System

Abstract

An object of a system according to an embodiment is to provide effective diet support by increasing motivation in accordance with an action or a situation of a user.SOLUTION: A system according to an embodiment includes an encouragement unit, a stretch video providing unit, and a mealkit suggestion unit. The encouragement unit provides encouragement in accordance with the user's behavior and situation. The stretch movie providing unit provides an optimal stretch movie to the user encouraged by the encouragement unit. The makeup kit proposing section proposes a makeup kit most suitable for the user based on the stretch moving image provided by the stretch moving image providing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to maintain motivation while dieting and lacking effective support.

[0005] The system according to the embodiment aims to increase motivation according to the user's behavior and situation and provide effective diet support. [Means for solving the problem]

[0006] The system according to the embodiment includes a pep talk unit, a stretching video providing unit, and a meal kit suggestion unit. The pep talk unit provides encouragement according to the user's behavior and situation. The stretching video providing unit provides an optimal stretching video to the user who has been encouraged by the pep talk unit. The meal kit suggestion unit suggests an optimal meal kit to the user based on the stretching video provided by the stretching video providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can increase motivation according to the user's behavior and situation, and provide effective diet support. [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 support system according to the embodiment of the present invention is a system in which a generation AI disguised as the user's "favorite" gives encouragement and encouragement to the user while dieting, thereby increasing the user's motivation. This enables the diet support system to increase the user's motivation and improve the success rate of dieting.

[0029] A diet support system according to an embodiment includes a motivational unit, a stretching video provider, and a meal kit suggestion unit. The motivational unit provides encouragement based on the user's behavior and situation. For example, when a user is about to slack off on exercise, the motivational unit offers words of encouragement such as, "Do your best! You can do it!" Furthermore, when a user's diet management is not going well, the motivational unit offers words of encouragement such as, "Try a little harder!" The stretching video provider provides the optimal stretching video to the user encouraged by the motivational unit. For example, when the user starts stretching, an AI disguised as a favorite idol may say, "Let's stretch together!" and play the video. The generation AI recommends the optimal stretching video based on user feedback. The meal kit suggestion unit suggests the optimal meal kit to the user based on the stretching video provided by the stretching video provider. For example, if a user has difficulty choosing a diet-friendly meal, an AI registered dietitian suggests a meal kit tailored to the user's needs. The generation AI analyzes the user's diet history and health condition to recommend the optimal meal kit. As a result, the diet support system according to the embodiment can provide appropriate support according to the user's behavior and situation, thereby increasing the success rate of dieting.

[0030] The stretching video providing unit can analyze the user's physical data and generate individually optimized stretching videos. For example, the stretching video providing unit analyzes the user's flexibility data and generates stretching videos according to the user's flexibility. For example, a user with low flexibility is provided with a stretching video for beginners. The stretching video providing unit also analyzes the user's muscle strength data and generates stretching videos according to the user's muscle strength. For example, a user with low muscle strength is provided with a stretching video with a light load. The stretching video providing unit also comprehensively analyzes the user's physical data and generates individually optimized stretching videos. For example, a stretching video that takes into account the balance between flexibility and muscle strength is provided. This makes it possible to generate optimal stretching videos based on the user's physical data and provide effective exercise.

[0031] The stretching video providing unit can adjust the content of the stretching video in real time based on user feedback to provide optimal exercise effects. The stretching video providing unit, for example, collects user feedback in real time and adjusts the content of the stretching video. For example, if the user provides feedback that "this movement is difficult," it makes the movement easier. The stretching video providing unit also adjusts the difficulty level of the stretching video based on user feedback. For example, if the user provides feedback that "I want to put more strain on myself," it increases the difficulty level. The stretching video providing unit also analyzes user feedback and adjusts the content of the stretching video to provide optimal exercise effects. For example, if the user provides feedback that "this part was effective," it strengthens that part. In this way, the content of the stretching video can be adjusted based on user feedback to provide optimal exercise effects.

[0032] The meal kit suggestion unit can analyze a user's dietary history and health data to generate an individually optimized meal kit. For example, the meal kit suggestion unit analyzes the user's dietary history and suggests an optimal meal kit based on the user's past eating patterns. For example, it provides a meal kit containing ingredients that the user has previously preferred. The meal kit suggestion unit also analyzes the user's health data to generate a meal kit according to the user's health condition. For example, if the user needs a specific nutrient, it provides a meal kit containing that nutrient. The meal kit suggestion unit also comprehensively analyzes the user's dietary history and health data to generate an individually optimized meal kit. For example, it provides a meal kit that is optimal for the user's weight management and health maintenance. This allows the system to generate an optimal meal kit based on the user's dietary history and health data, providing effective dietary management.

[0033] The meal kit suggestion unit can provide a customized meal kit based on the user's taste preferences and allergy information. The meal kit suggestion unit, for example, analyzes the user's taste preferences and provides a meal kit that matches the preferences. For example, if the user likes spicy food, a meal kit including spicy dishes is provided. The meal kit suggestion unit also provides a meal kit that avoids allergies based on the user's allergy information. For example, if the user has a nut allergy, a meal kit that does not contain nuts is provided. The meal kit suggestion unit also comprehensively analyzes the user's taste preferences and allergy information and provides a customized meal kit. For example, it provides an optimal meal kit that takes the user's taste preferences and allergies into consideration. This allows for providing a customized meal kit based on the user's taste preferences and allergy information, thereby providing effective dietary management.

[0034] The meal kit suggestion unit can have an AI disguised as a favorite idol provide a plan that supports not only the user's diet but also their overall lifestyle (exercise, sleep). For example, the meal kit suggestion unit can have an AI disguised as a favorite idol provide not only a meal plan but also an exercise plan for the user. For example, it can suggest a comprehensive diet plan that combines diet and exercise. The meal kit suggestion unit can also have an AI disguised as a favorite idol analyze the user's sleep data and provide advice for improving sleep. For example, it can suggest a health plan that combines diet and sleep. The meal kit suggestion unit can also have an AI disguised as a favorite idol provide a plan that supports the user's overall lifestyle. For example, it can suggest a plan that comprehensively manages diet, exercise, and sleep. This allows for the provision of a plan that supports the user's overall lifestyle and provides effective health management.

[0035] The meal kit suggestion unit can have AI disguised as the user's favorite idol suggest meal kits that can be enjoyed together with the user's family and friends. For example, the meal kit suggestion unit can have AI disguised as the user's favorite idol suggest meal kits that can be enjoyed together with the user's family and friends. For example, it can provide balanced meals that the whole family can enjoy. The meal kit suggestion unit can also have AI disguised as the user's favorite idol suggest meal kits that can be enjoyed together with the user's friends. For example, it can provide easy recipes that can be made together with friends. The meal kit suggestion unit can also have AI disguised as the user's favorite idol suggest meal kits that can be enjoyed together with the user's family and friends. For example, it can provide meals that are suitable for special events or parties. This can suggest meal kits that can be enjoyed together with the user's family and friends, and provide effective dietary management.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The diet support system can further include a sleep management unit that analyzes the user's sleep data and provides optimal sleep advice. For example, it can analyze the user's sleep patterns and suggest appropriate bedtimes and wake-up times. The sleep management unit can also provide advice on relaxation methods and environmental settings to improve the user's sleep quality. For example, it can suggest relaxing stretches to do before bed or how to adjust the temperature and lighting in the bedroom. This can improve the user's sleep quality and further increase the success rate of dieting.

[0038] The diet support system may further include a social support unit that supports the user's social activities. For example, the social support unit may suggest events where the user can enjoy exercise and meals with friends and family. The social support unit may also provide an online community where the user can interact with other diet buddies. For example, the social support unit may provide a forum or chat function where users can share their diet progress and encourage each other. This supports the user's social activities and helps maintain motivation for dieting.

[0039] The diet support system may further include a stress management unit that monitors the user's stress level and provides advice for stress reduction. For example, the system may analyze the user's heart rate and breathing patterns and suggest relaxation methods when stress levels are high. The stress management unit may also provide music or meditation guides to help the user relax. For example, when the user is feeling stressed, the system may play relaxing music or provide meditation guides. This reduces the user's stress and increases the success rate of dieting.

[0040] The diet support system can further include an exercise management unit that analyzes the user's exercise data and provides an individually optimized exercise plan. For example, it can analyze the user's exercise history and physical fitness level to propose an optimal exercise plan. The exercise management unit can also monitor the user's exercise progress and adjust the exercise plan as needed. For example, if the user achieves an exercise goal, it can set a new goal and update the exercise plan. This effectively supports the user's exercise and increases the success rate of dieting.

[0041] The diet support system can further include a nutrition management unit that analyzes the user's dietary data and evaluates the balance of the diet. For example, it can analyze the user's diet history and provide advice if the nutritional balance is unbalanced. The nutrition management unit can also suggest dietary improvements so that the user can consume the necessary nutrients. For example, if a specific nutrient is lacking, it can suggest ingredients and recipes that contain that nutrient. This can improve the balance of the user's diet and increase the success rate of dieting.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The encouragement module provides encouragement based on the user's behavior and situation. For example, if the user is about to slack off on exercise, it will offer words of encouragement such as "Do your best! You can do it!". Also, if the user is not managing their diet well, it will offer words of encouragement such as "Try a little harder!" Step 2: The stretching video provider provides the optimal stretching video to the user who has been encouraged by the encouragement provider. For example, when the user starts stretching, an AI disguised as their favorite idol will say, "Let's stretch together!" and play the video. The generation AI will recommend the optimal stretching video based on the user's feedback. Step 3: The meal kit suggestion unit suggests the optimal meal kit to the user based on the stretching video provided by the stretching video provision unit. For example, if the user has difficulty choosing a meal suitable for their diet, the AI ​​registered dietitian will suggest a meal kit tailored to the user's needs. The generation AI analyzes the user's dietary history and health condition to recommend the optimal meal kit.

[0044] (Example 2) The diet support system according to the embodiment of the present invention is a system in which a generation AI disguised as the user's "favorite" gives encouragement and encouragement to the user while dieting, thereby increasing the user's motivation. This enables the diet support system to increase the user's motivation and improve the success rate of dieting.

[0045] A diet support system according to an embodiment includes a motivational unit, a stretching video provider, and a meal kit suggestion unit. The motivational unit provides encouragement based on the user's behavior and situation. For example, when a user is about to slack off on exercise, the motivational unit offers words of encouragement such as, "Do your best! You can do it!" Furthermore, when a user's diet management is not going well, the motivational unit offers words of encouragement such as, "Try a little harder!" The stretching video provider provides the optimal stretching video to the user encouraged by the motivational unit. For example, when the user starts stretching, an AI disguised as a favorite idol may say, "Let's stretch together!" and play the video. The generation AI recommends the optimal stretching video based on user feedback. The meal kit suggestion unit suggests the optimal meal kit to the user based on the stretching video provided by the stretching video provider. For example, if a user has difficulty choosing a diet-friendly meal, an AI registered dietitian suggests a meal kit tailored to the user's needs. The generation AI analyzes the user's diet history and health condition to recommend the optimal meal kit. As a result, the diet support system according to the embodiment can provide appropriate support according to the user's behavior and situation, thereby increasing the success rate of dieting.

[0046] The encouragement unit can analyze the user's past behavioral data and generate the most effective encouraging message at a specific timing. For example, the encouragement unit analyzes the user's exercise history and food records and sends a message such as "Now is the time to work hard!" during times when the user tends to skip exercise. For example, if a user tends to skip exercise in the evening, an encouraging message is sent during that time. The encouragement unit also learns the user's behavioral patterns and sends encouraging messages such as "Try a little harder!" during times when it is difficult to manage one's diet. For example, if a user tends to snack late at night, a message urging caution is sent during that time. The encouragement unit also generates the most effective message for a specific day of the week or time of day based on the user's past behavioral data. For example, if a user tends to skip exercise on weekends, an encouraging message is sent especially on weekends. This allows messages to be generated at the optimal time based on the user's behavioral data, enabling effective encouragement.

[0047] The encouragement unit can monitor the user's emotional state in real time and generate an optimal message based on the emotion. The encouragement unit, for example, analyzes the user's facial expressions and voice to estimate the emotion in real time. For example, when the user is tired, it sends a message such as "Take a short rest and try again!". The encouragement unit also monitors the user's emotional state, and when the user has strong positive emotions, it sends an encouraging message such as "Keep it up!". For example, when the user feels a sense of accomplishment after exercising, it sends a message that further enhances that emotion. The encouragement unit also sends a gentle message such as "Don't push yourself too hard, but take it one step at a time" based on the user's emotional data when the user has strong negative emotions. For example, when diet management is not going well, it gives words of encouragement. This allows the system to generate an optimal message based on the user's emotional state and provide effective encouragement.

[0048] The encouragement unit can use the emotion estimation function to customize the character and language of the favorite character that evokes the most positive emotions in the user. For example, the encouragement unit analyzes the user's emotional response and selects the favorite character that elicits the most positive emotions. For example, if a specific character is the most encouraging to the user, that character is used. The encouragement unit also customizes the language that evokes the most positive emotions in the user based on the emotion estimation data. For example, if the user responds strongly to the words "Do your best!", that word is used frequently. The encouragement unit also adjusts the language and tone of the favorite character based on the user's emotion data. For example, if the user responds to a gentle tone, a message is generated in that tone. This allows the user to customize the character and language that evokes the most positive emotions in the user, thereby providing effective encouragement.

[0049] The stretching video providing unit can analyze the user's physical data and generate individually optimized stretching videos. For example, the stretching video providing unit analyzes the user's flexibility data and generates stretching videos according to the user's flexibility. For example, a user with low flexibility is provided with a stretching video for beginners. The stretching video providing unit also analyzes the user's muscle strength data and generates stretching videos according to the user's muscle strength. For example, a user with low muscle strength is provided with a stretching video with a light load. The stretching video providing unit also comprehensively analyzes the user's physical data and generates individually optimized stretching videos. For example, a stretching video that takes into account the balance between flexibility and muscle strength is provided. This makes it possible to generate optimal stretching videos based on the user's physical data and provide effective exercise.

[0050] The stretching video providing unit can adjust the content of the stretching video in real time based on user feedback to provide optimal exercise effects. The stretching video providing unit, for example, collects user feedback in real time and adjusts the content of the stretching video. For example, if the user provides feedback that "this movement is difficult," it makes the movement easier. The stretching video providing unit also adjusts the difficulty level of the stretching video based on user feedback. For example, if the user provides feedback that "I want to put more strain on myself," it increases the difficulty level. The stretching video providing unit also analyzes user feedback and adjusts the content of the stretching video to provide optimal exercise effects. For example, if the user provides feedback that "this part was effective," it strengthens that part. In this way, the content of the stretching video can be adjusted based on user feedback to provide optimal exercise effects.

[0051] The stretching video providing unit can use the emotion estimation function to customize the style and music of the stretching video that the user will enjoy most. The stretching video providing unit, for example, uses the emotion estimation function to customize the style of the stretching video that the user will enjoy most. For example, it provides a video in a style that the user finds relaxing. The stretching video providing unit also customizes the music that the user will enjoy most based on the user's emotion data. For example, it incorporates music that the user finds relaxing into the stretching video. The stretching video providing unit also customizes the style and music of the stretching video that the user will enjoy most based on the emotion estimation data. For example, it provides a video that combines a style and music that the user finds relaxing. This allows the style and music of the stretching video that the user will enjoy most to be customized and provide effective exercise.

[0052] The meal kit suggestion unit can analyze a user's dietary history and health data to generate an individually optimized meal kit. For example, the meal kit suggestion unit analyzes the user's dietary history and suggests an optimal meal kit based on the user's past eating patterns. For example, it provides a meal kit containing ingredients that the user has previously preferred. The meal kit suggestion unit also analyzes the user's health data to generate a meal kit according to the user's health condition. For example, if the user needs a specific nutrient, it provides a meal kit containing that nutrient. The meal kit suggestion unit also comprehensively analyzes the user's dietary history and health data to generate an individually optimized meal kit. For example, it provides a meal kit that is optimal for the user's weight management and health maintenance. This allows the system to generate an optimal meal kit based on the user's dietary history and health data, providing effective dietary management.

[0053] The meal kit suggestion unit can provide a customized meal kit based on the user's taste preferences and allergy information. The meal kit suggestion unit, for example, analyzes the user's taste preferences and provides a meal kit that matches the preferences. For example, if the user likes spicy food, a meal kit including spicy dishes is provided. The meal kit suggestion unit also provides a meal kit that avoids allergies based on the user's allergy information. For example, if the user has a nut allergy, a meal kit that does not contain nuts is provided. The meal kit suggestion unit also comprehensively analyzes the user's taste preferences and allergy information and provides a customized meal kit. For example, it provides an optimal meal kit that takes the user's taste preferences and allergies into consideration. This allows for providing a customized meal kit based on the user's taste preferences and allergy information, thereby providing effective dietary management.

[0054] The meal kit suggestion unit can use the emotion estimation function to select ingredients and recipes that will most satisfy the user and reflect them in the meal kit. The meal kit suggestion unit, for example, uses the emotion estimation function to select ingredients that will most satisfy the user. For example, if a user has positive emotions toward a particular ingredient, a meal kit containing that ingredient is provided. The meal kit suggestion unit also selects recipes that will most satisfy the user based on the user's emotion data. For example, if a user has positive emotions toward a particular dish, a meal kit containing that dish is provided. The meal kit suggestion unit also selects ingredients and recipes that will most satisfy the user based on the emotion estimation data and reflects them in the meal kit. For example, the meal kit suggestion unit analyzes the user's emotional response and provides a meal kit that combines optimal ingredients and recipes. This allows for effective dietary management by selecting ingredients and recipes that will most satisfy the user and reflecting them in the meal kit.

[0055] The meal kit suggestion unit can have an AI disguised as a favorite idol provide a plan that supports not only the user's diet but also their overall lifestyle (exercise, sleep). For example, the meal kit suggestion unit can have an AI disguised as a favorite idol provide not only a meal plan but also an exercise plan for the user. For example, it can suggest a comprehensive diet plan that combines diet and exercise. The meal kit suggestion unit can also have an AI disguised as a favorite idol analyze the user's sleep data and provide advice for improving sleep. For example, it can suggest a health plan that combines diet and sleep. The meal kit suggestion unit can also have an AI disguised as a favorite idol provide a plan that supports the user's overall lifestyle. For example, it can suggest a plan that comprehensively manages diet, exercise, and sleep. This allows for the provision of a plan that supports the user's overall lifestyle and provides effective health management.

[0056] The meal kit suggestion unit can have AI disguised as the user's favorite idol suggest meal kits that can be enjoyed together with the user's family and friends. For example, the meal kit suggestion unit can have AI disguised as the user's favorite idol suggest meal kits that can be enjoyed together with the user's family and friends. For example, it can provide balanced meals that the whole family can enjoy. The meal kit suggestion unit can also have AI disguised as the user's favorite idol suggest meal kits that can be enjoyed together with the user's friends. For example, it can provide easy recipes that can be made together with friends. The meal kit suggestion unit can also have AI disguised as the user's favorite idol suggest meal kits that can be enjoyed together with the user's family and friends. For example, it can provide meals that are suitable for special events or parties. This can suggest meal kits that can be enjoyed together with the user's family and friends, and provide effective dietary management.

[0057] The meal kit suggestion unit can use the emotion estimation function to suggest the meal scene that the user will enjoy most and provide a meal kit that matches that. The meal kit suggestion unit, for example, uses the emotion estimation function to suggest the meal scene that the user will enjoy most. For example, if the user has positive emotions toward a specific theme, a meal kit that matches that theme is provided. The meal kit suggestion unit also suggests a meal kit that matches a specific event based on the user's emotion data. For example, if the user has positive emotions toward a birthday or anniversary, a meal kit that matches that event is provided. The meal kit suggestion unit also suggests the meal scene that the user will enjoy most based on the emotion estimation data and provides a meal kit that matches that. For example, it analyzes the user's emotional response and provides a meal kit that matches the optimal theme or event. This makes it possible to suggest the meal scene that the user will enjoy most and provide a meal kit that matches that. This allows for effective dietary management.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The diet support system can further include a sleep management unit that analyzes the user's sleep data and provides optimal sleep advice. For example, it can analyze the user's sleep patterns and suggest appropriate bedtimes and wake-up times. The sleep management unit can also provide advice on relaxation methods and environmental settings to improve the user's sleep quality. For example, it can suggest relaxing stretches to do before bed or how to adjust the temperature and lighting in the bedroom. This can improve the user's sleep quality and further increase the success rate of dieting.

[0060] The diet support system may further include a social support unit that supports the user's social activities. For example, the social support unit may suggest events where the user can enjoy exercise and meals with friends and family. The social support unit may also provide an online community where the user can interact with other diet buddies. For example, the social support unit may provide a forum or chat function where users can share their diet progress and encourage each other. This supports the user's social activities and helps maintain motivation for dieting.

[0061] The diet support system may further include a stress management unit that monitors the user's stress level and provides advice for stress reduction. For example, the system may analyze the user's heart rate and breathing patterns and suggest relaxation methods when stress levels are high. The stress management unit may also provide music or meditation guides to help the user relax. For example, when the user is feeling stressed, the system may play relaxing music or provide meditation guides. This reduces the user's stress and increases the success rate of dieting.

[0062] The diet support system can further include an exercise management unit that analyzes the user's exercise data and provides an individually optimized exercise plan. For example, it can analyze the user's exercise history and physical fitness level to propose an optimal exercise plan. The exercise management unit can also monitor the user's exercise progress and adjust the exercise plan as needed. For example, if the user achieves an exercise goal, it can set a new goal and update the exercise plan. This effectively supports the user's exercise and increases the success rate of dieting.

[0063] The diet support system can further include a nutrition management unit that analyzes the user's dietary data and evaluates the balance of the diet. For example, it can analyze the user's diet history and provide advice if the nutritional balance is unbalanced. The nutrition management unit can also suggest dietary improvements so that the user can consume the necessary nutrients. For example, if a specific nutrient is lacking, it can suggest ingredients and recipes that contain that nutrient. This can improve the balance of the user's diet and increase the success rate of dieting.

[0064] The diet support system may further include an exercise adjustment unit that estimates the user's emotions and adjusts the difficulty of the exercise based on the estimated emotions. For example, when the user is tired, the system may suggest lowering the difficulty of the exercise. Also, when the user is feeling positive, the system may suggest increasing the difficulty of the exercise. This allows the system to adjust the difficulty of the exercise according to the user's emotional state and provide effective exercise.

[0065] The diet support system can further include a meal adjustment unit that estimates the user's emotions and suggests meals based on the estimated emotions. For example, when the user is feeling stressed, it can suggest relaxing ingredients and recipes. Also, when the user is feeling positive, it can suggest nutritious ingredients and recipes. This makes it possible to suggest meals according to the user's emotional state and provide effective diet management.

[0066] The diet support system can further include a stretching adjustment unit that estimates the user's emotions and adjusts the content of the stretching video based on the estimated emotions. For example, when the user is tired, a relaxing stretching video can be provided. On the other hand, when the user is feeling positive, a challenging stretching video can be provided. This allows the content of the stretching video to be adjusted according to the user's emotional state, providing effective exercise.

[0067] The diet support system can further include a meal kit adjustment unit that estimates the user's emotions and adjusts the contents of the meal kit based on the estimated emotions. For example, when the user is feeling stressed, a meal kit containing relaxing ingredients and recipes can be provided. Also, when the user is feeling positive, a meal kit containing nutritious ingredients and recipes can be provided. This allows the contents of the meal kit to be adjusted according to the user's emotional state, providing effective diet management.

[0068] The diet support system may further include an encouragement adjustment unit that estimates the user's emotions and adjusts the content of the encouragement based on the estimated emotions. For example, when the user is tired, gentle words of encouragement may be given. On the other hand, when the user is feeling positive, powerful words of encouragement may be given. This allows the content of the encouragement to be adjusted according to the user's emotional state, thereby providing effective motivation improvement.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The encouragement module provides encouragement based on the user's behavior and situation. For example, if the user is about to slack off on exercise, it will offer words of encouragement such as "Do your best! You can do it!". Also, if the user is not managing their diet well, it will offer words of encouragement such as "Try a little harder!" Step 2: The stretching video provider provides the optimal stretching video to the user who has been encouraged by the encouragement provider. For example, when the user starts stretching, an AI disguised as their favorite idol will say, "Let's stretch together!" and play the video. The generation AI will recommend the optimal stretching video based on the user's feedback. Step 3: The meal kit suggestion unit suggests the optimal meal kit to the user based on the stretching video provided by the stretching video provision unit. For example, if the user has difficulty choosing a meal suitable for their diet, the AI ​​registered dietitian will suggest a meal kit tailored to the user's needs. The generation AI analyzes the user's dietary history and health condition to recommend the optimal meal kit.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 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 motivational unit that gives motivational advice according to the user's behavior and situation; a stretching video providing unit that provides an optimal stretching video to the user who has been encouraged by the encouragement unit; a meal kit suggestion unit that suggests an optimal meal kit to a user based on the stretching video provided by the stretching video provision unit. A system characterized by:

2. The encouraging section Analyzing the user's past behavioral data and generating the most effective encouraging message at a specific time 2. The system of claim 1.

3. The encouraging section The emotional state of the user is monitored in real time, and an optimal message is generated according to the emotional state.

2. The system of claim 1.

4. The encouraging section Customize the character and language of the character that the user feels most positive about.

2. The system of claim 1.

5. The stretching video providing unit Analyzing the user's physical data and generating individually optimized stretching videos 2. The system of claim 1.

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