System

Abstract

An object of a system according to an embodiment is to propose an optimum meal substitute based on the preference and health condition of a user and to interpolate visual information and aroma information.SOLUTION: A system includes a proposal part, a visual interpolation part, and a fragrance interpolation part. The suggestion unit suggests an optimum meal substitute based on the preference and health condition of the user using the generated AI. The visual interpolation unit visually interpolates the meal substitute proposed by the proposal unit via the AR terminal. The scent interpolator interpolates the scent information of the meal substitute suggested by the suggester via the AR terminal.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 do not adequately suggest optimal meal replacements based on the user's preferences and health status, and do not adequately complement visual and aroma information, leaving room for improvement.

[0005] The system according to the embodiment aims to propose optimal substitute meals based on the user's preferences and health condition, and to complement visual and aroma information. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, a visual interpolation unit, and a scent interpolation unit. The suggestion unit uses a generation AI to suggest optimal substitute meals based on a user's preferences and health condition. The visual interpolation unit visually interpolates the substitute meals suggested by the suggestion unit via an AR terminal. The scent interpolation unit interpolates scent information for the substitute meals suggested by the suggestion unit via the AR terminal. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal meal replacements based on the user's preferences and health condition, and can interpolate visual and aroma information. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A dietary management system according to an embodiment of the present invention adjusts a user's sense of fullness and supports a balanced diet. This system utilizes cross-modal phenomena to transform the user's eating experience without changing the amount or flavor actually consumed. This allows the dietary management system to control calorie intake and reduce dieting stress while allowing the user to enjoy a satisfying meal.

[0029] A meal management system according to an embodiment includes a suggestion unit, a visual interpolation unit, and an aroma interpolation unit. The suggestion unit uses a generation AI to suggest optimal meal replacements based on a user's preferences and health status. For example, the generation AI analyzes the user's past meal history and health data to suggest optimal meal replacements. The generation AI can also analyze the user's real-time emotional data to suggest meal replacements that best suit the user's mood at that time. The visual interpolation unit visually interpolates the meal replacements suggested by the suggestion unit via an AR device. For example, the AR device can change visual information so that somen noodles appear to be thick ramen. The visual interpolation unit can also visually reproduce the texture and temperature sensation experienced during eating. The aroma interpolation unit interpolates aroma information for the meal replacements suggested by the suggestion unit via the AR device. For example, the aroma interpolation unit reproduces the aroma of thick ramen. The aroma interpolation unit can also collect olfactory data from the user and generate aroma information tailored to the user's individual preferences. This allows the meal management system according to an embodiment to suggest optimal meal replacements based on the user's preferences and health status, and provide satisfaction by interpolating visual and aroma information.

[0030] The suggestion unit can analyze the user's past meal history and real-time emotional data to suggest optimal meal replacements. For example, the suggestion unit stores the user's past meal history in a database and analyzes it in combination with real-time emotional data. For example, if the user is feeling stressed, the suggestion unit suggests meal replacements containing ingredients with a relaxing effect. The suggestion unit also uses the generation AI to identify meal patterns that have previously generated high satisfaction based on the user's meal history and emotional data, and suggests meal replacements based on those patterns. For example, the suggestion unit may use low-calorie ingredients that have been popular in the past. The suggestion unit also analyzes the user's emotional data in real time to suggest meal replacements that are optimal for the user's mood at that time. For example, if the user is tired, the suggestion unit suggests meal replacements containing ingredients that are suitable for replenishing energy. In this way, the user's satisfaction can be improved by analyzing the user's past meal history and real-time emotional data to suggest optimal meal replacements.

[0031] The visual interpolation unit can visually reproduce the texture and temperature sensation experienced during a meal. The visual interpolation unit visually reproduces the texture experienced during a meal, for example, using AR technology. For example, the visual interpolation unit changes the visual information so that somen noodles appear to be thick ramen. The visual interpolation unit also visually reproduces the temperature sensation experienced during a meal, for example, changing the visual information so that cold food appears warm. The visual interpolation unit also visually reproduces the optimal texture and temperature sensation in accordance with the user's preferences. For example, if the user prefers warm food, the visual information is changed to make the food appear warm. In this way, by visually reproducing the texture and temperature sensation experienced during a meal, a more realistic eating experience can be provided.

[0032] The scent interpolation unit can collect olfactory data of a user and generate scent information based on individual preferences. The scent interpolation unit, for example, collects olfactory data of a user and generates scent information tailored to individual preferences. For example, scent information during a meal is generated based on the scent preferred by the user. The scent interpolation unit also analyzes the user's olfactory data and generates scent information based on scents that have been well-received in the past. For example, the scent interpolation unit reproduces scents that have been well-received in the past. The scent interpolation unit also generates optimal scent information based on the user's olfactory data. For example, if the user is seeking a relaxing effect, a scent with a relaxing effect is generated. In this way, by collecting the user's olfactory data and generating scent information tailored to individual preferences, a more satisfying dining experience can be provided.

[0033] The suggestion unit can monitor the user's stress level in real time and make meal suggestions to reduce stress. For example, the suggestion unit monitors the user's stress level in real time and makes meal suggestions to reduce stress. For example, it suggests meals that include ingredients with a relaxing effect. Furthermore, the suggestion unit uses a generation AI to analyze the user's stress level, identify eating patterns that have had a stress-reducing effect in the past, and suggest meals based on that. For example, it uses ingredients with a relaxing effect that have been well-received in the past. Furthermore, the suggestion unit makes optimal meal suggestions based on the user's stress level. For example, if the user is feeling stressed, it suggests meals that include ingredients with a relaxing effect. In this way, the user's stress can be reduced by monitoring the user's stress level in real time and making meal suggestions to reduce stress.

[0034] The suggestion unit can analyze the user's psychological state and suggest ingredients and recipes that are effective in reducing stress. For example, the suggestion unit analyzes the user's psychological state and suggests ingredients that are effective in reducing stress. For example, it suggests recipes that use herbs and spices that have a relaxing effect. Furthermore, the suggestion unit identifies recipes that have had a stress-reducing effect in the past based on the user's psychological state using the generation AI, and makes suggestions based on those. For example, it uses recipes with a relaxing effect that have been well-received in the past. Furthermore, the suggestion unit analyzes the user's psychological state in real time and suggests optimal ingredients and recipes. For example, if the user is feeling stressed, it suggests recipes that use ingredients that have a relaxing effect. In this way, the user's stress can be reduced by analyzing the user's psychological state and suggesting ingredients and recipes that are effective in reducing stress.

[0035] The suggestion unit can adjust the lighting and music during meals according to the user's stress level, thereby providing a relaxing environment. The suggestion unit, for example, analyzes the user's stress level in real time and provides optimal lighting and music. For example, if the user is feeling stressed, it provides lighting and music with a relaxing effect. Furthermore, the suggestion unit uses an emotion estimation function to provide lighting and music that gives a sense of security when the user is feeling anxious. For example, it provides soft lighting and quiet music. Furthermore, the suggestion unit provides lighting and music that have had a relaxing effect in the past based on the user's stress level. For example, it reproduces lighting and music that have had a relaxing effect in the past. In this way, the lighting and music during meals can be adjusted according to the user's stress level, thereby providing a relaxing environment, thereby reducing the user's stress.

[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 suggestion unit can also optimize meal timing based on the user's eating history and health data. For example, if it has been found in the past that the user tends to feel fuller for longer by eating at a specific time of day, the suggestion unit can suggest eating at that time of day. The suggestion unit can also analyze the user's activity data and suggest optimal meal timing after exercise. For example, it can suggest a protein-rich meal within 30 minutes after exercise. The suggestion unit can also suggest meal timing to improve sleep quality based on the user's sleep data. For example, it can suggest an easily digestible light meal before bed. This can support the improvement of the user's health by optimizing the user's meal timing.

[0038] The visual interpolation unit can also visually recreate the dining environment. For example, it can recreate a natural landscape that helps the user relax using AR technology. The visual interpolation unit can also recreate the atmosphere of a restaurant that the user likes. For example, it can recreate the interior design and lighting of a high-end restaurant. The visual interpolation unit can also recreate memorable places that the user has visited in the past. For example, it can recreate the scenery of a travel destination or the atmosphere of a particular event. In this way, the dining environment can be visually recreated to further enrich the user's dining experience.

[0039] The suggestion unit can also optimize the nutritional balance of meals based on the user's dietary history and health data. For example, if the user is deficient in a particular nutrient, it can suggest ingredients that are rich in that nutrient. The suggestion unit can also analyze the user's allergy data and suggest safe ingredients. For example, it can suggest alternative ingredients that do not cause allergic reactions. The suggestion unit can also suggest meals that emphasize specific nutrients according to the user's health goals. For example, it can suggest meals that are high in protein to a user who is aiming to build muscle. This can support the improvement of the user's health by optimizing the nutritional balance of the user's meals.

[0040] The suggestion unit can also increase the variety of meals based on the user's meal history and health data. For example, it can suggest new dishes that the user has not tried before. The suggestion unit can also analyze the user's preference data and suggest new recipes that suit their tastes. For example, it can suggest new dishes using ingredients that the user likes. The suggestion unit can also suggest recipes using ingredients that are in season based on the user's meal history. For example, it can suggest dishes using seasonal ingredients. This can increase the variety of meals the user can have, thereby enriching their dining experience.

[0041] The suggestion unit can also optimize meal presentation based on the user's dietary history and health data. For example, it can suggest ways to plate food so that the user can enjoy it visually. The suggestion unit can also analyze the user's preference data and suggest presentations that suit the user's preferences. For example, it can suggest presentations that incorporate the user's favorite colors and shapes. The suggestion unit can also suggest presentations that are suited to special events or anniversaries based on the user's dietary history. For example, it can suggest presentations that are suitable for birthdays or anniversaries. This can enrich the dining experience by optimizing the user's meal presentation.

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

[0043] Step 1: The suggestion unit uses the generation AI to suggest optimal meal replacements based on the user's preferences and health status. For example, the generation AI may analyze the user's past eating history and health data to suggest optimal meal replacements. The generation AI may also analyze the user's real-time emotional data to suggest meal replacements that best suit their mood at that time. Step 2: The visual interpolation unit visually interpolates the substitute meal suggested by the suggestion unit via the AR device. For example, the visual information is changed so that somen noodles look like thick ramen through the AR device. The visual interpolation unit can also visually reproduce the texture and temperature sensation during the meal. Step 3: The aroma interpolation unit interpolates the aroma information of the substitute meal suggested by the suggestion unit via the AR device. For example, the aroma interpolation unit recreates the aroma of rich ramen. The aroma interpolation unit can also collect olfactory data from the user and generate aroma information tailored to individual preferences.

[0044] (Example 2) A dietary management system according to an embodiment of the present invention adjusts a user's sense of fullness and supports a balanced diet. This system utilizes cross-modal phenomena to transform the user's eating experience without changing the amount or flavor actually consumed. This allows the dietary management system to control calorie intake and reduce dieting stress while allowing the user to enjoy a satisfying meal.

[0045] A meal management system according to an embodiment includes a suggestion unit, a visual interpolation unit, and an aroma interpolation unit. The suggestion unit uses a generation AI to suggest optimal meal replacements based on a user's preferences and health status. For example, the generation AI analyzes the user's past meal history and health data to suggest optimal meal replacements. The generation AI can also analyze the user's real-time emotional data to suggest meal replacements that best suit the user's mood at that time. The visual interpolation unit visually interpolates the meal replacements suggested by the suggestion unit via an AR device. For example, the AR device can change visual information so that somen noodles appear to be thick ramen. The visual interpolation unit can also visually reproduce the texture and temperature sensation experienced during eating. The aroma interpolation unit interpolates aroma information for the meal replacements suggested by the suggestion unit via the AR device. For example, the aroma interpolation unit reproduces the aroma of thick ramen. The aroma interpolation unit can also collect olfactory data from the user and generate aroma information tailored to the user's individual preferences. This allows the meal management system according to an embodiment to suggest optimal meal replacements based on the user's preferences and health status, and provide satisfaction by interpolating visual and aroma information.

[0046] The suggestion unit can analyze the user's past meal history and real-time emotional data to suggest optimal meal replacements. For example, the suggestion unit stores the user's past meal history in a database and analyzes it in combination with real-time emotional data. For example, if the user is feeling stressed, the suggestion unit suggests meal replacements containing ingredients with a relaxing effect. The suggestion unit also uses the generation AI to identify meal patterns that have previously generated high satisfaction based on the user's meal history and emotional data, and suggests meal replacements based on those patterns. For example, the suggestion unit may use low-calorie ingredients that have been popular in the past. The suggestion unit also analyzes the user's emotional data in real time to suggest meal replacements that are optimal for the user's mood at that time. For example, if the user is tired, the suggestion unit suggests meal replacements containing ingredients that are suitable for replenishing energy. In this way, the user's satisfaction can be improved by analyzing the user's past meal history and real-time emotional data to suggest optimal meal replacements.

[0047] The visual interpolation unit can visually reproduce the texture and temperature sensation experienced during a meal. The visual interpolation unit visually reproduces the texture experienced during a meal, for example, using AR technology. For example, the visual interpolation unit changes the visual information so that somen noodles appear to be thick ramen. The visual interpolation unit also visually reproduces the temperature sensation experienced during a meal, for example, changing the visual information so that cold food appears warm. The visual interpolation unit also visually reproduces the optimal texture and temperature sensation in accordance with the user's preferences. For example, if the user prefers warm food, the visual information is changed to make the food appear warm. In this way, by visually reproducing the texture and temperature sensation experienced during a meal, a more realistic eating experience can be provided.

[0048] The scent interpolation unit can collect olfactory data of a user and generate scent information based on individual preferences. The scent interpolation unit, for example, collects olfactory data of a user and generates scent information tailored to individual preferences. For example, scent information during a meal is generated based on the scent preferred by the user. The scent interpolation unit also analyzes the user's olfactory data and generates scent information based on scents that have been well-received in the past. For example, the scent interpolation unit reproduces scents that have been well-received in the past. The scent interpolation unit also generates optimal scent information based on the user's olfactory data. For example, if the user is seeking a relaxing effect, a scent with a relaxing effect is generated. In this way, by collecting the user's olfactory data and generating scent information tailored to individual preferences, a more satisfying dining experience can be provided.

[0049] The suggestion unit can provide relaxation music and environmental sounds that correspond to the user's emotional state, thereby reducing stress during meals. The suggestion unit, for example, analyzes the user's emotional state in real time and provides relaxation music and environmental sounds. For example, if the user is feeling stressed, music with a relaxing effect is played. The suggestion unit also uses an emotion estimation function to provide environmental sounds that give a sense of security when the user is feeling anxious. For example, sounds of nature or quiet music is played. The suggestion unit also provides music and environmental sounds that have had a relaxing effect in the past based on the user's emotional data. For example, relaxing music that has been well-received in the past is reproduced. In this way, stress during meals can be reduced by providing relaxation music and environmental sounds that correspond to the user's emotional state.

[0050] The suggestion unit can monitor the user's stress level in real time and make meal suggestions to reduce stress. For example, the suggestion unit monitors the user's stress level in real time and makes meal suggestions to reduce stress. For example, it suggests meals that include ingredients with a relaxing effect. Furthermore, the suggestion unit uses a generation AI to analyze the user's stress level, identify eating patterns that have had a stress-reducing effect in the past, and suggest meals based on that. For example, it uses ingredients with a relaxing effect that have been well-received in the past. Furthermore, the suggestion unit makes optimal meal suggestions based on the user's stress level. For example, if the user is feeling stressed, it suggests meals that include ingredients with a relaxing effect. In this way, the user's stress can be reduced by monitoring the user's stress level in real time and making meal suggestions to reduce stress.

[0051] The suggestion unit can analyze the user's psychological state and suggest ingredients and recipes that are effective in reducing stress. For example, the suggestion unit analyzes the user's psychological state and suggests ingredients that are effective in reducing stress. For example, it suggests recipes that use herbs and spices that have a relaxing effect. Furthermore, the suggestion unit identifies recipes that have had a stress-reducing effect in the past based on the user's psychological state using the generation AI, and makes suggestions based on those. For example, it uses recipes with a relaxing effect that have been well-received in the past. Furthermore, the suggestion unit analyzes the user's psychological state in real time and suggests optimal ingredients and recipes. For example, if the user is feeling stressed, it suggests recipes that use ingredients that have a relaxing effect. In this way, the user's stress can be reduced by analyzing the user's psychological state and suggesting ingredients and recipes that are effective in reducing stress.

[0052] The suggestion unit can provide relaxation music and environmental sounds that correspond to the user's emotional state, thereby reducing stress during meals. The suggestion unit, for example, analyzes the user's emotional state in real time and provides relaxation music and environmental sounds. For example, if the user is feeling stressed, music with a relaxing effect is played. The suggestion unit also uses an emotion estimation function to provide environmental sounds that give a sense of security when the user is feeling anxious. For example, sounds of nature or quiet music is played. The suggestion unit also provides music and environmental sounds that have had a relaxing effect in the past based on the user's emotional data. For example, relaxing music that has been well-received in the past is reproduced. In this way, stress during meals can be reduced by providing relaxation music and environmental sounds that correspond to the user's emotional state.

[0053] The suggestion unit can adjust the lighting and music during meals according to the user's stress level, thereby providing a relaxing environment. The suggestion unit, for example, analyzes the user's stress level in real time and provides optimal lighting and music. For example, if the user is feeling stressed, it provides lighting and music with a relaxing effect. Furthermore, the suggestion unit uses an emotion estimation function to provide lighting and music that gives a sense of security when the user is feeling anxious. For example, it provides soft lighting and quiet music. Furthermore, the suggestion unit provides lighting and music that have had a relaxing effect in the past based on the user's stress level. For example, it reproduces lighting and music that have had a relaxing effect in the past. In this way, the lighting and music during meals can be adjusted according to the user's stress level, thereby providing a relaxing environment, thereby reducing the user's stress.

[0054] The suggestion unit can provide an optimal stress reduction method according to the user's emotional state. For example, if the user is feeling stressed, the suggestion unit provides a stress reduction method that has a relaxing effect. For example, by combining music or fragrances that have a relaxing effect. Furthermore, if the user is tired, the suggestion unit provides a stress reduction method that is suitable for replenishing energy. For example, by using ingredients that are suitable for replenishing energy and playing invigorating music. Furthermore, if the user is feeling anxious, the suggestion unit provides a stress reduction method that gives a sense of security. For example, by combining fragrances or visual information that give a sense of security. In this way, the user's stress can be reduced by providing an optimal stress reduction method according to the user's emotional state.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The suggestion unit can also optimize meal timing based on the user's eating history and health data. For example, if it has been found in the past that the user tends to feel fuller for longer by eating at a specific time of day, the suggestion unit can suggest eating at that time of day. The suggestion unit can also analyze the user's activity data and suggest optimal meal timing after exercise. For example, it can suggest a protein-rich meal within 30 minutes after exercise. The suggestion unit can also suggest meal timing to improve sleep quality based on the user's sleep data. For example, it can suggest an easily digestible light meal before bed. This can support the improvement of the user's health by optimizing the user's meal timing.

[0057] The suggestion unit can also suggest conversation topics during meals based on the user's emotional data. For example, if the user is feeling stressed, the suggestion unit can suggest topics that have a relaxing effect. Furthermore, if the user is feeling lonely, the suggestion unit can also suggest topics that will evoke empathy. For example, the suggestion unit can suggest topics related to common hobbies or interests. Furthermore, if the user is in a happy mood, the suggestion unit can also suggest topics that will make the meal even more enjoyable. For example, the suggestion unit can suggest topics that will provoke humor or laughter. In this way, by suggesting conversation topics that correspond to the user's emotional state, communication during meals can be made smoother and the user's satisfaction can be improved.

[0058] The visual interpolation unit can also visually recreate the dining environment. For example, it can recreate a natural landscape that helps the user relax using AR technology. The visual interpolation unit can also recreate the atmosphere of a restaurant that the user likes. For example, it can recreate the interior design and lighting of a high-end restaurant. The visual interpolation unit can also recreate memorable places that the user has visited in the past. For example, it can recreate the scenery of a travel destination or the atmosphere of a particular event. In this way, the dining environment can be visually recreated to further enrich the user's dining experience.

[0059] The scent interpolation unit can also dynamically change the scent during a meal based on the user's olfactory data. For example, by changing the scent as the meal progresses, a dining experience that never gets boring can be provided. The scent interpolation unit can also adjust the scent based on the user's emotional state. For example, it can emphasize scents that have a relaxing effect. The scent interpolation unit can also recreate scents that were popular in the past based on the user's olfactory data. For example, it can recreate the scent of a particular dish. In this way, by dynamically changing the scent based on the user's olfactory data, a more satisfying dining experience can be provided.

[0060] The suggestion unit can provide relaxation music and environmental sounds that correspond to the user's emotional state, thereby reducing stress during meals. For example, if the user is feeling stressed, music with a relaxing effect is played. Furthermore, the suggestion unit uses the emotion estimation function to provide environmental sounds that give a sense of security when the user is feeling anxious. For example, natural sounds or quiet music is played. Furthermore, the suggestion unit can provide music and environmental sounds that have had a relaxing effect in the past based on the user's emotional data. For example, it can reproduce relaxing music that has been well-received in the past. In this way, stress during meals can be reduced by providing relaxation music and environmental sounds that correspond to the user's emotional state.

[0061] The suggestion unit can also optimize the nutritional balance of meals based on the user's dietary history and health data. For example, if the user is deficient in a particular nutrient, it can suggest ingredients that are rich in that nutrient. The suggestion unit can also analyze the user's allergy data and suggest safe ingredients. For example, it can suggest alternative ingredients that do not cause allergic reactions. The suggestion unit can also suggest meals that emphasize specific nutrients according to the user's health goals. For example, it can suggest meals that are high in protein to a user who is aiming to build muscle. This can support the improvement of the user's health by optimizing the nutritional balance of the user's meals.

[0062] The suggestion unit can analyze the user's psychological state and suggest ingredients and recipes that are effective in reducing stress. For example, it can suggest recipes that use herbs and spices that have a relaxing effect. The suggestion unit also uses the generation AI to identify recipes that have had a stress-reducing effect in the past based on the user's psychological state and makes suggestions based on those. For example, it can use recipes with a relaxing effect that have been well-received in the past. The suggestion unit also analyzes the user's psychological state in real time and suggests optimal ingredients and recipes. For example, if the user is feeling stressed, it can suggest recipes that use ingredients that have a relaxing effect. In this way, the user's stress can be reduced by analyzing the user's psychological state and suggesting ingredients and recipes that are effective in reducing stress.

[0063] The suggestion unit can also increase the variety of meals based on the user's meal history and health data. For example, it can suggest new dishes that the user has not tried before. The suggestion unit can also analyze the user's preference data and suggest new recipes that suit their tastes. For example, it can suggest new dishes using ingredients that the user likes. The suggestion unit can also suggest recipes using ingredients that are in season based on the user's meal history. For example, it can suggest dishes using seasonal ingredients. This can increase the variety of meals the user can have, thereby enriching their dining experience.

[0064] The suggestion unit can adjust the lighting and music during meals according to the user's stress level, providing a relaxing environment. For example, the suggestion unit can analyze the user's stress level in real time and provide optimal lighting and music. For example, if the user is feeling stressed, the suggestion unit can provide lighting and music that gives a sense of security if the user is feeling anxious, using an emotion estimation function. For example, the suggestion unit can provide soft lighting and quiet music. The suggestion unit can also provide lighting and music that have had a relaxing effect in the past, based on the user's stress level. For example, the suggestion unit can reproduce lighting and music that have had a relaxing effect in the past. In this way, the lighting and music during meals can be adjusted according to the user's stress level, providing a relaxing environment, thereby reducing the user's stress.

[0065] The suggestion unit can also optimize meal presentation based on the user's dietary history and health data. For example, it can suggest ways to plate food so that the user can enjoy it visually. The suggestion unit can also analyze the user's preference data and suggest presentations that suit the user's preferences. For example, it can suggest presentations that incorporate the user's favorite colors and shapes. The suggestion unit can also suggest presentations that are suited to special events or anniversaries based on the user's dietary history. For example, it can suggest presentations that are suitable for birthdays or anniversaries. This can enrich the dining experience by optimizing the user's meal presentation.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The suggestion unit uses the generation AI to suggest optimal meal replacements based on the user's preferences and health status. For example, the generation AI may analyze the user's past eating history and health data to suggest optimal meal replacements. The generation AI may also analyze the user's real-time emotional data to suggest meal replacements that best suit their mood at that time. Step 2: The visual interpolation unit visually interpolates the substitute meal suggested by the suggestion unit via the AR device. For example, the visual information is changed so that somen noodles look like thick ramen through the AR device. The visual interpolation unit can also visually reproduce the texture and temperature sensation during the meal. Step 3: The aroma interpolation unit interpolates the aroma information of the substitute meal suggested by the suggestion unit via the AR device. For example, the aroma interpolation unit recreates the aroma of rich ramen. The aroma interpolation unit can also collect olfactory data from the user and generate aroma information tailored to individual preferences.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0081] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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]

[0135] 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 suggestion unit that uses a generative AI to suggest optimal substitute meals based on the user's preferences and health condition; a visual interpolation unit that visually interpolates the substitute meals suggested by the suggestion unit via the AR terminal; a scent interpolation unit that interpolates scent information of the substitute meal suggested by the suggestion unit via the AR terminal. A system characterized by:

2. The proposal unit Analyze the user's past meal history and real-time emotional data to suggest optimal meal replacements 2. The system of claim 1.

3. The visual interpolation unit Visually reproduce texture and temperature sensations during eating 2. The system of claim 1.

4. The scent interpolation unit Collecting olfactory data of the user and generating scent information based on individual preferences 2. The system of claim 1.

5. The proposal unit Provide relaxation music and environmental sounds according to the user's emotional state to reduce stress during meals 2. The system of claim 1.

Images