an encouraging message when the user's motivation drops. The notification unit, for example, uses the emotion estimation function to send an encouraging message when the user's motivation drops. For example, when the emotion score is low, the notification unit sends a message such as "Do your best! You're almost there!". The notification unit also customizes the encouraging message when the user's motivation drops based on the user's emotion data. For example, it sends a personalized message that includes the user's name. The notification unit also uses the emotion estimation function to suggest specific actions the user can take to regain motivation. For example, it sends advice such as "Try taking a short break and then resume." In this way, it is possible to send an encouraging message when the user's motivation drops.

[0076] The plan generation unit can introduce a game element in which users form teams and work together to achieve a goal. The plan generation unit, for example, introduces a game element in which users form teams and work together to achieve a goal. For example, a reward can be earned if all members of a team achieve a certain amount of exercise. The plan generation unit also builds a system in which team members encourage each other to achieve a goal. For example, they can send each other messages of encouragement using a message function. The plan generation unit can also plan team challenges to increase competitive spirit. For example, teams can compete against each other to see who can do the most exercise, and the winning team can be given a prize. This makes it possible to provide a game element in which users work together to achieve a goal.

[0077] The plan generation unit can provide an exercise game using AR (augmented reality) based on the user's exercise data. The plan generation unit provides an exercise game using AR (augmented reality) based on the user's exercise data, for example. For example, the actual exercise is reflected in the movements of a character in the game. The plan generation unit also uses AR technology to build a system that allows the user to exercise together with a virtual training partner. For example, a virtual trainer provides exercise instruction in real time. The plan generation unit also provides missions and challenges in the AR game based on the user's exercise data. For example, in-game items can be earned by performing a specific exercise a certain number of times. In this way, an exercise game using AR can be provided based on the user's exercise data.

[0078] The plan generation unit can use the emotion estimation function to suggest game elements that the user will enjoy the most. The plan generation unit, for example, uses the emotion estimation function to suggest game elements that the user will enjoy the most. For example, the plan generation unit identifies game elements that the user finds enjoyable and designs a game around those elements. The plan generation unit also customizes the most enjoyable game elements based on the user's emotion data. For example, the plan generation unit suggests games that incorporate the user's favorite themes or characters. The plan generation unit also uses the emotion estimation function to suggest game elements for times when the user will enjoy playing. For example, the plan generation unit suggests playing a game during times when the user is relaxing. This makes it possible to provide game elements that the user will enjoy the most.

[0079] The plan generation unit can analyze user feedback in real time and instantly revise the plan. The plan generation unit, for example, builds a system that analyzes user feedback in real time and instantly revise the plan. For example, if a user provides feedback that "this exercise is too hard," the exercise intensity is immediately adjusted. The plan generation unit also revise the meal plan in real time based on user feedback. For example, if feedback is provided that "I don't like this meal," a different menu is suggested. The plan generation unit also analyzes feedback in real time and instantly provides a plan tailored to the user's preferences. For example, new exercises and meal menus are added in response to feedback such as "I want more variety." This allows the plan to be instantly revised based on user feedback.

[0080] The plan generation unit can analyze the user's feedback using the emotion estimation function and propose a plan that will increase emotional satisfaction. For example, the plan generation unit analyzes the user's feedback using the emotion estimation function and proposes a plan that will increase emotional satisfaction. For example, the plan generation unit adjusts the plan based on feedback that has a strong positive emotion. The plan generation unit also uses the emotion estimation function to analyze the emotional aspects of the feedback and provide a plan that will satisfy the user. For example, it proposes exercises and meals that the user feels are enjoyable. The plan generation unit also analyzes the feedback based on the user's emotion data and proposes specific actions to increase emotional satisfaction. For example, it provides exercises and meals that will relax the user. In this way, a plan that will increase emotional satisfaction can be provided based on the user's feedback.

[0081] The plan generation unit allows the AI ​​to automatically generate new exercise and meal menus based on user feedback. For example, the plan generation unit automatically generates a new exercise menu based on user feedback. For example, it suggests new exercises in response to feedback such as "I'm bored of this exercise." The plan generation unit also analyzes user feedback and automatically generates new meal menus based on the AI. For example, it suggests new recipes in response to feedback such as "I want more variety." The plan generation unit also builds a system in which the AI ​​automatically customizes exercise and meal plans based on feedback. For example, it generates a new plan tailored to the user's preferences and health condition. This allows the AI ​​to automatically generate new exercise and meal menus based on user feedback.

[0082] The plan generation unit can extract common improvements that can be applied to other users based on user feedback. The plan generation unit, for example, analyzes user feedback and extracts common improvements that can be applied to other users. For example, if many users provide feedback that "this exercise is too hard," the plan generation unit adjusts the overall exercise intensity. The plan generation unit also extracts common improvements based on feedback and builds a system that applies to all users. For example, a new menu is added in response to feedback such as "I would like more variety." The plan generation unit also identifies common improvements based on user feedback and improves the quality of the overall plan. For example, the timing of exercise is adjusted in response to feedback such as "exercising at this time of day is not effective." In this way, common improvements that can be applied to other users can be extracted based on user feedback.

[0083] The plan generation unit can propose plans according to the season and weather based on user feedback. For example, the plan generation unit proposes exercise plans according to the season and weather based on user feedback. For example, outdoor walking or running is proposed in summer, and indoor exercise is proposed in winter. The plan generation unit also analyzes the feedback and proposes meal plans according to the season and weather. For example, hot soup or hot pot dishes are proposed in winter, and cold salads and fruit are proposed in summer. The plan generation unit also builds a system that customizes plans according to the season and weather based on user feedback. For example, indoor exercises are proposed on rainy days. In this way, plans according to the season and weather can be provided based on user feedback.

[0084] The plan generation unit can use the emotion estimation function to analyze the emotional aspects of the feedback and propose a plan that is more in line with the emotions. The plan generation unit, for example, uses the emotion estimation function to analyze the emotional aspects of the feedback and propose a plan that is more in line with the emotions. For example, the plan is adjusted based on feedback that contains strong positive emotions. The plan generation unit also analyzes feedback based on the user's emotion data and provides a plan that increases emotional satisfaction. For example, it suggests exercises and meals that the user finds enjoyable. The plan generation unit also uses the emotion estimation function to analyze the emotional aspects of the feedback and propose a plan that satisfies the user. For example, it provides exercises and meals that allow the user to relax. In this way, the emotional aspects of the feedback can be analyzed and a plan that is more in line with the emotions can be provided.

[0085] The data collection unit can monitor the user's health data in real time and immediately issue an alert if an abnormality is detected. The data collection unit, for example, builds a system that monitors the user's health data in real time and immediately issues an alert if an abnormality is detected. For example, an alert is sent if the heart rate becomes abnormally high. The data collection unit also suggests specific actions to take based on the health data when an abnormality is detected. For example, it may suggest reviewing the meal plan if weight increases rapidly. The data collection unit also analyzes the user's health data in real time and encourages the user to visit a medical institution if an abnormality is detected. For example, it may recommend a doctor's examination if blood pressure is abnormally high. This makes it possible to monitor the user's health data in real time and immediately issue an alert if an abnormality is detected.

[0086] The data collection unit analyzes the user's health data using the emotion estimation function, allowing early detection of signs of stress and fatigue. The data collection unit, for example, analyzes the user's health data using the emotion estimation function to build a system that detects signs of stress and fatigue early. For example, it evaluates stress levels based on heart rate and sleep data. The data collection unit also analyzes the user's health data using the emotion estimation function, and suggests relaxing exercises and meals if signs of stress or fatigue are detected. For example, if stress is high, it suggests yoga or herbal tea. The data collection unit also monitors the user's health data in real time, and issues an alert if it detects signs of stress or fatigue using the emotion estimation function. For example, it notifies the user to take a rest if fatigue is accumulating. In this way, the user's health data can be analyzed using the emotion estimation function, allowing early detection of signs of stress and fatigue.

[0087] The data collection unit can predict individual health risks based on the user's health data and suggest preventive measures. The data collection unit, for example, builds a system that predicts individual health risks based on the user's health data and suggests preventive measures. For example, future health risks are evaluated based on blood pressure and blood sugar level data. The data collection unit also analyzes the health data and suggests preventive measures according to the individual health risks. For example, if the risk of heart disease is high, a heart-friendly diet and exercise plan is suggested. The data collection unit also predicts individual health risks based on the user's health data and encourages regular health checks and visits to medical institutions. For example, if a specific risk is high, regular health checks are recommended. In this way, individual health risks can be predicted based on the user's health data and preventive measures can be suggested.

[0088] The data collection unit can provide a function for managing the health of the entire family based on the user's health data. The data collection unit, for example, builds a system that provides a function for managing the health of the entire family based on the user's health data. For example, the data collection unit centrally manages the health data of all family members and sets common health goals. The data collection unit also analyzes the health data of all family members, identifies common health risks, and suggests preventive measures. For example, it provides exercise plans and meal plans for the entire family to work on. The data collection unit also develops an app that supports health management of the entire family based on the user's health data. For example, it shares the health data of all family members and monitors their health status in real time. This makes it possible to provide a function for managing the health of the entire family based on the user's health data.

[0089] The data collection unit can analyze health trends across an entire region based on the user's health data and propose health measures for each region. The data collection unit, for example, builds a system that analyzes health trends across an entire region based on the user's health data and proposes health measures for each region. For example, it identifies health risks for each region and proposes preventive measures. The data collection unit also analyzes health data for the entire region, identifies common health risks, and proposes health measures for each region. For example, it proposes measures to improve lack of exercise and diet in a specific region. The data collection unit also analyzes health trends across an entire region based on the user's health data and plans health events and campaigns for each region. For example, it holds health fairs and fitness challenges for each region. This makes it possible to analyze health trends across an entire region based on the user's health data and propose health measures for each region.

[0090] The data collection unit can use the emotion estimation function to provide health advice according to the user's emotional state. The data collection unit, for example, uses the emotion estimation function to build a system that provides health advice according to the user's emotional state. For example, if stress is high, the data collection unit suggests exercises and meals that have a relaxing effect. The data collection unit also provides health advice according to the user's emotional state based on the user's emotional data. For example, if positive emotions are strong, the data collection unit suggests a challenging exercise plan. The data collection unit also uses the emotion estimation function to provide health advice according to the user's emotional state in real time. For example, if emotions are unstable, the data collection unit suggests music or meditation that has a relaxing effect. This makes it possible to provide health advice according to the user's emotional state.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The data collection unit can also collect data on the user's family and friends. The plan generation unit can generate a plan for collaborative efforts based on the data collected by the data collection unit. For example, it can plan a walking or cycling trip that the whole family can participate in. The plan generation unit also suggests exercise plans that can be done with friends. For example, it can suggest a plan to go to the gym with friends or an online group exercise. The plan generation unit also suggests meal plans that can be enjoyed with family and friends. For example, it can plan healthy recipes to make together or a meal challenge to participate in together. This makes it possible to provide plans for collaborative efforts with the user's family and friends.

[0093] The plan generation unit can generate an exercise plan linked to a user's hobbies and interests based on the user's hobbies and interests. For example, if the user's hobby is dancing, the plan generation unit can suggest an exercise plan that incorporates dancing. For example, dance lessons can be incorporated into daily exercise. If the user likes outdoor activities, the plan generation unit can suggest an exercise plan that includes hiking and camping. For example, weekend activities in nature can be planned. If the user's hobby is music, the plan generation unit can suggest exercises that are timed with music. For example, the plan generation unit can present a plan that includes aerobics and rhythmic exercises done while listening to favorite music. This makes it possible to provide an exercise plan based on the user's hobbies and interests.

[0094] The data collection unit can collect the user's genetic information. The analysis unit can analyze the genetic information to generate a genetically optimized diet plan. For example, the genetic information can be collected based on DNA testing or family history. For example, if a specific gene affects fat metabolism, the analysis unit can suggest a diet appropriate for that gene. The analysis unit also presents a plan that maximizes the effectiveness of exercise based on the user's genetic information. For example, if a user has a genetically high level of endurance, the analysis unit can suggest a plan centered on running or cycling. The analysis unit also analyzes the genetic information to present a meal plan that includes ingredients that have a high absorption efficiency for specific nutrients. For example, if a user has a gene that promotes vitamin D absorption, the analysis unit can suggest ingredients that are high in vitamin D. This allows the system to provide an optimal diet plan based on the user's genetic information.

[0095] The notification unit works in conjunction with the user's calendar app to suggest optimal exercise times based on the user's schedule. For example, it can schedule short exercise sessions between meetings or appointments. The notification unit also analyzes data from the calendar app to identify the times when it is easiest for the user to exercise. For example, it can suggest exercising before commuting in the morning or during lunch breaks. The notification unit also adjusts the type and intensity of exercise to suit the user's schedule. For example, it can suggest light exercise on busy days and high-intensity exercise on days when the user has more time. This allows the device to provide optimal exercise times based on the user's schedule.

[0096] The data collection unit can collect the user's sleep patterns. The analysis unit can analyze the data collected by the data collection unit and suggest optimal exercise times. For example, the user's sleep patterns can be collected using a sleep tracker or self-reporting. The analysis unit, for example, suggests exercise during times when the user is most energetic. The analysis unit also adjusts the timing of exercise based on the sleep data. For example, it suggests light exercise the morning after staying up late, and high-intensity exercise on days when the user has had enough sleep. The analysis unit also selects the type of exercise based on the user's sleep patterns. For example, it suggests relaxing yoga or stretching on days when the user has not had enough sleep. This makes it possible to suggest optimal exercise times based on the user's sleep patterns.

[0097] The plan generation unit can use the emotion estimation function to suggest exercises and meals that the user will enjoy most. For example, it identifies exercises that the user finds enjoyable and creates a plan centered around those exercises. The plan generation unit also suggests a meal plan that the user will enjoy most based on the user's emotion data. For example, it provides recipes that incorporate the user's favorite ingredients and dishes. The plan generation unit also uses the emotion estimation function to suggest exercises during times of the day that the user finds enjoyable. For example, it suggests yoga or stretching in the evening when the user is relaxed. This makes it possible to provide the user with exercises and meals that they will enjoy most.

[0098] The notification unit can use the emotion estimation function to send an encouraging message when the user's motivation drops. For example, when the emotion score is low, it can send a message such as "Keep up the good work! You're almost there!" The notification unit can also customize the encouraging message based on the user's emotion data when motivation drops. For example, it can send a personalized message that includes the user's name. The notification unit can also use the emotion estimation function to suggest specific actions the user can take to regain motivation. For example, it can send advice such as "Try taking a short break and then resume." This makes it possible to send an encouraging message when the user's motivation drops.

[0099] The plan generation unit can analyze the user's feedback using the emotion estimation function and propose a plan that will increase emotional satisfaction. For example, the plan can be adjusted based on feedback that contains strong positive emotions. The plan generation unit can also use the emotion estimation function to analyze the emotional aspects of the feedback and provide a plan that will satisfy the user. For example, it can suggest exercises and meals that the user finds enjoyable. The plan generation unit can also analyze the feedback based on the user's emotion data and propose specific actions to increase emotional satisfaction. For example, it can provide exercises and meals that will relax the user. In this way, a plan that will increase emotional satisfaction can be provided based on the user's feedback.

[0100] The data collection unit can use the emotion estimation function to provide health advice according to the user's emotional state. For example, if stress is high, it can suggest exercise or meals that have a relaxing effect. The data collection unit also provides health advice according to the user's emotional state based on the user's emotional data. For example, if positive emotions are strong, it can suggest a challenging exercise plan. The data collection unit also uses the emotion estimation function to provide health advice according to the user's emotional state in real time. For example, if emotions are unstable, it can suggest music or meditation that has a relaxing effect. This makes it possible to provide health advice according to the user's emotional state.

[0101] The data collection unit analyzes the user's health data using the emotion estimation function, allowing for early detection of signs of stress and fatigue. For example, it evaluates stress levels based on heart rate and sleep data. The data collection unit also uses the emotion estimation function to analyze the user's health data and, if signs of stress or fatigue are detected, suggests exercise or meals that have a relaxing effect. For example, if stress is high, it suggests yoga or herbal tea. The data collection unit also monitors the user's health data in real time, and issues an alert using the emotion estimation function if it detects signs of stress or fatigue. For example, it notifies the user to take a rest if fatigue is accumulating. This allows the user's health data to be analyzed using the emotion estimation function, allowing for early detection of signs of stress and fatigue.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The data collection unit collects information about the user's lifestyle and health status. For example, the data collection unit collects information such as the user's diet, exercise history, and sleep patterns. The data collection unit can also collect data in real time using wearable devices or smartphone apps. Step 2: The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the user's health condition and lifestyle habits using statistical analysis and machine learning algorithms. The analysis unit can also analyze the user's psychological state using an emotion estimation function. Step 3: The plan generation unit generates an optimal diet plan based on the information analyzed by the analysis unit. For example, the plan generation unit generates a meal plan that takes into account calorie restriction and nutritional balance. The plan generation unit can also generate an exercise plan that takes into account the user's hobbies and interests and is linked to those hobbies. Step 4: The notification unit notifies the user of the plan generated by the plan generation unit. For example, the notification unit notifies the user of the plan using a push notification on a smartphone or an email notification. The notification unit can also work with the user's calendar app to suggest optimal exercise times based on the user's schedule.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 data collection unit that collects information about the user's lifestyle and health condition; an analysis unit that analyzes the information collected by the data collection unit; a plan generation unit that generates an optimal diet plan based on the information analyzed by the analysis unit; a notification unit that notifies a user of the plan generated by the plan generation unit. A system characterized by:

2. The data collection unit Collecting users' diet and exercise history in real time, The analysis unit The plan is fine-tuned according to daily changes based on the data collected by the data collection unit.

2. The system of claim 1.

3. The analysis unit Analyze the user's psychological state with emotion estimation function, Provide a plan based on your stress level 2. The system of claim 1.

4. The data collection unit Collecting the user's genetic information, The analysis unit Analyzing the genetic information to generate a genetically optimized diet plan 2. The system of claim 1.

5. The plan generation unit Generates exercise plans linked to user hobbies and interests 2. The system of claim 1.

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