System

Abstract

An object of a system according to an embodiment is to provide an optimal taste stimulus according to a taste preference and a stress level of a user.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects information on a taste preference and a stress level of a user. The generation unit analyzes the information collected by the collection unit and generates a taste stimulus. The providing unit provides the taste stimulus generated by the generating unit to the tongue as an electric signal.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] With conventional technologies, it is difficult to provide optimal taste stimulation according to a user's taste preferences and stress level, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal taste stimulation according to the taste preferences and stress level of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects information on a user's taste preferences and stress level. The generation unit analyzes the information collected by the collection unit and generates a taste stimulus. The provision unit applies the taste stimulus generated by the generation unit to the tongue as an electrical signal. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal taste stimulation according to the taste preferences and stress level of the user. [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) A taste stimulus provision system according to an embodiment of the present invention collects information on a user's taste preferences and stress level, and a generation AI generates an optimal taste stimulus and delivers it to the tongue as an electrical signal. The taste stimulus provision system collects information on a user's taste preferences and stress level, and a generation AI analyzes this information to generate an optimal taste stimulus. The generated taste stimulus is delivered to the tongue as an electrical signal. For example, if a user desires sweetness, the taste stimulus provision system collects that information, and the generation AI generates an electrical signal corresponding to the sweetness and delivers it to the tongue. The taste stimulus provision system also takes tactile and visual information into account. If the user prefers a particular texture or appearance, the system learns that information and adjusts the taste stimulus accordingly. This allows the user to enjoy a taste without actually eating. Furthermore, the taste stimulus provision system is particularly beneficial for people experiencing stress or with dietary restrictions. For example, a person on a diet who desires sweetness can use this system to enjoy sweetness without consuming calories. This system is also effective for people with dietary restrictions who want to enjoy a specific flavor. As a result, the taste stimulus provision system can not only provide enjoyment of meals but also contribute to stress reduction. As a result, the taste stimulus provision system can provide optimal taste stimuli based on the user's taste preferences and stress level. For example, if a user desires sweetness, they can enjoy sweetness without ingesting calories. Also, if a person who is feeling stressed desires sweetness, they can use this system to reduce stress. This improves the quality of life of the user.

[0029] A taste stimulus providing system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects information on a user's taste preferences and stress level. Examples of the user's taste preferences include, but are not limited to, sweetness, sourness, saltiness, bitterness, and umami. For example, if the user desires sweetness, the collection unit collects such information. The collection unit can also collect the user's stress level. For example, heart rate, electrodermal activity, and survey results are used to collect the stress level. The generation unit uses a generation AI to analyze the information collected by the collection unit and generate taste stimuli. The generated taste stimuli are generated as electrical signals with specific frequencies and intensities. For example, if the user desires sweetness, the generation AI generates an electrical signal corresponding to the sweetness and applies it to the tongue. The generation unit can also adjust the taste stimuli taking tactile and visual information into consideration. For example, if the user prefers a specific texture or appearance, the generation AI learns that information and adjusts the taste stimuli accordingly. The providing unit provides the taste stimulus generated by the generating unit to the tongue as an electrical signal. The providing unit provides the electrical signal to the tongue using, for example, electrodes. The providing unit can also adjust the taste stimulus based on the user's preferences. For example, if the user desires a stronger sweetness, the providing unit adjusts the intensity of the electrical signal. This allows the taste stimulus providing system according to the embodiment to provide an optimal taste stimulus based on the user's taste preferences and stress level.

[0030] The collection unit can analyze the user's past taste preferences and stress level history and select a collection method. For example, the collection unit predicts and collects the user's current taste preferences based on the user's past favorite tastes. The collection unit can also analyze the user's past fluctuations in stress level and determine the optimal collection timing. Furthermore, the collection unit can analyze the relationship between the user's past taste preferences and stress level and customize the collection method. This makes it possible to select the optimal collection method based on the past history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past taste preference data into the generation AI and have the generation AI select the optimal collection method.

[0031] The collection unit can filter the taste preferences and stress levels based on the user's current lifestyle and health condition when collecting them. The collection unit can adjust the collection of taste preferences based on, for example, the results of a user health check. The collection unit can also determine the optimal collection timing based on the user's current lifestyle. Furthermore, the collection unit can limit the collection of specific taste preferences based on the user's health condition. This enables information collection tailored to the user's lifestyle and health condition. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input the user's health check data into the generation AI and have the generation AI select the optimal collection method.

[0032] When collecting taste preferences and stress levels, the collection unit can select a collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects taste preferences using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect taste preferences using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect taste preferences using image analysis technology. This enables optimal information collection depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's voice data into a generation AI and have the generation AI collect taste preferences.

[0033] When collecting taste preferences and stress levels, the collection unit can prioritize collection of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit collects taste preferences based on the food culture of that area. Also, if the user is traveling, the collection unit can collect taste preferences based on the food culture of the travel destination. Furthermore, if the user is at home, the collection unit can collect information based on past taste preferences at home. This makes it possible to collect information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal collection method.

[0034] The collection unit can analyze the user's social media activities and collect related information when collecting taste preferences and stress levels. For example, the collection unit can analyze photos of food shared by the user on social media to collect taste preferences. The collection unit can also analyze the content of the user's social media posts to estimate the user's stress level. Furthermore, the collection unit can collect related taste preferences by referring to the activities of the user's friends on social media. This makes it possible to collect information based on the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect taste preferences and stress levels.

[0035] The collection unit can customize the collection method by reflecting the user's past feedback when collecting taste preferences and stress levels. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also prioritize the collection of specific taste preferences based on the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and optimize the collection method. This makes it possible to collect information based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0036] When generating taste stimuli, the generation unit can adjust the level of detail of the generated taste stimuli based on the user's taste preferences and stress level. For example, if the user prefers sweetness, the generation unit can generate taste stimuli with increased detail for sweetness. Furthermore, if the user is feeling stressed, the generation unit can generate taste stimuli with increased detail that are effective for stress reduction. Furthermore, if the user prefers a particular taste, the generation unit can adjust the level of detail for that taste when generating the taste stimuli. This makes it possible to generate detailed taste stimuli according to the user's preferences and stress level. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's taste preference data into the generation AI and cause the generation AI to adjust the level of detail of the taste stimuli.

[0037] When generating taste stimuli, the generation unit can apply a generation algorithm that takes into account the user's health condition and allergy information. For example, if the user has an allergy, the generation unit generates taste stimuli that do not contain the allergen. The generation unit can also generate health-conscious taste stimuli depending on the user's health condition. Furthermore, the generation unit can generate optimal taste stimuli based on the user's health checkup results. This makes it possible to generate taste stimuli that take into account the user's health condition and allergy information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health checkup data into the generation AI and cause the generation AI to generate taste stimuli.

[0038] When generating taste stimuli, the generation unit can improve the accuracy of generation by referring to the user's past taste stimulation results. The generation unit improves the accuracy of generation, for example, based on taste stimuli that the user has previously preferred. The generation unit can also analyze the user's past taste stimulation results and generate optimal taste stimuli. Furthermore, the generation unit can improve the generation algorithm by referring to the user's past feedback. This makes it possible to generate highly accurate taste stimuli by referring to past results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past taste stimulation data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0039] When generating gustatory stimuli, the generation unit can determine the priority of generation based on the user's dietary history. The generation unit can determine the priority of the next gustatory stimuli to be provided based on, for example, the user's most recent meal. The generation unit can also prioritize the generation of specific tastes based on the user's dietary history. Furthermore, the generation unit can analyze the user's dietary history and determine the optimal order in which gustatory stimuli are generated. This makes it possible to generate gustatory stimuli prioritized based on the user's dietary history. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's dietary history data into the generation AI and have the generation AI determine the priority of the gustatory stimuli.

[0040] When generating taste stimuli, the generation unit can adjust the order of generation based on the user's preferences. For example, the generation unit prioritizes generation of tastes preferred by the user. The generation unit can also adjust the order of generation of taste stimuli based on the user's preferences. Furthermore, the generation unit can generate taste stimuli in an optimal order by referring to the user's past preferences. This makes it possible to generate taste stimuli in an order based on the user's preferences. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user preference data to the generation AI and cause the generation AI to adjust the order of generation of taste stimuli.

[0041] When generating taste stimuli, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate taste stimuli using technical terminology. Also, if the user does not have technical expertise, the generation unit can generate taste stimuli using simple terminology. Furthermore, the generation unit can adjust the explanation of the generation algorithm according to the user's level of expertise. This enables the use of appropriate terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0042] When providing a taste stimulus, the providing unit can select the optimal delivery method by referring to the user's past taste stimulus history. For example, the providing unit selects the optimal method based on the delivery method that the user has previously preferred. The providing unit can also analyze the user's past taste stimulus history and select the optimal delivery method. Furthermore, the providing unit can also improve the delivery method by referring to the user's past feedback. This makes it possible to select the optimal delivery method based on the user's past history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past taste stimulus data into the generating AI and cause the generating AI to select the optimal delivery method.

[0043] The providing unit can customize the providing means based on the user's current living situation when providing the taste stimuli. For example, if the user is busy, the providing unit can provide the taste stimuli quickly. Also, if the user is relaxed, the providing unit can provide the taste stimuli in a relaxed manner. Furthermore, the providing unit can select the optimal providing means according to the user's lifestyle rhythm. This makes it possible to customize the providing means according to the user's lifestyle situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's lifestyle rhythm data into the generating AI and cause the generating AI to customize the providing means.

[0044] The providing unit can improve the providing method by reflecting user feedback when providing a taste stimulus. For example, if the user provides feedback on the provided taste stimulus, the providing unit improves the providing method based on the feedback. The providing unit can also analyze the user's past feedback and optimize the providing method. Furthermore, the providing unit can also reflect the user's feedback in real time and adjust the providing method. This makes it possible to improve the providing method based on the user's feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0045] When providing taste stimuli, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is in a specific region, the providing unit can provide taste stimuli based on the food culture of that region. Furthermore, if the user is traveling, the providing unit can also provide taste stimuli based on the food culture of the travel destination. Furthermore, if the user is at home, the providing unit can also provide taste stimuli based on the user's past taste preferences at home. This makes it possible to select the optimal delivery method based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location data to the generation AI and cause the generation AI to select the optimal delivery method.

[0046] When providing taste stimuli, the providing unit can analyze the user's social media activity and suggest a delivery method. For example, the providing unit can analyze photos of food shared by the user on social media and suggest the optimal delivery method. The providing unit can also analyze the content of the user's social media posts and provide related taste stimuli. Furthermore, the providing unit can provide related taste stimuli by referring to the activities of the user's friends on social media. This makes it possible to suggest delivery methods based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest the optimal delivery method.

[0047] The providing unit can customize the delivery method by reflecting the user's past feedback when providing a taste stimulus. The providing unit customizes the delivery method based on, for example, feedback provided by the user in the past. The providing unit can also adjust the delivery method of a specific taste stimulus based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and optimize the delivery method. This makes it possible to customize the delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the delivery method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The taste stimulus provision system further collects the user's exercise data, and the generation AI can generate the optimal taste stimulus after exercise. For example, if the user wants to feel refreshed after exercise, the collection unit collects the user's exercise data, and the generation unit generates a refreshing taste stimulus based on that data. Alternatively, if the user wants to relax after exercise, the collection unit collects the user's exercise data, and the generation unit generates a gentle taste stimulus based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimulus to suit the user's physical condition after exercise. This allows the user to have the optimal taste experience after exercise.

[0050] The taste stimulus provision system also collects the user's reading data, allowing the generation AI to generate optimal taste stimuli while reading. For example, if the user wants to relax and enjoy reading, the collection unit collects the user's reading data, and the generation unit generates gentle taste stimuli based on that data. Alternatively, if the user is reading a thrilling novel, the collection unit can collect the user's reading data, and the generation unit can generate stimulating taste stimuli based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimuli according to the reading progress. This allows the user to enjoy the synchronization of reading and taste.

[0051] The taste stimulus provision system can further collect the user's travel data, and the generation AI can generate taste stimuli based on the food culture of the travel destination. For example, if the user is traveling to a particular country, the collection unit collects data based on the food culture of that country, and the generation unit generates taste stimuli that recreate the local taste based on that data. Also, if the user wants to enjoy a particular dish during their trip, the collection unit collects data about that dish, and the generation unit can generate taste stimuli that recreate the taste of that dish based on that data. Furthermore, the provision unit can adjust the timing of providing taste stimuli to match the travel schedule. This allows the user to enjoy local flavors during their trip.

[0052] The taste stimulus provision system further collects the user's driving data, and the generation AI can generate optimal taste stimuli while driving. For example, if the user is tired after a long drive, the collection unit collects the user's driving data, and the generation unit generates taste stimuli with a refreshing effect based on that data. Also, if the user wants to concentrate while driving, the collection unit collects the user's driving data, and the generation unit can generate taste stimuli that increase concentration based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimuli according to the driving situation. This allows the user to have an optimal taste experience while driving.

[0053] The taste stimulus provision system can further collect user hobby data, and the generation AI can generate taste stimuli based on the hobby activity. For example, if the user is drawing, the collection unit collects the user's hobby data, and the generation unit generates taste stimuli that enhance creativity based on that data. Also, if the user is gardening, the collection unit collects the user's hobby data, and the generation unit can generate taste stimuli that have a relaxing effect based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimuli according to the progress of the hobby activity. This allows the user to have a taste experience while enjoying their hobby activity.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects information on the user's taste preferences and stress level. The user's taste preferences include sweetness, sourness, saltiness, bitterness, umami, etc. For example, if the user desires sweetness, that information is collected. The collection unit can also collect the user's stress level. Stress levels are collected using heart rate, electrodermal activity, questionnaire results, etc. Step 2: The generation unit uses the generation AI to analyze the information collected by the collection unit and generate taste stimuli. The generated taste stimuli are generated as electrical signals with specific frequencies and strengths. For example, if the user desires sweetness, the generation AI generates an electrical signal corresponding to the sweetness and delivers it to the tongue. The generation unit can also adjust the taste stimuli taking into account tactile and visual information. Step 3: The providing unit applies the taste stimulus generated by the generating unit to the tongue as an electrical signal. The providing unit applies the electrical signal to the tongue using, for example, electrodes. The providing unit can also adjust the taste stimulus based on the user's preferences. For example, if the user wants to experience a stronger sweetness, the providing unit adjusts the intensity of the electrical signal.

[0056] (Example 2) A taste stimulus provision system according to an embodiment of the present invention collects information on a user's taste preferences and stress level, and a generation AI generates an optimal taste stimulus and delivers it to the tongue as an electrical signal. The taste stimulus provision system collects information on a user's taste preferences and stress level, and a generation AI analyzes this information to generate an optimal taste stimulus. The generated taste stimulus is delivered to the tongue as an electrical signal. For example, if a user desires sweetness, the taste stimulus provision system collects that information, and the generation AI generates an electrical signal corresponding to the sweetness and delivers it to the tongue. The taste stimulus provision system also takes tactile and visual information into account. If the user prefers a particular texture or appearance, the system learns that information and adjusts the taste stimulus accordingly. This allows the user to enjoy a taste without actually eating. Furthermore, the taste stimulus provision system is particularly beneficial for people experiencing stress or with dietary restrictions. For example, a person on a diet who desires sweetness can use this system to enjoy sweetness without consuming calories. This system is also effective for people with dietary restrictions who want to enjoy a specific flavor. As a result, the taste stimulus provision system can not only provide enjoyment of meals but also contribute to stress reduction. As a result, the taste stimulus provision system can provide optimal taste stimuli based on the user's taste preferences and stress level. For example, if a user desires sweetness, they can enjoy sweetness without ingesting calories. Also, if a person who is feeling stressed desires sweetness, they can use this system to reduce stress. This improves the quality of life of the user.

[0057] A taste stimulus providing system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects information on a user's taste preferences and stress level. Examples of the user's taste preferences include, but are not limited to, sweetness, sourness, saltiness, bitterness, and umami. For example, if the user desires sweetness, the collection unit collects such information. The collection unit can also collect the user's stress level. For example, heart rate, electrodermal activity, and survey results are used to collect the stress level. The generation unit uses a generation AI to analyze the information collected by the collection unit and generate taste stimuli. The generated taste stimuli are generated as electrical signals with specific frequencies and intensities. For example, if the user desires sweetness, the generation AI generates an electrical signal corresponding to the sweetness and applies it to the tongue. The generation unit can also adjust the taste stimuli taking tactile and visual information into consideration. For example, if the user prefers a specific texture or appearance, the generation AI learns that information and adjusts the taste stimuli accordingly. The providing unit provides the taste stimulus generated by the generating unit to the tongue as an electrical signal. The providing unit provides the electrical signal to the tongue using, for example, electrodes. The providing unit can also adjust the taste stimulus based on the user's preferences. For example, if the user desires a stronger sweetness, the providing unit adjusts the intensity of the electrical signal. This allows the taste stimulus providing system according to the embodiment to provide an optimal taste stimulus based on the user's taste preferences and stress level.

[0058] The collection unit can estimate the user's emotions and adjust the timing of collecting taste preferences and stress levels based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects taste preference information at times when the stress level increases. Furthermore, if the user is relaxed, the collection unit can also collect taste preference information at regular intervals. Furthermore, if the user is in a hurry, the collection unit can also collect taste preference information in a short period of time. This allows information to be collected at the optimal timing depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0059] The collection unit can analyze the user's past taste preferences and stress level history and select a collection method. For example, the collection unit predicts and collects the user's current taste preferences based on the user's past favorite tastes. The collection unit can also analyze the user's past fluctuations in stress level and determine the optimal collection timing. Furthermore, the collection unit can analyze the relationship between the user's past taste preferences and stress level and customize the collection method. This makes it possible to select the optimal collection method based on the past history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past taste preference data into the generation AI and have the generation AI select the optimal collection method.

[0060] The collection unit can filter the taste preferences and stress levels based on the user's current lifestyle and health condition when collecting them. The collection unit can adjust the collection of taste preferences based on, for example, the results of a user health check. The collection unit can also determine the optimal collection timing based on the user's current lifestyle. Furthermore, the collection unit can limit the collection of specific taste preferences based on the user's health condition. This enables information collection tailored to the user's lifestyle and health condition. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input the user's health check data into the generation AI and have the generation AI select the optimal collection method.

[0061] When collecting taste preferences and stress levels, the collection unit can select a collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects taste preferences using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect taste preferences using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect taste preferences using image analysis technology. This enables optimal information collection depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's voice data into a generation AI and have the generation AI collect taste preferences.

[0062] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information about the user's stress level. Furthermore, if the user is relaxed, the collection unit can prioritize collecting information about the user's taste preferences. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting information that can be collected in a short time. This allows the priority of information to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0063] When collecting taste preferences and stress levels, the collection unit can prioritize collection of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit collects taste preferences based on the food culture of that area. Also, if the user is traveling, the collection unit can collect taste preferences based on the food culture of the travel destination. Furthermore, if the user is at home, the collection unit can collect information based on past taste preferences at home. This makes it possible to collect information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal collection method.

[0064] The collection unit can analyze the user's social media activities and collect related information when collecting taste preferences and stress levels. For example, the collection unit can analyze photos of food shared by the user on social media to collect taste preferences. The collection unit can also analyze the content of the user's social media posts to estimate the user's stress level. Furthermore, the collection unit can collect related taste preferences by referring to the activities of the user's friends on social media. This makes it possible to collect information based on the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect taste preferences and stress levels.

[0065] The collection unit can customize the collection method by reflecting the user's past feedback when collecting taste preferences and stress levels. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also prioritize the collection of specific taste preferences based on the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and optimize the collection method. This makes it possible to collect information based on the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0066] The generation unit can estimate the user's emotion and adjust the method for generating taste stimuli based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a gentle taste stimulus. Furthermore, when the user is stressed, the generation unit can generate a taste stimulus that is effective in reducing stress. Furthermore, when the user is excited, the generation unit can generate a stimulating taste stimulus. This enables the generation of taste stimuli according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0067] When generating taste stimuli, the generation unit can adjust the level of detail of the generated taste stimuli based on the user's taste preferences and stress level. For example, if the user prefers sweetness, the generation unit can generate taste stimuli with increased detail for sweetness. Furthermore, if the user is feeling stressed, the generation unit can generate taste stimuli with increased detail that are effective for stress reduction. Furthermore, if the user prefers a particular taste, the generation unit can adjust the level of detail for that taste when generating the taste stimuli. This makes it possible to generate detailed taste stimuli according to the user's preferences and stress level. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's taste preference data into the generation AI and cause the generation AI to adjust the level of detail of the taste stimuli.

[0068] When generating taste stimuli, the generation unit can apply a generation algorithm that takes into account the user's health condition and allergy information. For example, if the user has an allergy, the generation unit generates taste stimuli that do not contain the allergen. The generation unit can also generate health-conscious taste stimuli depending on the user's health condition. Furthermore, the generation unit can generate optimal taste stimuli based on the user's health checkup results. This makes it possible to generate taste stimuli that take into account the user's health condition and allergy information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's health checkup data into the generation AI and cause the generation AI to generate taste stimuli.

[0069] When generating taste stimuli, the generation unit can improve the accuracy of generation by referring to the user's past taste stimulation results. The generation unit improves the accuracy of generation, for example, based on taste stimuli that the user has previously preferred. The generation unit can also analyze the user's past taste stimulation results and generate optimal taste stimuli. Furthermore, the generation unit can improve the generation algorithm by referring to the user's past feedback. This makes it possible to generate highly accurate taste stimuli by referring to past results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past taste stimulation data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0070] The generation unit can estimate the user's emotion and adjust the intensity of the gustatory stimulus based on the estimated user's emotion. For example, when the user is relaxed, the generation unit can generate a gustatory stimulus with a moderate intensity. Furthermore, when the user is stressed, the generation unit can generate a gustatory stimulus with a stronger intensity. Furthermore, when the user is excited, the generation unit can generate a gustatory stimulus with a stimulating intensity. This enables the generation of gustatory stimuli with an intensity corresponding to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0071] When generating gustatory stimuli, the generation unit can determine the priority of generation based on the user's dietary history. The generation unit can determine the priority of the next gustatory stimuli to be provided based on, for example, the user's most recent meal. The generation unit can also prioritize the generation of specific tastes based on the user's dietary history. Furthermore, the generation unit can analyze the user's dietary history and determine the optimal order in which gustatory stimuli are generated. This makes it possible to generate gustatory stimuli prioritized based on the user's dietary history. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's dietary history data into the generation AI and have the generation AI determine the priority of the gustatory stimuli.

[0072] When generating taste stimuli, the generation unit can adjust the order of generation based on the user's preferences. For example, the generation unit prioritizes generation of tastes preferred by the user. The generation unit can also adjust the order of generation of taste stimuli based on the user's preferences. Furthermore, the generation unit can generate taste stimuli in an optimal order by referring to the user's past preferences. This makes it possible to generate taste stimuli in an order based on the user's preferences. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user preference data to the generation AI and cause the generation AI to adjust the order of generation of taste stimuli.

[0073] When generating taste stimuli, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate taste stimuli using technical terminology. Also, if the user does not have technical expertise, the generation unit can generate taste stimuli using simple terminology. Furthermore, the generation unit can adjust the explanation of the generation algorithm according to the user's level of expertise. This enables the use of appropriate terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0074] The providing unit can estimate the user's emotion and adjust the method of providing the taste stimuli based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can provide the taste stimuli in a gentle manner. Furthermore, if the user is stressed, the providing unit can also provide the taste stimuli quickly. Furthermore, if the user is excited, the providing unit can also provide the taste stimuli in a stimulating manner. This makes it possible to adjust the method of providing the taste stimuli according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0075] When providing a taste stimulus, the providing unit can select the optimal delivery method by referring to the user's past taste stimulus history. For example, the providing unit selects the optimal method based on the delivery method that the user has previously preferred. The providing unit can also analyze the user's past taste stimulus history and select the optimal delivery method. Furthermore, the providing unit can also improve the delivery method by referring to the user's past feedback. This makes it possible to select the optimal delivery method based on the user's past history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past taste stimulus data into the generating AI and cause the generating AI to select the optimal delivery method.

[0076] The providing unit can customize the providing means based on the user's current living situation when providing the taste stimuli. For example, if the user is busy, the providing unit can provide the taste stimuli quickly. Also, if the user is relaxed, the providing unit can provide the taste stimuli in a relaxed manner. Furthermore, the providing unit can select the optimal providing means according to the user's lifestyle rhythm. This makes it possible to customize the providing means according to the user's lifestyle situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's lifestyle rhythm data into the generating AI and cause the generating AI to customize the providing means.

[0077] The providing unit can improve the providing method by reflecting user feedback when providing a taste stimulus. For example, if the user provides feedback on the provided taste stimulus, the providing unit improves the providing method based on the feedback. The providing unit can also analyze the user's past feedback and optimize the providing method. Furthermore, the providing unit can also reflect the user's feedback in real time and adjust the providing method. This makes it possible to improve the providing method based on the user's feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0078] The providing unit can estimate the user's emotion and adjust the timing of providing the taste stimuli based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide the taste stimuli at a gentle timing. Furthermore, when the user is stressed, the providing unit can also provide the taste stimuli quickly. Furthermore, when the user is excited, the providing unit can also provide the taste stimuli at a stimulating timing. This makes it possible to adjust the timing of providing the taste stimuli according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] When providing taste stimuli, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is in a specific region, the providing unit can provide taste stimuli based on the food culture of that region. Furthermore, if the user is traveling, the providing unit can also provide taste stimuli based on the food culture of the travel destination. Furthermore, if the user is at home, the providing unit can also provide taste stimuli based on the user's past taste preferences at home. This makes it possible to select the optimal delivery method based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location data to the generation AI and cause the generation AI to select the optimal delivery method.

[0080] When providing taste stimuli, the providing unit can analyze the user's social media activity and suggest a delivery method. For example, the providing unit can analyze photos of food shared by the user on social media and suggest the optimal delivery method. The providing unit can also analyze the content of the user's social media posts and provide related taste stimuli. Furthermore, the providing unit can provide related taste stimuli by referring to the activities of the user's friends on social media. This makes it possible to suggest delivery methods based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest the optimal delivery method.

[0081] The providing unit can customize the delivery method by reflecting the user's past feedback when providing a taste stimulus. The providing unit customizes the delivery method based on, for example, feedback provided by the user in the past. The providing unit can also adjust the delivery method of a specific taste stimulus based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and optimize the delivery method. This makes it possible to customize the delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on the user's taste preferences and stress level using the camera 42 and microphone 38B of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate taste stimuli. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and applies the generated taste stimuli to the tongue as electrical signals. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information on the user's taste preferences and stress level using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate taste stimuli. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated taste stimuli to the tongue as electrical signals. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects information on the user's taste preferences and stress level using the camera 42 and microphone 238 of the headset terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate taste stimuli. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314, and applies the generated taste stimuli to the tongue as electrical signals. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on the user's taste preferences and stress level using the camera 42 and microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to generate taste stimuli. The provision unit is realized, for example, by the control unit 46A of the robot 414, and applies the generated taste stimuli to the tongue as electrical signals.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The taste stimulus provision system can also collect the user's music preferences, and the generation AI can generate taste stimuli linked to the music. For example, if the user wants to relax, the collection unit collects the user's relaxing music playlist, and the generation unit generates gentle taste stimuli that match the music. Alternatively, if the user wants to feel energized, the collection unit can collect the user's up-tempo music playlist, and the generation unit can generate stimulating taste stimuli that match the music. Furthermore, the provision unit can adjust the intensity of the taste stimuli to match the rhythm of the music. This allows the user to enjoy the synchronization of music and taste.

[0084] The taste stimulus provision system further collects the user's exercise data, and the generation AI can generate the optimal taste stimulus after exercise. For example, if the user wants to feel refreshed after exercise, the collection unit collects the user's exercise data, and the generation unit generates a refreshing taste stimulus based on that data. Alternatively, if the user wants to relax after exercise, the collection unit collects the user's exercise data, and the generation unit generates a gentle taste stimulus based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimulus to suit the user's physical condition after exercise. This allows the user to have the optimal taste experience after exercise.

[0085] The taste stimulus provision system can further collect the user's sleep data, and the generation AI can generate taste stimuli to improve the quality of sleep. For example, if the user wants to relax before going to bed, the collection unit can collect the user's sleep data, and the generation unit can generate gentle taste stimuli based on that data. Alternatively, if the user wants to feel refreshed after waking up in the morning, the collection unit can collect the user's sleep data, and the generation unit can generate refreshing taste stimuli based on that data. Furthermore, the provision unit can adjust the timing of providing taste stimuli to match the user's sleep cycle. This allows the user to have a taste experience that improves the quality of their sleep.

[0086] The taste stimulus provision system also collects the user's reading data, allowing the generation AI to generate optimal taste stimuli while reading. For example, if the user wants to relax and enjoy reading, the collection unit collects the user's reading data, and the generation unit generates gentle taste stimuli based on that data. Alternatively, if the user is reading a thrilling novel, the collection unit can collect the user's reading data, and the generation unit can generate stimulating taste stimuli based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimuli according to the reading progress. This allows the user to enjoy the synchronization of reading and taste.

[0087] The taste stimulus provision system can further collect the user's movie viewing data, and the generation AI can generate taste stimuli that match the movie scene. For example, if the user is watching an emotional scene, the collection unit collects the user's movie viewing data, and the generation unit generates a sweet taste stimulus based on that data. Also, if the user is watching an action scene, the collection unit collects the user's movie viewing data, and the generation unit can generate a stimulating taste stimulus based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimulus to match the movie scene. This allows the user to enjoy the synchronization between the movie and their taste buds.

[0088] The taste stimulus provision system can further collect the user's travel data, and the generation AI can generate taste stimuli based on the food culture of the travel destination. For example, if the user is traveling to a particular country, the collection unit collects data based on the food culture of that country, and the generation unit generates taste stimuli that recreate the local taste based on that data. Also, if the user wants to enjoy a particular dish during their trip, the collection unit collects data about that dish, and the generation unit can generate taste stimuli that recreate the taste of that dish based on that data. Furthermore, the provision unit can adjust the timing of providing taste stimuli to match the travel schedule. This allows the user to enjoy local flavors during their trip.

[0089] The taste stimulus provision system can further collect the user's learning data, and the generation AI can generate taste stimuli to improve learning efficiency. For example, if a user wants to concentrate on studying, the collection unit collects the user's learning data, and the generation unit generates taste stimuli that increase concentration based on that data. Alternatively, if a user wants to study in a relaxed manner, the collection unit can collect the user's learning data, and the generation unit can generate taste stimuli that have a relaxing effect based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimuli according to the progress of the study. This allows the user to have a taste experience that improves learning efficiency.

[0090] The taste stimulus provision system further collects the user's driving data, and the generation AI can generate optimal taste stimuli while driving. For example, if the user is tired after a long drive, the collection unit collects the user's driving data, and the generation unit generates taste stimuli with a refreshing effect based on that data. Also, if the user wants to concentrate while driving, the collection unit collects the user's driving data, and the generation unit can generate taste stimuli that increase concentration based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimuli according to the driving situation. This allows the user to have an optimal taste experience while driving.

[0091] The taste stimulus providing system can further collect data on the user's pet, and the generation AI can generate taste stimuli based on interactions with the pet. For example, when a user is playing with a pet, the collection unit collects data on the pet, and the generation unit generates enjoyable taste stimuli based on that data. Also, when a user is caring for a pet, the collection unit collects data on the pet, and the generation unit can generate taste stimuli with a relaxing effect based on that data. Furthermore, the providing unit can adjust the intensity of the taste stimuli according to the situation of the interaction with the pet. This allows the user to have a taste experience while enjoying interactions with their pet.

[0092] The taste stimulus provision system can further collect user hobby data, and the generation AI can generate taste stimuli based on the hobby activity. For example, if the user is drawing, the collection unit collects the user's hobby data, and the generation unit generates taste stimuli that enhance creativity based on that data. Also, if the user is gardening, the collection unit collects the user's hobby data, and the generation unit can generate taste stimuli that have a relaxing effect based on that data. Furthermore, the provision unit can adjust the intensity of the taste stimuli according to the progress of the hobby activity. This allows the user to have a taste experience while enjoying their hobby activity.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The collection unit collects information on the user's taste preferences and stress level. The user's taste preferences include sweetness, sourness, saltiness, bitterness, umami, etc. For example, if the user desires sweetness, that information is collected. The collection unit can also collect the user's stress level. Stress levels are collected using heart rate, electrodermal activity, questionnaire results, etc. Step 2: The generation unit uses the generation AI to analyze the information collected by the collection unit and generate taste stimuli. The generated taste stimuli are generated as electrical signals with specific frequencies and strengths. For example, if the user desires sweetness, the generation AI generates an electrical signal corresponding to the sweetness and delivers it to the tongue. The generation unit can also adjust the taste stimuli taking into account tactile and visual information. Step 3: The providing unit applies the taste stimulus generated by the generating unit to the tongue as an electrical signal. The providing unit applies the electrical signal to the tongue using, for example, electrodes. The providing unit can also adjust the taste stimulus based on the user's preferences. For example, if the user wants to experience a stronger sweetness, the providing unit adjusts the intensity of the electrical signal.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 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 identification processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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 system comprising: a collection unit that collects information on a user's taste preferences and stress levels; a generation unit that analyzes the information collected by the collection unit and generates taste stimuli; and a presentation unit that provides the taste stimuli generated by the generation unit to the tongue as electrical signals.

2. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting taste preferences and stress levels based on the estimated user emotions.

2. The system of claim 1.

3. The system according to claim 1 , wherein the collection unit analyzes the user's past taste preferences and stress level history and selects a collection method.

4. The system according to claim 1 , wherein the collection unit performs filtering based on the user's current living situation and health condition when collecting taste preferences and stress levels.

5. The system according to claim 1 , wherein the collection unit selects a collection means in response to a user's input method when collecting taste preferences and stress levels.

6. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The system according to claim 1 , wherein the collection unit, when collecting taste preferences and stress levels, prioritizes collection of highly relevant information based on geographical location information of the user.

8. The collecting unit Analyzing users' social media activity to collect relevant information when collecting taste preferences and stress levels 2. The system of claim 1.

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