System

Abstract

An object of the system according to the embodiment is to reproduce "taste", "smell", and "tactile sensation" in online shopping.SOLUTION: A system according to an embodiment includes a taste simulator, a smell simulator, and a tactile simulator. A taste simulator reproduces a particular taste. The odor simulator reproduces a specific odor. The tactile simulator reproduces a particular tactile feel.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have been unable to replicate the "taste," "smell," and "touch" of products when shopping online, limiting the user experience.

[0005] The system according to the embodiment aims to reproduce the "taste," "smell," and "touch" of products when shopping online. [Means for solving the problem]

[0006] The system according to the embodiment includes a taste simulator, an odor simulator, and a tactile simulator. The taste simulator reproduces a specific taste. The odor simulator reproduces a specific odor. The tactile simulator reproduces a specific tactile sensation. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the "taste," "smell," and "touch" of products when shopping online. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The dedicated device according to the embodiment of the present invention is a system in which generative AI reproduces the taste, smell, and texture of a product without the user actually picking up the product, thereby significantly improving the user's purchasing experience.

[0029] A dedicated device according to an embodiment includes a taste simulator, an odor simulator, and a tactile simulator. The taste simulator reproduces specific tastes. For example, the taste simulator can reproduce specific tastes such as sweet, sour, bitter, salty, and umami. The taste simulator can also generate chemicals that act directly on the user's tongue. The odor simulator reproduces specific smells. For example, the odor simulator can reproduce specific smells such as floral, food, and chemical odors. The odor simulator can also generate fragrances that act directly on the user's nose. The tactile simulator reproduces specific tactile sensations. For example, the tactile simulator can reproduce specific tactile sensations such as soft, hard, smooth, and rough. The tactile simulator can also generate vibrations and pressure that act directly on the user's hand. This allows the user to experience the taste, smell, and texture of a product without actually picking up the product.

[0030] The taste simulator generates chemical substances to reproduce specific tastes and applies them directly to the user's tongue. For example, the taste simulator generates chemical substances to reproduce specific tastes and applies them directly to the user's tongue. For example, the taste simulator generates chemical substances to reproduce sweet tastes and applies them directly to the user's tongue. The taste simulator also generates chemical substances to reproduce sour tastes and applies them directly to the user's tongue. The taste simulator also generates chemical substances to reproduce bitter tastes and applies them directly to the user's tongue. This allows the user to experience the taste of a product without actually consuming it.

[0031] The smell simulator can generate a fragrance to reproduce a specific smell and apply it directly to the user's nose. For example, the smell simulator can generate a fragrance to reproduce a specific smell and apply it directly to the user's nose. For example, the smell simulator can generate a fragrance to reproduce a floral smell and apply it directly to the user's nose. The smell simulator can also generate a fragrance to reproduce a food smell and apply it directly to the user's nose. The smell simulator can also generate a fragrance to reproduce a chemical smell and apply it directly to the user's nose. This allows the user to experience the smell of the product without actually picking it up.

[0032] The tactile simulator generates vibrations and pressure to reproduce a specific tactile sensation and applies them directly to the user's hand. For example, the tactile simulator generates vibrations and pressure to reproduce a specific tactile sensation and applies them directly to the user's hand. For example, the tactile simulator generates vibrations and pressure to reproduce a soft tactile sensation and applies them directly to the user's hand. The tactile simulator also generates vibrations and pressure to reproduce a hard tactile sensation and applies them directly to the user's hand. The tactile simulator also generates vibrations and pressure to reproduce a smooth tactile sensation and applies them directly to the user's hand. This allows the user to experience the tactile sensation of a product without actually picking it up.

[0033] The taste simulator can use AI to learn a user's past taste history and reproduce the optimal taste for each individual user. For example, the taste simulator can learn the user's past taste history and reproduce the optimal taste for each individual user. For example, the taste simulator can learn the tastes that the user has liked and avoided in the past and reproduce the optimal taste. The taste simulator can also suggest the optimal combination of flavors based on the taste data that the user has evaluated in the past. The taste simulator can also learn the user's taste history and adjust the taste simulator settings in real time. This allows the optimal taste to be reproduced based on the user's past taste history.

[0034] The odor simulator can learn a user's past olfactory history and use AI to recreate the optimal odor for each individual user. For example, the odor simulator can learn the user's past olfactory history and use AI to recreate the optimal odor for each individual user. For example, the odor simulator can learn the odors that the user has liked and avoided in the past and recreate the optimal odor. The odor simulator can also suggest the optimal odor combination based on the olfactory data that the user has previously evaluated. The odor simulator can also learn the user's olfactory history and adjust the odor simulator settings in real time. This allows the optimal odor to be recreated based on the user's past olfactory history.

[0035] The tactile simulator can use AI to learn the user's past tactile history and reproduce the optimal tactile sensation for each individual user. For example, the tactile simulator can learn the user's past tactile history and reproduce the optimal tactile sensation for each individual user. For example, the tactile simulator can learn the tactile sensations that the user has previously preferred and avoided, and reproduce the optimal tactile sensation. The tactile simulator can also suggest the optimal combination of tactile sensations based on the tactile data that the user has previously evaluated. The tactile simulator can also learn the user's tactile history and adjust the tactile simulator settings in real time. This allows the optimal tactile sensation to be reproduced based on the user's past tactile history.

[0036] The taste simulator can adjust the composition of the chemical substances to be generated in real time and fine-tune the taste based on user feedback. For example, the taste simulator can adjust the composition of the chemical substances to be generated in real time and fine-tune the taste based on user feedback. For example, the taste simulator receives real-time feedback from the user and instantly adjusts the chemical substance composition. Furthermore, the taste simulator collects feedback data when the user evaluates the taste and reflects it in the next taste reproduction. Furthermore, the taste simulator detects the reaction of the user's tongue with a sensor and fine-tunes the taste in real time. This allows the taste to be adjusted in real time based on user feedback.

[0037] The odor simulator can adjust the composition of the fragrance to be generated in real time and fine-tune the odor based on user feedback. For example, the odor simulator can adjust the composition of the fragrance to be generated in real time and fine-tune the odor based on user feedback. For example, the odor simulator receives real-time user feedback and instantly adjusts the composition of the fragrance. Furthermore, when the user evaluates the odor, the odor simulator collects feedback data and reflects it in the next odor reproduction. Furthermore, the odor simulator detects the user's olfactory response with a sensor and fine-tunes the odor in real time. This allows the odor to be adjusted in real time based on user feedback.

[0038] The tactile simulator can adjust the vibrations and pressure it generates in real time and fine-tune the tactile sensation based on user feedback. For example, the tactile simulator can adjust the vibrations and pressure it generates in real time and fine-tune the tactile sensation based on user feedback. For example, the tactile simulator receives real-time user feedback and instantly adjusts the vibrations and pressure. Furthermore, the tactile simulator collects feedback data when the user evaluates the tactile sensation and reflects it in the next tactile sensation reproduction. Furthermore, the tactile simulator detects the user's tactile response with a sensor and fine-tunes the tactile sensation in real time. This allows the tactile sensation to be adjusted in real time based on user feedback.

[0039] When prototyping a cooking recipe, the taste simulator can be used to check the taste before actually cooking. For example, when prototyping a cooking recipe, the taste simulator can be used to check the taste before actually cooking. For example, the taste simulator can be used to check the taste at each stage of the recipe and adjust it as necessary. When prototyping a cooking recipe, the taste simulator can also be used to check the final taste and complete the recipe. The taste simulator can also be used to compare the tastes of different recipes and select the optimal recipe. This allows the taste to be checked before actually cooking.

[0040] When developing a new perfume product, a smell simulator can be used to check the scent before the product is actually made. For example, when developing a new perfume product, a smell simulator can be used to check the scent before the product is actually made. For example, a smell simulator can be used to check the scent at each stage of the perfume development and adjust it as necessary. When developing a new perfume product, a smell simulator can also be used to check the final scent and complete the product. A smell simulator can also be used to try different combinations of fragrances and select the optimal scent. This allows the scent to be checked before the product is actually made.

[0041] When creating a product prototype, a tactile simulator can be used to check the tactile feel before actually making the product. For example, when creating a product prototype, a tactile simulator can be used to check the tactile feel before actually making the product. For example, a tactile simulator can be used to check the tactile feel at each stage of the prototype and adjust it as necessary. A tactile simulator can also be used when creating a product prototype to check the final tactile feel before completing the product. A tactile simulator can also be used to compare the tactile feel of different materials and select the most suitable material. This allows the tactile feel to be checked before actually making the product.

[0042] The taste simulator can be applied to educational purposes and used in culinary schools and nutrition classes. The taste simulator can be applied to educational purposes and used in culinary schools and nutrition classes, for example. For example, the taste simulator can be used in culinary school classes to check the taste before students actually cook. The taste simulator can also be used in nutrition classes to experience the taste of different nutrients. The taste simulator can also be used to create teaching materials for learning the basic tastes of cooking. This allows for educational applications and use in culinary schools and nutrition classes.

[0043] The odor simulator can be applied to medical purposes and used in the rehabilitation of olfactory disorders. The odor simulator can be applied to medical purposes, for example, in the rehabilitation of olfactory disorders. For example, the odor simulator can be used in the rehabilitation of olfactory disorders, where patients can recover their sense of smell by experiencing different scents. The odor simulator can also be used to train patients with olfactory disorders to recognize specific scents. The odor simulator can also be incorporated into rehabilitation programs at medical institutions to support the recovery of patients' sense of smell. This allows the odor simulator to be applied to medical purposes and used in the rehabilitation of olfactory disorders.

[0044] The tactile simulator can be applied to rehabilitation purposes and used to treat tactile disorders. The tactile simulator can be applied to rehabilitation purposes and used to treat tactile disorders, for example. For example, the tactile simulator can be used in rehabilitation for tactile disorders, allowing patients to experience different tactile sensations and recover their sense of touch. The tactile simulator can also be used to train patients with tactile disorders to recognize specific tactile sensations. The tactile simulator can also be incorporated into rehabilitation programs at medical institutions to support patients in recovering their sense of touch. This allows the simulator to be applied to rehabilitation purposes and used to treat tactile disorders.

[0045] The device's operation interface can use AI to learn the user's operation history and suggest the optimal operation method. The device's operation interface can, for example, use AI to learn the user's operation history and suggest the optimal operation method. For example, the device's operation interface can learn the user's past operation history and prioritize displaying frequently used functions. The device's operation interface can also suggest the optimal operation method based on the user's operation history. The device's operation interface can also learn the user's operation patterns and customize the operation interface in real time. This makes it possible to suggest the optimal operation method based on the user's operation history.

[0046] The device can automatically learn a user's preferences and automatically adjust settings to provide optimal tastes, smells, and textures. For example, the device can automatically learn a user's preferences and automatically adjust settings to provide optimal tastes, smells, and textures. For example, the device can learn a user's preferences, record the optimal flavor intensity or type of scent, and reflect that the next time the device is used. The device can also automatically adjust settings based on the user's preferences. The device can also adjust settings in real time based on user feedback. This allows the device to automatically adjust optimal settings based on the user's preferences.

[0047] The device can be linked to a smart home system and integrated with other devices in the home for operation. For example, the device can be linked to a smart home system and integrated with other devices in the home for operation. For example, the device can be linked to a smart speaker or a smart light and operated by voice commands. The device can also be linked to a smart home system and automatically adjust settings according to the home environment. The device can also be linked to a smart home system and integrated with other devices in the home for operation.

[0048] The device can be applied to educational purposes and used for experiments and experiential learning in online classes. The device can be applied to educational purposes, for example, for experiments and experiential learning in online classes. For example, the device can be used to provide taste and smell experiences in chemistry experiments. The device can also be used to allow students in online classes to learn about taste, smell, and touch without actually experiencing them. The device can also be used to experience the tactile sensations of different materials in physics classes. This allows the device to be applied to educational purposes and used for experiments and experiential learning in online classes.

[0049] Online shopping sites can use AI to learn a user's purchasing history and recommend the most suitable products. Online shopping sites can, for example, use AI to learn a user's purchasing history and recommend the most suitable products. For example, an online shopping site can learn a user's past purchasing history and identify their preferred products. The online shopping site can also recommend the most suitable products based on the user's purchasing history. The online shopping site can also learn a user's purchasing patterns and recommend products in real time. This allows the site to recommend the most suitable products based on the user's purchasing history.

[0050] The device can collect user feedback in real time and reflect it on an online shopping site. For example, the device can collect user feedback in real time and reflect it on an online shopping site. For example, if a user comments that a product is "too sweet," the device can reflect that information on the site. The device can also cause the online shopping site to update product information based on the user feedback. The device can also collect user feedback in real time, and the online shopping site can recommend products based on that data. This allows user feedback to be collected in real time and reflected on an online shopping site.

[0051] Online shopping sites can be linked with virtual reality (VR) to provide a more realistic purchasing experience. For example, online shopping sites can use VR goggles to allow users to walk around a virtual store and select products. Online shopping sites can also use VR to allow users to view products from a 360-degree angle. Online shopping sites can also use VR to allow users to experience the taste, smell, and feel of products in a virtual space. This allows online shopping sites to be linked with virtual reality (VR) to provide a more realistic purchasing experience.

[0052] Online shopping sites can be linked with social media, allowing users to enjoy a shopping experience while sharing with their friends and family. Online shopping sites can be linked with social media, for example, allowing users to enjoy a shopping experience while sharing with their friends and family. For example, online shopping sites allow users to share product information with their friends and family. Furthermore, online shopping sites can be linked with social media, allowing users to enjoy online shopping with their friends and family. Furthermore, online shopping sites can be linked with social media, allowing users to refer to the purchasing history and ratings of their friends and family. In this way, by linking with social media, users can enjoy a shopping experience while sharing with their friends and family.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The dedicated device can monitor the user's health condition and recommend health-conscious products. For example, the device can measure the user's heart rate and blood pressure and recommend products based on their health condition. The device can also record the user's diet history and suggest products that take nutritional balance into consideration. The device can also track the user's exercise volume and recommend products suitable for post-exercise use. This allows the device to provide optimal products based on the user's health condition.

[0055] The dedicated device can analyze a user's purchasing history and recommend new products that go well with previously purchased products. For example, the device can suggest new products that go well with products based on data on products the user has purchased in the past. The device can also learn the user's purchasing patterns and recommend products that will be useful for the next purchase. The device can also analyze the user's preferences and provide the best products for each individual user. This allows the device to recommend the best products based on the user's purchasing history.

[0056] The dedicated device can monitor a user's sleep patterns and provide an optimal sleeping environment. For example, the device analyzes the user's sleep data and suggests optimal bedding and pillows. The device also adjusts the optimal bedroom temperature and humidity based on the user's sleep patterns. The device also provides relaxing music and environmental sounds according to the user's sleep state. This allows the device to provide an optimal sleeping environment based on the user's sleep patterns.

[0057] The dedicated device can analyze the user's dietary history and propose a nutritionally balanced meal plan. For example, the device can create a nutritionally balanced meal plan based on the user's past dietary data. The device can also propose foods to supplement necessary nutrients based on the user's dietary history. The device can also learn the user's eating patterns and provide a healthy meal plan. This allows the device to propose a nutritionally balanced meal plan based on the user's dietary history.

[0058] The dedicated device can analyze the user's exercise history and propose an optimal exercise plan. For example, the device creates an effective exercise plan based on the user's past exercise data. The device also proposes appropriate exercise intensity and frequency based on the user's exercise history. The device also learns the user's exercise patterns and provides an optimal exercise plan for each individual user. This allows the device to propose an optimal exercise plan based on the user's exercise history.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The taste simulator reproduces specific tastes. For example, it can reproduce specific tastes such as sweet, sour, bitter, salty, and umami. The taste simulator can also generate chemicals that act directly on the user's tongue. Step 2: The odor simulator reproduces a specific odor. For example, it can reproduce specific odors such as the scent of flowers, food, or chemicals. The odor simulator can also generate fragrances that act directly on the user's nose. Step 3: The tactile simulator reproduces specific tactile sensations, such as soft, hard, smooth, and rough. The tactile simulator can also generate vibrations and pressure that act directly on the user's hand.

[0061] (Example 2) The dedicated device according to the embodiment of the present invention is a system in which generative AI reproduces the taste, smell, and texture of a product without the user actually picking up the product, thereby significantly improving the user's purchasing experience.

[0062] A dedicated device according to an embodiment includes a taste simulator, an odor simulator, and a tactile simulator. The taste simulator reproduces specific tastes. For example, the taste simulator can reproduce specific tastes such as sweet, sour, bitter, salty, and umami. The taste simulator can also generate chemicals that act directly on the user's tongue. The odor simulator reproduces specific smells. For example, the odor simulator can reproduce specific smells such as floral, food, and chemical odors. The odor simulator can also generate fragrances that act directly on the user's nose. The tactile simulator reproduces specific tactile sensations. For example, the tactile simulator can reproduce specific tactile sensations such as soft, hard, smooth, and rough. The tactile simulator can also generate vibrations and pressure that act directly on the user's hand. This allows the user to experience the taste, smell, and texture of a product without actually picking up the product.

[0063] The taste simulator generates chemical substances to reproduce specific tastes and applies them directly to the user's tongue. For example, the taste simulator generates chemical substances to reproduce specific tastes and applies them directly to the user's tongue. For example, the taste simulator generates chemical substances to reproduce sweet tastes and applies them directly to the user's tongue. The taste simulator also generates chemical substances to reproduce sour tastes and applies them directly to the user's tongue. The taste simulator also generates chemical substances to reproduce bitter tastes and applies them directly to the user's tongue. This allows the user to experience the taste of a product without actually consuming it.

[0064] The smell simulator can generate a fragrance to reproduce a specific smell and apply it directly to the user's nose. For example, the smell simulator can generate a fragrance to reproduce a specific smell and apply it directly to the user's nose. For example, the smell simulator can generate a fragrance to reproduce a floral smell and apply it directly to the user's nose. The smell simulator can also generate a fragrance to reproduce a food smell and apply it directly to the user's nose. The smell simulator can also generate a fragrance to reproduce a chemical smell and apply it directly to the user's nose. This allows the user to experience the smell of the product without actually picking it up.

[0065] The tactile simulator generates vibrations and pressure to reproduce a specific tactile sensation and applies them directly to the user's hand. For example, the tactile simulator generates vibrations and pressure to reproduce a specific tactile sensation and applies them directly to the user's hand. For example, the tactile simulator generates vibrations and pressure to reproduce a soft tactile sensation and applies them directly to the user's hand. The tactile simulator also generates vibrations and pressure to reproduce a hard tactile sensation and applies them directly to the user's hand. The tactile simulator also generates vibrations and pressure to reproduce a smooth tactile sensation and applies them directly to the user's hand. This allows the user to experience the tactile sensation of a product without actually picking it up.

[0066] The taste simulator can use AI to learn a user's past taste history and reproduce the optimal taste for each individual user. For example, the taste simulator can learn the user's past taste history and reproduce the optimal taste for each individual user. For example, the taste simulator can learn the tastes that the user has liked and avoided in the past and reproduce the optimal taste. The taste simulator can also suggest the optimal combination of flavors based on the taste data that the user has evaluated in the past. The taste simulator can also learn the user's taste history and adjust the taste simulator settings in real time. This allows the optimal taste to be reproduced based on the user's past taste history.

[0067] The odor simulator can learn a user's past olfactory history and use AI to recreate the optimal odor for each individual user. For example, the odor simulator can learn the user's past olfactory history and use AI to recreate the optimal odor for each individual user. For example, the odor simulator can learn the odors that the user has liked and avoided in the past and recreate the optimal odor. The odor simulator can also suggest the optimal odor combination based on the olfactory data that the user has previously evaluated. The odor simulator can also learn the user's olfactory history and adjust the odor simulator settings in real time. This allows the optimal odor to be recreated based on the user's past olfactory history.

[0068] The tactile simulator can use AI to learn the user's past tactile history and reproduce the optimal tactile sensation for each individual user. For example, the tactile simulator can learn the user's past tactile history and reproduce the optimal tactile sensation for each individual user. For example, the tactile simulator can learn the tactile sensations that the user has previously preferred and avoided, and reproduce the optimal tactile sensation. The tactile simulator can also suggest the optimal combination of tactile sensations based on the tactile data that the user has previously evaluated. The tactile simulator can also learn the user's tactile history and adjust the tactile simulator settings in real time. This allows the optimal tactile sensation to be reproduced based on the user's past tactile history.

[0069] The taste simulator can adjust the composition of the chemical substances to be generated in real time and fine-tune the taste based on user feedback. For example, the taste simulator can adjust the composition of the chemical substances to be generated in real time and fine-tune the taste based on user feedback. For example, the taste simulator receives real-time feedback from the user and instantly adjusts the chemical substance composition. Furthermore, the taste simulator collects feedback data when the user evaluates the taste and reflects it in the next taste reproduction. Furthermore, the taste simulator detects the reaction of the user's tongue with a sensor and fine-tunes the taste in real time. This allows the taste to be adjusted in real time based on user feedback.

[0070] The odor simulator can adjust the composition of the fragrance to be generated in real time and fine-tune the odor based on user feedback. For example, the odor simulator can adjust the composition of the fragrance to be generated in real time and fine-tune the odor based on user feedback. For example, the odor simulator receives real-time user feedback and instantly adjusts the composition of the fragrance. Furthermore, when the user evaluates the odor, the odor simulator collects feedback data and reflects it in the next odor reproduction. Furthermore, the odor simulator detects the user's olfactory response with a sensor and fine-tunes the odor in real time. This allows the odor to be adjusted in real time based on user feedback.

[0071] The tactile simulator can adjust the vibrations and pressure it generates in real time and fine-tune the tactile sensation based on user feedback. For example, the tactile simulator can adjust the vibrations and pressure it generates in real time and fine-tune the tactile sensation based on user feedback. For example, the tactile simulator receives real-time user feedback and instantly adjusts the vibrations and pressure. Furthermore, the tactile simulator collects feedback data when the user evaluates the tactile sensation and reflects it in the next tactile sensation reproduction. Furthermore, the tactile simulator detects the user's tactile response with a sensor and fine-tunes the tactile sensation in real time. This allows the tactile sensation to be adjusted in real time based on user feedback.

[0072] The taste simulator can use the emotion estimation function to automatically adjust the intensity and type of flavor according to the user's emotional state. For example, the taste simulator uses the emotion estimation function to automatically adjust the intensity and type of flavor according to the user's emotional state. For example, the taste simulator emphasizes sweetness when the user is relaxed and suppresses bitterness when the user is stressed. The taste simulator also adjusts the intensity of flavor based on the user's emotional state. The taste simulator also provides a type of flavor according to the user's emotion. This allows the intensity and type of flavor to be automatically adjusted according to the user's emotional state.

[0073] The tactile simulator can use the emotion estimation function to automatically adjust the intensity and type of tactile sensations according to the user's emotional state. For example, the tactile simulator uses the emotion estimation function to automatically adjust the intensity and type of tactile sensations according to the user's emotional state. For example, the tactile simulator emphasizes soft tactile sensations when the user is relaxed and suppresses hard tactile sensations when the user is stressed. The tactile simulator also adjusts the intensity of tactile sensations based on the user's emotional state. The tactile simulator also provides types of tactile sensations according to the user's emotions. This allows the intensity and type of tactile sensations to be automatically adjusted according to the user's emotional state.

[0074] When prototyping a cooking recipe, the taste simulator can be used to check the taste before actually cooking. For example, when prototyping a cooking recipe, the taste simulator can be used to check the taste before actually cooking. For example, the taste simulator can be used to check the taste at each stage of the recipe and adjust it as necessary. When prototyping a cooking recipe, the taste simulator can also be used to check the final taste and complete the recipe. The taste simulator can also be used to compare the tastes of different recipes and select the optimal recipe. This allows the taste to be checked before actually cooking.

[0075] When developing a new perfume product, a smell simulator can be used to check the scent before the product is actually made. For example, when developing a new perfume product, a smell simulator can be used to check the scent before the product is actually made. For example, a smell simulator can be used to check the scent at each stage of the perfume development and adjust it as necessary. When developing a new perfume product, a smell simulator can also be used to check the final scent and complete the product. A smell simulator can also be used to try different combinations of fragrances and select the optimal scent. This allows the scent to be checked before the product is actually made.

[0076] When creating a product prototype, a tactile simulator can be used to check the tactile feel before actually making the product. For example, when creating a product prototype, a tactile simulator can be used to check the tactile feel before actually making the product. For example, a tactile simulator can be used to check the tactile feel at each stage of the prototype and adjust it as necessary. A tactile simulator can also be used when creating a product prototype to check the final tactile feel before completing the product. A tactile simulator can also be used to compare the tactile feel of different materials and select the most suitable material. This allows the tactile feel to be checked before actually making the product.

[0077] The taste simulator can be applied to educational purposes and used in culinary schools and nutrition classes. The taste simulator can be applied to educational purposes and used in culinary schools and nutrition classes, for example. For example, the taste simulator can be used in culinary school classes to check the taste before students actually cook. The taste simulator can also be used in nutrition classes to experience the taste of different nutrients. The taste simulator can also be used to create teaching materials for learning the basic tastes of cooking. This allows for educational applications and use in culinary schools and nutrition classes.

[0078] The odor simulator can be applied to medical purposes and used in the rehabilitation of olfactory disorders. The odor simulator can be applied to medical purposes, for example, in the rehabilitation of olfactory disorders. For example, the odor simulator can be used in the rehabilitation of olfactory disorders, where patients can recover their sense of smell by experiencing different scents. The odor simulator can also be used to train patients with olfactory disorders to recognize specific scents. The odor simulator can also be incorporated into rehabilitation programs at medical institutions to support the recovery of patients' sense of smell. This allows the odor simulator to be applied to medical purposes and used in the rehabilitation of olfactory disorders.

[0079] The tactile simulator can be applied to rehabilitation purposes and used to treat tactile disorders. The tactile simulator can be applied to rehabilitation purposes and used to treat tactile disorders, for example. For example, the tactile simulator can be used in rehabilitation for tactile disorders, allowing patients to experience different tactile sensations and recover their sense of touch. The tactile simulator can also be used to train patients with tactile disorders to recognize specific tactile sensations. The tactile simulator can also be incorporated into rehabilitation programs at medical institutions to support patients in recovering their sense of touch. This allows the simulator to be applied to rehabilitation purposes and used to treat tactile disorders.

[0080] The taste simulator uses the emotion estimation function to provide a taste that matches a user's emotion when the user feels that emotion. For example, the taste simulator uses the emotion estimation function to provide a taste that matches a user's emotion when the user feels that emotion. For example, the taste simulator emphasizes sweetness when the user is relaxed and suppresses bitterness when the user is stressed. The taste simulator also adjusts the intensity of the taste based on the user's emotional state. The taste simulator also provides a type of taste that matches the user's emotion. This allows the taste simulator to provide a taste that matches a user's emotion when the user feels that emotion.

[0081] The smell simulator uses the emotion estimation function to provide a smell that matches a user's emotion when the user feels that emotion. For example, the smell simulator uses the emotion estimation function to provide a smell that matches a user's emotion when the user feels that emotion. For example, the smell simulator emphasizes the scent of lavender when the user is relaxed and suppresses the scent of mint when the user is stressed. The smell simulator also adjusts the intensity of the smell based on the user's emotional state. The smell simulator also provides a type of scent that matches the user's emotion. This allows the smell simulator to provide a smell that matches a user's emotion when the user feels that emotion.

[0082] The tactile simulator can use the emotion estimation function to provide a tactile sensation that matches the user's emotion when the user feels that emotion. For example, the ... emphasizes a soft tactile sensation when the user is relaxed and suppresses a hard tactile sensation when the user is stressed. The tactile simulator also adjusts the intensity of the tactile sensation based on the user's emotional state. The tactile simulator also provides a type of tactile sensation that matches the user's emotion. This allows the tactile simulator to provide a tactile sensation that matches the user's emotion when the user feels that emotion.

[0083] The device's operation interface can use AI to learn the user's operation history and suggest the optimal operation method. The device's operation interface can, for example, use AI to learn the user's operation history and suggest the optimal operation method. For example, the device's operation interface can learn the user's past operation history and prioritize displaying frequently used functions. The device's operation interface can also suggest the optimal operation method based on the user's operation history. The device's operation interface can also learn the user's operation patterns and customize the operation interface in real time. This makes it possible to suggest the optimal operation method based on the user's operation history.

[0084] The device can automatically learn a user's preferences and automatically adjust settings to provide optimal tastes, smells, and textures. For example, the device can automatically learn a user's preferences and automatically adjust settings to provide optimal tastes, smells, and textures. For example, the device can learn a user's preferences, record the optimal flavor intensity or type of scent, and reflect that the next time the device is used. The device can also automatically adjust settings based on the user's preferences. The device can also adjust settings in real time based on user feedback. This allows the device to automatically adjust optimal settings based on the user's preferences.

[0085] The device can be linked to a smart home system and integrated with other devices in the home for operation. For example, the device can be linked to a smart home system and integrated with other devices in the home for operation. For example, the device can be linked to a smart speaker or a smart light and operated by voice commands. The device can also be linked to a smart home system and automatically adjust settings according to the home environment. The device can also be linked to a smart home system and integrated with other devices in the home for operation.

[0086] The device can be applied to educational purposes and used for experiments and experiential learning in online classes. The device can be applied to educational purposes, for example, for experiments and experiential learning in online classes. For example, the device can be used to provide taste and smell experiences in chemistry experiments. The device can also be used to allow students in online classes to learn about taste, smell, and touch without actually experiencing them. The device can also be used to experience the tactile sensations of different materials in physics classes. This allows the device to be applied to educational purposes and used for experiments and experiential learning in online classes.

[0087] The device can use the emotion estimation function to provide an operation interface that matches the user's emotion when the user feels a particular emotion. For example, the device can use the emotion estimation function to provide an operation interface that matches the user's emotion when the user feels a particular emotion. For example, the device provides a simple operation interface when the user is relaxed and avoids complicated operations when the user is feeling stressed. The device also automatically customizes the operation interface based on the user's emotional state. The device also provides an operation interface that matches the user's emotion. In this way, the device can provide an operation interface that matches the user's emotion when the user feels a particular emotion.

[0088] Online shopping sites can use AI to learn a user's purchasing history and recommend the most suitable products. Online shopping sites can, for example, use AI to learn a user's purchasing history and recommend the most suitable products. For example, an online shopping site can learn a user's past purchasing history and identify their preferred products. The online shopping site can also recommend the most suitable products based on the user's purchasing history. The online shopping site can also learn a user's purchasing patterns and recommend products in real time. This allows the site to recommend the most suitable products based on the user's purchasing history.

[0089] The device can collect user feedback in real time and reflect it on an online shopping site. For example, the device can collect user feedback in real time and reflect it on an online shopping site. For example, if a user comments that a product is "too sweet," the device can reflect that information on the site. The device can also cause the online shopping site to update product information based on the user feedback. The device can also collect user feedback in real time, and the online shopping site can recommend products based on that data. This allows user feedback to be collected in real time and reflected on an online shopping site.

[0090] An online shopping site can use the emotion estimation function to recommend products according to the emotional state of a user. For example, an online shopping site can use the emotion estimation function to recommend products according to the emotional state of a user. For example, when a user is relaxed, the online shopping site recommends products that have a relaxing effect, and when a user is stressed, the online shopping site recommends products that relieve stress. Furthermore, the online shopping site recommends optimal products based on the emotional state of the user. Furthermore, the online shopping site provides products according to the user's emotions. This makes it possible to recommend products according to the emotional state of the user.

[0091] Online shopping sites can be linked with virtual reality (VR) to provide a more realistic purchasing experience. For example, online shopping sites can use VR goggles to allow users to walk around a virtual store and select products. Online shopping sites can also use VR to allow users to view products from a 360-degree angle. Online shopping sites can also use VR to allow users to experience the taste, smell, and feel of products in a virtual space. This allows online shopping sites to be linked with virtual reality (VR) to provide a more realistic purchasing experience.

[0092] Online shopping sites can be linked with social media, allowing users to enjoy a shopping experience while sharing with their friends and family. Online shopping sites can be linked with social media, for example, allowing users to enjoy a shopping experience while sharing with their friends and family. For example, online shopping sites allow users to share product information with their friends and family. Furthermore, online shopping sites can be linked with social media, allowing users to enjoy online shopping with their friends and family. Furthermore, online shopping sites can be linked with social media, allowing users to refer to the purchasing history and ratings of their friends and family. In this way, by linking with social media, users can enjoy a shopping experience while sharing with their friends and family.

[0093] An online shopping site can use the emotion estimation function to recommend products that match a user's emotion when the user feels that emotion. For example, an online shopping site can use the emotion estimation function to recommend products that match a user's emotion when the user feels that emotion. For example, when the user is relaxed, the online shopping site can recommend products that have a relaxing effect, and when the user is stressed, the online shopping site can recommend products that relieve stress. The online shopping site can also recommend optimal products based on the user's emotional state. The online shopping site can also provide products that match the user's emotion. In this way, when the user feels a specific emotion, the online shopping site can recommend products that match the emotion.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The dedicated device can monitor the user's health condition and recommend health-conscious products. For example, the device can measure the user's heart rate and blood pressure and recommend products based on their health condition. The device can also record the user's diet history and suggest products that take nutritional balance into consideration. The device can also track the user's exercise volume and recommend products suitable for post-exercise use. This allows the device to provide optimal products based on the user's health condition.

[0096] The dedicated device can analyze a user's purchasing history and recommend new products that go well with previously purchased products. For example, the device can suggest new products that go well with products based on data on products the user has purchased in the past. The device can also learn the user's purchasing patterns and recommend products that will be useful for the next purchase. The device can also analyze the user's preferences and provide the best products for each individual user. This allows the device to recommend the best products based on the user's purchasing history.

[0097] The dedicated device can provide relaxing music and images based on the user's emotional state. For example, the device plays relaxing music when the user is feeling stressed. The device also displays relaxing images according to the user's emotional state. The device also provides relaxing environmental sounds according to the user's emotions. This allows the device to provide relaxing content according to the user's emotional state.

[0098] The dedicated device can suggest activities for relieving stress based on the user's emotional state. For example, the device suggests yoga or meditation activities when the user is feeling stressed. The device also suggests a walking course with a relaxing effect based on the user's emotional state. The device also suggests hobbies or activities for relieving stress based on the user's emotions. In this way, activities for relieving stress can be suggested according to the user's emotional state.

[0099] The dedicated device can provide aromatherapy for emotional stabilization based on the user's emotional state. For example, the device can provide a lavender scent when the user is relaxed. The device can also suggest an aromatic oil for emotional stabilization based on the user's emotional state. The device can also use an aroma diffuser with a relaxing effect based on the user's emotion. This allows the device to provide aromatherapy for emotional stabilization based on the user's emotional state.

[0100] The dedicated device can monitor a user's sleep patterns and provide an optimal sleeping environment. For example, the device analyzes the user's sleep data and suggests optimal bedding and pillows. The device also adjusts the optimal bedroom temperature and humidity based on the user's sleep patterns. The device also provides relaxing music and environmental sounds according to the user's sleep state. This allows the device to provide an optimal sleeping environment based on the user's sleep patterns.

[0101] The dedicated device can analyze the user's dietary history and propose a nutritionally balanced meal plan. For example, the device can create a nutritionally balanced meal plan based on the user's past dietary data. The device can also propose foods to supplement necessary nutrients based on the user's dietary history. The device can also learn the user's eating patterns and provide a healthy meal plan. This allows the device to propose a nutritionally balanced meal plan based on the user's dietary history.

[0102] The dedicated device can analyze the user's exercise history and propose an optimal exercise plan. For example, the device creates an effective exercise plan based on the user's past exercise data. The device also proposes appropriate exercise intensity and frequency based on the user's exercise history. The device also learns the user's exercise patterns and provides an optimal exercise plan for each individual user. This allows the device to propose an optimal exercise plan based on the user's exercise history.

[0103] The dedicated device can provide a meditation guide for stabilizing emotions based on the user's emotional state. For example, the device can provide a meditation guide with a relaxing effect when the user is feeling stressed. The device can also suggest breathing techniques for stabilizing emotions based on the user's emotional state. The device can also provide meditation music with a relaxing effect based on the user's emotions. In this way, a meditation guide for stabilizing emotions can be provided according to the user's emotional state.

[0104] The dedicated device can provide a counseling service for stabilizing emotions based on the user's emotional state. For example, the device can suggest online counseling when the user is feeling stressed. The device can also suggest a self-care method for stabilizing emotions according to the user's emotional state. The device can also provide a counseling session with a relaxing effect based on the user's emotions. This allows the device to provide a counseling service for stabilizing emotions according to the user's emotional state.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The taste simulator reproduces specific tastes. For example, it can reproduce specific tastes such as sweet, sour, bitter, salty, and umami. The taste simulator can also generate chemicals that act directly on the user's tongue. Step 2: The odor simulator reproduces a specific odor. For example, it can reproduce specific odors such as the scent of flowers, food, or chemicals. The odor simulator can also generate fragrances that act directly on the user's nose. Step 3: The tactile simulator reproduces specific tactile sensations, such as soft, hard, smooth, and rough. The tactile simulator can also generate vibrations and pressure that act directly on the user's hand.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0174] 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 taste simulator that reproduces specific flavors, An odor simulator that reproduces specific odors, a tactile simulator that reproduces a specific tactile sensation; A system characterized by:

2. The taste simulator comprises: It produces chemicals to replicate specific flavors and delivers them directly to the user's tongue.

2. The system of claim 1.

3. The odor simulator comprises: Creates fragrances to replicate specific smells and delivers them directly to the user's nose 2. The system of claim 1.

4. The tactile simulator comprises: Generates vibrations and pressure to replicate specific tactile sensations and applies them directly to the user's hand 2. The system of claim 1.

5. The taste simulator comprises: Uses AI to learn the user's taste history and recreate the perfect flavor for each individual user.

2. The system of claim 1.

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