system

Abstract

The system according to the embodiment aims to generate and provide a scent based on the location information of a user. According to an embodiment, the system includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives location information of a user. The generating unit generates a fragrance based on the location information received by the receiving unit. The providing unit provides the fragrance generated by the generating unit to the user.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it has been difficult to embody the sense of smell in virtual spaces or digital content, and there have been limitations to improving the user experience.

[0005] The system according to the embodiment aims to generate and provide a scent based on the location information of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives location information of a user. The generating unit generates a fragrance based on the location information received by the receiving unit. The providing unit provides the fragrance generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide a scent based on the location information of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An olfactory generation system according to an embodiment of the present invention generates and provides an aroma based on a user's location, environmental information, scene information, video information, audio information, and the like. When a user moves to a specific location, this olfactory generation system generates an aroma related to that location in real time. For example, when a user moves to a virtual flower field, a floral scent wafts through the air. This system uses an olfactory generation AI to generate an appropriate aroma based on the user's location and environmental information. Next, the system generates an aroma based on a specific event or scene in the game. For example, in a cooking scene in the game, a cooking aroma wafts through the air. This system also uses an olfactory generation AI to generate an appropriate aroma based on the game's progress and scene information. Furthermore, a system is provided that allows viewers of cooking shows to experience the aroma of cooking. For example, viewers can experience the aroma of cooking through a television or smartphone. This system uses an olfactory generation AI to generate an appropriate aroma based on video and audio information. These systems are built around an olfactory generation AI and are expected to significantly improve the user experience. The olfactory generation AI receives inputs such as a user's location, environmental information, scene information, video information, and audio information, and generates an appropriate aroma. The generated scent is provided to the user through a dedicated device, allowing the olfactory generation system to generate and provide scents based on the user's location and environmental information.

[0029] An olfactory generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives location information of a user. The location information includes, but is not limited to, GPS information, Wi-Fi location information, and beacon information. The receiving unit identifies the user's current location using, for example, GPS information. The receiving unit can also identify the user's indoor location using Wi-Fi location information. The receiving unit can also identify the user's location within a specific area using beacon information. The generating unit generates a fragrance based on the location information received by the receiving unit. Examples of fragrance generation include, but are not limited to, the type of fragrance, the generation process, and the generation device. The generating unit generates a fragrance by, for example, mixing specific fragrances. The generating unit can also adjust the intensity and duration of the fragrance. The generating unit can also customize the type of fragrance to suit the user's preferences. The providing unit provides the fragrance generated by the generating unit to the user. Examples of fragrance generation include, but are not limited to, a fragrance diffuser, a smart device, a wearable device, and the like. The providing unit diffuses the scent using, for example, a scent diffuser. The providing unit can also control the scent using a smart device. Furthermore, the providing unit can also provide the scent individually using a wearable device. In this way, the olfactory generation system according to the embodiment can generate and provide the scent based on the user's location information.

[0030] The reception unit can receive environmental information about the user. The environmental information includes, but is not limited to, for example, temperature, humidity, illuminance, and sound. For example, the reception unit measures the ambient temperature using a temperature sensor. The reception unit can also measure the ambient humidity using a humidity sensor. Furthermore, the reception unit can measure the ambient brightness using an illuminance sensor. This allows the reception unit to receive environmental information about the user.

[0031] The generation unit can generate a fragrance based on the environmental information received by the reception unit. The generation unit can adjust the type of fragrance based on, for example, temperature information. For example, when the temperature is high, a refreshing fragrance can be generated. The generation unit can also adjust the intensity of the fragrance based on humidity information. For example, when the humidity is high, the intensity of the fragrance can be weakened. Furthermore, the generation unit can also adjust the duration of the fragrance based on illuminance information. For example, when the illuminance is low, the duration of the fragrance can be extended. This allows the generation unit to generate an appropriate fragrance based on the environmental information.

[0032] The reception unit can receive scene information of the user. The scene information includes, for example, the user's activity content, location, time period, etc., but is not limited to these examples. For example, the reception unit detects the user's activity content using a sensor. The reception unit can also identify the user's current location using GPS information. Furthermore, the reception unit can obtain the user's activity time period using clock information. This allows the reception unit to receive the user's scene information.

[0033] The generation unit can generate a fragrance based on the scene information received by the reception unit. The generation unit, for example, adjusts the type of fragrance based on the user's activity. For example, if the user is relaxing, it can generate a fragrance that has a relaxing effect. The generation unit can also adjust the intensity of the fragrance based on the user's current location. For example, if the user is outdoors, it can increase the intensity of the fragrance. Furthermore, the generation unit can adjust the duration of the fragrance based on the time period during which the user is active. For example, if the user is active at night, it can increase the duration of the fragrance. This allows the generation unit to generate an appropriate fragrance based on the scene information.

[0034] The reception unit can receive video information. Video information includes, but is not limited to, camera footage, recorded footage, and live footage, for example. The reception unit can obtain real-time footage using a camera, for example. The reception unit can also play back recorded footage. Furthermore, the reception unit can receive live footage via the Internet. This allows the reception unit to receive video information.

[0035] The generation unit can generate a fragrance based on the video information received by the reception unit. The generation unit can adjust the type of fragrance based on, for example, camera footage. For example, if a flower is captured in the video, a floral fragrance can be generated. The generation unit can also adjust the intensity of the fragrance based on recorded video. For example, if the video is dark, the intensity of the fragrance can be weakened. Furthermore, the generation unit can adjust the duration of the fragrance based on live video. For example, if the video continues for a long period of time, the duration of the fragrance can be extended. This allows the generation unit to generate an appropriate fragrance based on the video information.

[0036] The reception unit can receive audio information. Examples of audio information include, but are not limited to, microphone recordings, audio files, and real-time audio. The reception unit can obtain real-time audio using, for example, a microphone. The reception unit can also play back recorded audio files. Furthermore, the reception unit can receive real-time audio via the Internet. This allows the reception unit to receive audio information.

[0037] The generation unit can generate a fragrance based on the audio information received by the reception unit. The generation unit can adjust the type of fragrance based on, for example, microphone recording. For example, if the audio includes the sound of cooking, the generation unit can generate the aroma of cooking. The generation unit can also adjust the intensity of the fragrance based on the audio file. For example, if the audio is loud, the intensity of the fragrance can be increased. Furthermore, the generation unit can adjust the duration of the fragrance based on real-time audio. For example, if the audio continues for a long time, the duration of the fragrance can be extended. This allows the generation unit to generate an appropriate fragrance based on the audio information.

[0038] The providing unit can provide the scent to the user through a dedicated device. Examples of dedicated devices include, but are not limited to, a scent diffuser, a smart device, and a wearable device. The providing unit can diffuse the scent using, for example, a scent diffuser. The providing unit can also control the scent using a smart device. Furthermore, the providing unit can also provide the scent individually using a wearable device. This allows the providing unit to provide the scent through a dedicated device, thereby improving the user experience.

[0039] The reception unit can analyze the user's past movement history and select the optimal method for acquiring location information. The reception unit can optimize the timing for acquiring location information based on, for example, places that the user has frequently visited in the past. The reception unit can also analyze the user's movement patterns and suggest an efficient method for acquiring location information. Furthermore, the reception unit can adjust the frequency of acquiring location information in a specific time period based on the user's past movement history. This allows the reception unit to optimize the method for acquiring location information based on the user's past movement history.

[0040] The reception unit can perform filtering based on the user's current activity status when acquiring location information. For example, when the user is walking, the reception unit sets the frequency of acquiring location information to be high. Furthermore, when the user is standing still, the reception unit can also set the frequency of acquiring location information to be low. Furthermore, when the user is driving a vehicle, the reception unit can temporarily stop acquiring location information. In this way, the reception unit can adjust the frequency of acquiring location information according to the user's activity status.

[0041] When acquiring location information, the reception unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. For example, if the user is in an urban area, the reception unit prioritizes acquiring location information of surrounding commercial facilities and tourist spots. In addition, if the user is in a suburban area, the reception unit can also prioritize acquiring location information of natural environments and recreational facilities. Furthermore, if the user is in a specific area, the reception unit can also prioritize acquiring event information and location information of local specialties. This allows the reception unit to prioritize acquiring highly relevant information based on the user's geographical location information.

[0042] When acquiring location information, the reception unit can analyze the user's social media activity and acquire related information. For example, the reception unit prioritizes acquiring location information of places where the user has checked in on social media. The reception unit can also prioritize acquiring location information of places and events that the user is following on social media. Furthermore, the reception unit can also prioritize acquiring location information of places that the user has shared on social media. This allows the reception unit to acquire related information based on the user's social media activity.

[0043] When generating a scent, the generation unit can generate an optimal scent by referring to the user's past olfactory history. The generation unit generates an optimal scent based on, for example, scents that the user has previously preferred. The generation unit can also suggest scents suitable for specific scenes based on the user's past olfactory history. Furthermore, the generation unit can analyze the user's olfactory history and generate scents according to the season or time of day. This allows the generation unit to generate an optimal scent based on the user's past olfactory history.

[0044] When generating the scent, the generation unit can adjust the scent intensity based on the user's current activity status. For example, if the user is relaxed, the generation unit sets the scent intensity to low. Also, if the user is exercising, the generation unit can set the scent intensity to high. Furthermore, if the user is concentrating, the generation unit can set the scent intensity to medium. This allows the generation unit to adjust the scent intensity according to the user's activity status.

[0045] When generating a scent, the generation unit can generate an optimal scent by taking into account the user's geographical location information. For example, if the user is at the seaside, the generation unit can generate a sea breeze scent. If the user is in the mountains, the generation unit can also generate a forest scent. Furthermore, if the user is in an urban area, the generation unit can also generate a flower or fruit scent. This allows the generation unit to generate an optimal scent based on the user's geographical location information.

[0046] When generating a scent, the generation unit can analyze the user's social media activity and generate a related scent. For example, the generation unit can generate a scent of a place that the user has shared on social media. The generation unit can also generate a scent related to a brand or event that the user follows on social media. Furthermore, the generation unit can also generate a scent of a place that the user has checked in to on social media. This allows the generation unit to generate a related scent based on the user's social media activity.

[0047] When providing a scent, the providing unit can select the optimal scent providing method by referring to the user's past olfactory history. For example, the providing unit selects the optimal scent providing method based on the scent providing methods that the user has previously preferred. The providing unit can also suggest a scent providing method suitable for a specific scene based on the user's olfactory history. Furthermore, the providing unit can analyze the user's olfactory history and select a scent providing method according to the season or time of day. This allows the providing unit to select the optimal scent providing method based on the user's past olfactory history.

[0048] When providing the scent, the providing unit can adjust the timing of providing the scent based on the user's current activity status. For example, if the user is relaxed, the providing unit can set the timing of providing the scent later. Also, if the user is exercising, the providing unit can set the timing of providing the scent earlier. Furthermore, if the user is concentrating, the providing unit can set the timing of providing the scent to a medium level. This allows the providing unit to adjust the timing of providing the scent according to the user's activity status.

[0049] When providing a scent, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, if the user is at the seaside, the providing unit can intermittently provide a sea breeze scent. If the user is in the mountains, the providing unit can also continuously provide a forest scent. Furthermore, if the user is in an urban area, the providing unit can intermittently provide a flower or fruit scent. This allows the providing unit to select the optimal providing method based on the user's geographical location information.

[0050] The provision unit can analyze the user's social media activity when providing the scent and suggest a related scent provision method. For example, the provision unit can intermittently provide the scent of a location the user has shared on social media. The provision unit can also continuously provide a scent related to a brand or event the user follows on social media. Furthermore, the provision unit can intermittently provide the scent of a location the user has checked in on social media. This allows the provision unit to suggest a related scent provision method based on the user's social media activity.

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

[0052] The reception unit can also receive health information of the user. The health information includes, for example, heart rate, blood pressure, body temperature, stress level, etc. The reception unit can measure the user's heart rate using, for example, a wearable device. The reception unit can also measure the user's blood pressure using a blood pressure monitor. The reception unit can also measure the user's body temperature using a thermometer. This allows the reception unit to receive the user's health information.

[0053] The generation unit can generate a fragrance based on the health information received by the reception unit. For example, if the user's heart rate is high, the generation unit can generate a fragrance with a relaxing effect. Furthermore, if the user's blood pressure is high, the generation unit can generate a fragrance with a calming effect. Furthermore, if the user's body temperature is high, the generation unit can generate a fragrance that provides a cooling sensation. This allows the generation unit to generate an appropriate fragrance based on the health information.

[0054] The reception unit can analyze the user's past olfactory history and select the optimal method for acquiring location information. The reception unit can optimize the timing for acquiring location information based on, for example, places that the user has frequently visited in the past. The reception unit can also analyze the user's movement patterns and suggest an efficient method for acquiring location information. Furthermore, the reception unit can adjust the frequency of acquiring location information during a specific time period based on the user's past movement history. This allows the reception unit to optimize the method for acquiring location information based on the user's past movement history.

[0055] When generating a scent, the generation unit can generate an optimal scent by referring to the user's past olfactory history. The generation unit generates an optimal scent based on, for example, scents that the user has previously preferred. The generation unit can also suggest scents suitable for specific scenes based on the user's past olfactory history. Furthermore, the generation unit can analyze the user's olfactory history and generate scents according to the season or time of day. This allows the generation unit to generate an optimal scent based on the user's past olfactory history.

[0056] When providing the scent, the providing unit can adjust the timing of providing the scent based on the user's current activity status. For example, if the user is relaxed, the providing unit can set the timing of providing the scent later. Also, if the user is exercising, the providing unit can set the timing of providing the scent earlier. Furthermore, if the user is concentrating, the providing unit can set the timing of providing the scent to a medium level. This allows the providing unit to adjust the timing of providing the scent according to the user's activity status.

[0057] The reception unit can analyze the user's social media activity and acquire related information. For example, the reception unit prioritizes acquiring location information of places where the user has checked in on social media. The reception unit can also prioritize acquiring location information of places and events that the user is following on social media. Furthermore, the reception unit can also prioritize acquiring location information of places that the user has shared on social media. This allows the reception unit to acquire related information based on the user's social media activity.

[0058] When generating a scent, the generation unit can generate an optimal scent by taking into account the user's geographical location information. For example, if the user is at the seaside, the generation unit can generate a sea breeze scent. If the user is in the mountains, the generation unit can also generate a forest scent. Furthermore, if the user is in an urban area, the generation unit can also generate a flower or fruit scent. This allows the generation unit to generate an optimal scent based on the user's geographical location information.

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

[0060] Step 1: The reception unit receives location information of the user. The location information includes, for example, GPS information, Wi-Fi location information, beacon information, etc. The reception unit can identify the user's current location using the GPS information, identify an indoor location using the Wi-Fi location information, and identify a location within a specific area using the beacon information. Step 2: The generator generates a fragrance based on the location information received by the receiver. The fragrance generation includes the type of fragrance, the generation process, the generation device, etc. The generator generates a fragrance by mixing specific fragrances, and can adjust the intensity and duration of the fragrance to customize the type of fragrance to suit the user's preferences. Step 3: The providing unit provides the scent generated by the generating unit to the user. The providing unit may use a scent diffuser, a smart device, a wearable device, or the like. The providing unit may diffuse the scent using a scent diffuser, control the scent using a smart device, or individually provide the scent using a wearable device.

[0061] (Example 2) An olfactory generation system according to an embodiment of the present invention generates and provides an aroma based on a user's location, environmental information, scene information, video information, audio information, and the like. When a user moves to a specific location, this olfactory generation system generates an aroma related to that location in real time. For example, when a user moves to a virtual flower field, a floral scent wafts through the air. This system uses an olfactory generation AI to generate an appropriate aroma based on the user's location and environmental information. Next, the system generates an aroma based on a specific event or scene in the game. For example, in a cooking scene in the game, a cooking aroma wafts through the air. This system also uses an olfactory generation AI to generate an appropriate aroma based on the game's progress and scene information. Furthermore, a system is provided that allows viewers of cooking shows to experience the aroma of cooking. For example, viewers can experience the aroma of cooking through a television or smartphone. This system uses an olfactory generation AI to generate an appropriate aroma based on video and audio information. These systems are built around an olfactory generation AI and are expected to significantly improve the user experience. The olfactory generation AI receives inputs such as a user's location, environmental information, scene information, video information, and audio information, and generates an appropriate aroma. The generated scent is provided to the user through a dedicated device, allowing the olfactory generation system to generate and provide scents based on the user's location and environmental information.

[0062] An olfactory generation system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives location information of a user. The location information includes, but is not limited to, GPS information, Wi-Fi location information, and beacon information. The receiving unit identifies the user's current location using, for example, GPS information. The receiving unit can also identify the user's indoor location using Wi-Fi location information. The receiving unit can also identify the user's location within a specific area using beacon information. The generating unit generates a fragrance based on the location information received by the receiving unit. Examples of fragrance generation include, but are not limited to, the type of fragrance, the generation process, and the generation device. The generating unit generates a fragrance by, for example, mixing specific fragrances. The generating unit can also adjust the intensity and duration of the fragrance. The generating unit can also customize the type of fragrance to suit the user's preferences. The providing unit provides the fragrance generated by the generating unit to the user. Examples of fragrance generation include, but are not limited to, a fragrance diffuser, a smart device, a wearable device, and the like. The providing unit diffuses the scent using, for example, a scent diffuser. The providing unit can also control the scent using a smart device. Furthermore, the providing unit can also provide the scent individually using a wearable device. In this way, the olfactory generation system according to the embodiment can generate and provide the scent based on the user's location information.

[0063] The reception unit can receive environmental information about the user. The environmental information includes, but is not limited to, for example, temperature, humidity, illuminance, and sound. For example, the reception unit measures the ambient temperature using a temperature sensor. The reception unit can also measure the ambient humidity using a humidity sensor. Furthermore, the reception unit can measure the ambient brightness using an illuminance sensor. This allows the reception unit to receive environmental information about the user.

[0064] The generation unit can generate a fragrance based on the environmental information received by the reception unit. The generation unit can adjust the type of fragrance based on, for example, temperature information. For example, when the temperature is high, a refreshing fragrance can be generated. The generation unit can also adjust the intensity of the fragrance based on humidity information. For example, when the humidity is high, the intensity of the fragrance can be weakened. Furthermore, the generation unit can also adjust the duration of the fragrance based on illuminance information. For example, when the illuminance is low, the duration of the fragrance can be extended. This allows the generation unit to generate an appropriate fragrance based on the environmental information.

[0065] The reception unit can receive scene information of the user. The scene information includes, for example, the user's activity content, location, time period, etc., but is not limited to these examples. For example, the reception unit detects the user's activity content using a sensor. The reception unit can also identify the user's current location using GPS information. Furthermore, the reception unit can obtain the user's activity time period using clock information. This allows the reception unit to receive the user's scene information.

[0066] The generation unit can generate a fragrance based on the scene information received by the reception unit. The generation unit, for example, adjusts the type of fragrance based on the user's activity. For example, if the user is relaxing, it can generate a fragrance that has a relaxing effect. The generation unit can also adjust the intensity of the fragrance based on the user's current location. For example, if the user is outdoors, it can increase the intensity of the fragrance. Furthermore, the generation unit can adjust the duration of the fragrance based on the time period during which the user is active. For example, if the user is active at night, it can increase the duration of the fragrance. This allows the generation unit to generate an appropriate fragrance based on the scene information.

[0067] The reception unit can receive video information. Video information includes, but is not limited to, camera footage, recorded footage, and live footage, for example. The reception unit can obtain real-time footage using a camera, for example. The reception unit can also play back recorded footage. Furthermore, the reception unit can receive live footage via the Internet. This allows the reception unit to receive video information.

[0068] The generation unit can generate a fragrance based on the video information received by the reception unit. The generation unit can adjust the type of fragrance based on, for example, camera footage. For example, if a flower is captured in the video, a floral fragrance can be generated. The generation unit can also adjust the intensity of the fragrance based on recorded video. For example, if the video is dark, the intensity of the fragrance can be weakened. Furthermore, the generation unit can adjust the duration of the fragrance based on live video. For example, if the video continues for a long period of time, the duration of the fragrance can be extended. This allows the generation unit to generate an appropriate fragrance based on the video information.

[0069] The reception unit can receive audio information. Examples of audio information include, but are not limited to, microphone recordings, audio files, and real-time audio. The reception unit can obtain real-time audio using, for example, a microphone. The reception unit can also play back recorded audio files. Furthermore, the reception unit can receive real-time audio via the Internet. This allows the reception unit to receive audio information.

[0070] The generation unit can generate a fragrance based on the audio information received by the reception unit. The generation unit can adjust the type of fragrance based on, for example, microphone recording. For example, if the audio includes the sound of cooking, the generation unit can generate the aroma of cooking. The generation unit can also adjust the intensity of the fragrance based on the audio file. For example, if the audio is loud, the intensity of the fragrance can be increased. Furthermore, the generation unit can adjust the duration of the fragrance based on real-time audio. For example, if the audio continues for a long time, the duration of the fragrance can be extended. This allows the generation unit to generate an appropriate fragrance based on the audio information.

[0071] The providing unit can provide the scent to the user through a dedicated device. Examples of dedicated devices include, but are not limited to, a scent diffuser, a smart device, and a wearable device. The providing unit can diffuse the scent using, for example, a scent diffuser. The providing unit can also control the scent using a smart device. Furthermore, the providing unit can also provide the scent individually using a wearable device. This allows the providing unit to provide the scent through a dedicated device, thereby improving the user experience.

[0072] The reception unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user emotions. For example, if the user is relaxed, the reception unit can set the frequency of acquiring location information low to reduce the load on the system. Furthermore, if the user is excited, the reception unit can set the frequency of acquiring location information high to enhance real-time fragrance generation. Furthermore, if the user is feeling stressed, the reception unit can adjust the timing of acquiring location information to match the user's pace. This allows the reception unit to adjust the timing of acquiring location information according to the user's emotions.

[0073] The reception unit can analyze the user's past movement history and select the optimal method for acquiring location information. The reception unit can optimize the timing for acquiring location information based on, for example, places that the user has frequently visited in the past. The reception unit can also analyze the user's movement patterns and suggest an efficient method for acquiring location information. Furthermore, the reception unit can adjust the frequency of acquiring location information in a specific time period based on the user's past movement history. This allows the reception unit to optimize the method for acquiring location information based on the user's past movement history.

[0074] The reception unit can perform filtering based on the user's current activity status when acquiring location information. For example, when the user is walking, the reception unit sets the frequency of acquiring location information to be high. Furthermore, when the user is standing still, the reception unit can also set the frequency of acquiring location information to be low. Furthermore, when the user is driving a vehicle, the reception unit can temporarily stop acquiring location information. In this way, the reception unit can adjust the frequency of acquiring location information according to the user's activity status.

[0075] The reception unit can estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user's emotions. For example, when the user is relaxed, the reception unit can prioritize acquiring location information of tourist spots and natural environments. Furthermore, when the user is in a hurry, the reception unit can also prioritize acquiring location information of public transportation facilities and major roads. Furthermore, when the user is excited, the reception unit can also prioritize acquiring location information of event venues and amusement facilities. In this way, the reception unit can determine the priority of location information according to the user's emotions.

[0076] When acquiring location information, the reception unit can prioritize acquiring highly relevant information in consideration of the user's geographical location information. For example, if the user is in an urban area, the reception unit prioritizes acquiring location information of surrounding commercial facilities and tourist spots. In addition, if the user is in a suburban area, the reception unit can also prioritize acquiring location information of natural environments and recreational facilities. Furthermore, if the user is in a specific area, the reception unit can also prioritize acquiring event information and location information of local specialties. This allows the reception unit to prioritize acquiring highly relevant information based on the user's geographical location information.

[0077] When acquiring location information, the reception unit can analyze the user's social media activity and acquire related information. For example, the reception unit prioritizes acquiring location information of places where the user has checked in on social media. The reception unit can also prioritize acquiring location information of places and events that the user is following on social media. Furthermore, the reception unit can also prioritize acquiring location information of places that the user has shared on social media. This allows the reception unit to acquire related information based on the user's social media activity.

[0078] The generation unit can estimate the user's emotions and adjust the type of scent to be generated based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a relaxing scent such as lavender or chamomile. If the user is excited, the generation unit can also generate a refreshing scent such as citrus or mint. Furthermore, if the user is stressed, the generation unit can also generate a calming scent such as vanilla or sandalwood. This allows the generation unit to adjust the type of scent according to the user's emotions.

[0079] When generating a scent, the generation unit can generate an optimal scent by referring to the user's past olfactory history. The generation unit generates an optimal scent based on, for example, scents that the user has previously preferred. The generation unit can also suggest scents suitable for specific scenes based on the user's past olfactory history. Furthermore, the generation unit can analyze the user's olfactory history and generate scents according to the season or time of day. This allows the generation unit to generate an optimal scent based on the user's past olfactory history.

[0080] When generating the scent, the generation unit can adjust the scent intensity based on the user's current activity status. For example, if the user is relaxed, the generation unit sets the scent intensity to low. Also, if the user is exercising, the generation unit can set the scent intensity to high. Furthermore, if the user is concentrating, the generation unit can set the scent intensity to medium. This allows the generation unit to adjust the scent intensity according to the user's activity status.

[0081] The generation unit can estimate the user's emotions and adjust the duration of the generated fragrance based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can set the duration of the fragrance to be long. If the user is in a hurry, the generation unit can also set the duration of the fragrance to be short. Furthermore, if the user is excited, the generation unit can set the duration of the fragrance to be medium. This allows the generation unit to adjust the duration of the fragrance according to the user's emotions.

[0082] When generating a scent, the generation unit can generate an optimal scent by taking into account the user's geographical location information. For example, if the user is at the seaside, the generation unit can generate a sea breeze scent. If the user is in the mountains, the generation unit can also generate a forest scent. Furthermore, if the user is in an urban area, the generation unit can also generate a flower or fruit scent. This allows the generation unit to generate an optimal scent based on the user's geographical location information.

[0083] When generating a scent, the generation unit can analyze the user's social media activity and generate a related scent. For example, the generation unit can generate a scent of a place that the user has shared on social media. The generation unit can also generate a scent related to a brand or event that the user follows on social media. Furthermore, the generation unit can also generate a scent of a place that the user has checked in to on social media. This allows the generation unit to generate a related scent based on the user's social media activity.

[0084] The providing unit can estimate the user's emotions and adjust the method of providing the aroma based on the estimated user's emotions. For example, the providing unit can diffuse the aroma slowly when the user is relaxed. The providing unit can also diffuse the aroma quickly when the user is excited. Furthermore, the providing unit can also diffuse the aroma intermittently when the user is feeling stressed. This allows the providing unit to adjust the method of providing the aroma according to the user's emotions.

[0085] When providing a scent, the providing unit can select the optimal scent providing method by referring to the user's past olfactory history. For example, the providing unit selects the optimal scent providing method based on the scent providing methods that the user has previously preferred. The providing unit can also suggest a scent providing method suitable for a specific scene based on the user's olfactory history. Furthermore, the providing unit can analyze the user's olfactory history and select a scent providing method according to the season or time of day. This allows the providing unit to select the optimal scent providing method based on the user's past olfactory history.

[0086] When providing the scent, the providing unit can adjust the timing of providing the scent based on the user's current activity status. For example, if the user is relaxed, the providing unit can set the timing of providing the scent later. Also, if the user is exercising, the providing unit can set the timing of providing the scent earlier. Furthermore, if the user is concentrating, the providing unit can set the timing of providing the scent to a medium level. This allows the providing unit to adjust the timing of providing the scent according to the user's activity status.

[0087] The providing unit can estimate the user's emotions and adjust the frequency of providing the aroma based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can set the frequency of providing the aroma low. Also, if the user is excited, the providing unit can set the frequency of providing the aroma high. Furthermore, if the user is feeling stressed, the providing unit can set the frequency of providing the aroma to a medium level. This allows the providing unit to adjust the frequency of providing the aroma according to the user's emotions.

[0088] When providing a scent, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, if the user is at the seaside, the providing unit can intermittently provide a sea breeze scent. If the user is in the mountains, the providing unit can also continuously provide a forest scent. Furthermore, if the user is in an urban area, the providing unit can intermittently provide a flower or fruit scent. This allows the providing unit to select the optimal providing method based on the user's geographical location information.

[0089] The provision unit can analyze the user's social media activity when providing the scent and suggest a related scent provision method. For example, the provision unit can intermittently provide the scent of a location the user has shared on social media. The provision unit can also continuously provide a scent related to a brand or event the user follows on social media. Furthermore, the provision unit can intermittently provide the scent of a location the user has checked in on social media. This allows the provision unit to suggest a related scent provision method based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives GPS information and Wi-Fi location information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a fragrance based on the received location information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated fragrance to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives GPS information and Wi-Fi location information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a scent based on the received location information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated scent to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives GPS information and Wi-Fi location information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a scent based on the received location information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated scent to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives GPS information and Wi-Fi location information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a scent based on the received location information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated scent to the user.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can also receive health information of the user. The health information includes, for example, heart rate, blood pressure, body temperature, stress level, etc. The reception unit can measure the user's heart rate using, for example, a wearable device. The reception unit can also measure the user's blood pressure using a blood pressure monitor. The reception unit can also measure the user's body temperature using a thermometer. This allows the reception unit to receive the user's health information.

[0092] The generation unit can generate a fragrance based on the health information received by the reception unit. For example, if the user's heart rate is high, the generation unit can generate a fragrance with a relaxing effect. Furthermore, if the user's blood pressure is high, the generation unit can generate a fragrance with a calming effect. Furthermore, if the user's body temperature is high, the generation unit can generate a fragrance that provides a cooling sensation. This allows the generation unit to generate an appropriate fragrance based on the health information.

[0093] The providing unit can estimate the user's emotions and adjust the method of providing the aroma based on the estimated user's emotions. For example, the providing unit can diffuse the aroma slowly when the user is relaxed. The providing unit can also diffuse the aroma quickly when the user is excited. Furthermore, the providing unit can also diffuse the aroma intermittently when the user is feeling stressed. This allows the providing unit to adjust the method of providing the aroma according to the user's emotions.

[0094] The reception unit can analyze the user's past olfactory history and select the optimal method for acquiring location information. The reception unit can optimize the timing for acquiring location information based on, for example, places that the user has frequently visited in the past. The reception unit can also analyze the user's movement patterns and suggest an efficient method for acquiring location information. Furthermore, the reception unit can adjust the frequency of acquiring location information during a specific time period based on the user's past movement history. This allows the reception unit to optimize the method for acquiring location information based on the user's past movement history.

[0095] When generating a scent, the generation unit can generate an optimal scent by referring to the user's past olfactory history. The generation unit generates an optimal scent based on, for example, scents that the user has previously preferred. The generation unit can also suggest scents suitable for specific scenes based on the user's past olfactory history. Furthermore, the generation unit can analyze the user's olfactory history and generate scents according to the season or time of day. This allows the generation unit to generate an optimal scent based on the user's past olfactory history.

[0096] When providing the scent, the providing unit can adjust the timing of providing the scent based on the user's current activity status. For example, if the user is relaxed, the providing unit can set the timing of providing the scent later. Also, if the user is exercising, the providing unit can set the timing of providing the scent earlier. Furthermore, if the user is concentrating, the providing unit can set the timing of providing the scent to a medium level. This allows the providing unit to adjust the timing of providing the scent according to the user's activity status.

[0097] The generation unit can estimate the user's emotions and adjust the type of scent to be generated based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a relaxing scent such as lavender or chamomile. If the user is excited, the generation unit can also generate a refreshing scent such as citrus or mint. Furthermore, if the user is stressed, the generation unit can also generate a calming scent such as vanilla or sandalwood. This allows the generation unit to adjust the type of scent according to the user's emotions.

[0098] The providing unit can estimate the user's emotions and adjust the frequency of providing the aroma based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can set the frequency of providing the aroma low. Also, if the user is excited, the providing unit can set the frequency of providing the aroma high. Furthermore, if the user is feeling stressed, the providing unit can set the frequency of providing the aroma to a medium level. This allows the providing unit to adjust the frequency of providing the aroma according to the user's emotions.

[0099] The reception unit can analyze the user's social media activity and acquire related information. For example, the reception unit prioritizes acquiring location information of places where the user has checked in on social media. The reception unit can also prioritize acquiring location information of places and events that the user is following on social media. Furthermore, the reception unit can also prioritize acquiring location information of places that the user has shared on social media. This allows the reception unit to acquire related information based on the user's social media activity.

[0100] When generating a scent, the generation unit can generate an optimal scent by taking into account the user's geographical location information. For example, if the user is at the seaside, the generation unit can generate a sea breeze scent. If the user is in the mountains, the generation unit can also generate a forest scent. Furthermore, if the user is in an urban area, the generation unit can also generate a flower or fruit scent. This allows the generation unit to generate an optimal scent based on the user's geographical location information.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives location information of the user. The location information includes, for example, GPS information, Wi-Fi location information, beacon information, etc. The reception unit can identify the user's current location using the GPS information, identify an indoor location using the Wi-Fi location information, and identify a location within a specific area using the beacon information. Step 2: The generator generates a fragrance based on the location information received by the receiver. The fragrance generation includes the type of fragrance, the generation process, the generation device, etc. The generator generates a fragrance by mixing specific fragrances, and can adjust the intensity and duration of the fragrance to customize the type of fragrance to suit the user's preferences. Step 3: The providing unit provides the scent generated by the generating unit to the user. The providing unit may use a scent diffuser, a smart device, a wearable device, or the like. The providing unit may diffuse the scent using a scent diffuser, control the scent using a smart device, or individually provide the scent using a wearable device.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[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.

[0174] [Explanation of symbols]

[0175] 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 reception unit that receives user location information; a generation unit that generates a fragrance based on the location information received by the reception unit; a providing unit that provides the scent generated by the generating unit to a user; Equipped with A system characterized by:

2. The reception unit Accepts user environment information The system of claim 1 .

3. The generation unit A fragrance is generated based on the environmental information received by the receiving unit. The system of claim 1 .

4. The reception unit Accepts user scene information The system of claim 1 .

5. The generation unit A fragrance is generated based on the scene information received by the receiving unit. The system of claim 1 .

6. The reception unit Accepts video information The system of claim 1 .

7. The generation unit A fragrance is generated based on the image information received by the receiving unit. The system of claim 1 .

8. The reception unit Accepts audio information The system of claim 1 .

Images