system

Abstract

The system according to this embodiment aims to model scent patterns on a large scale and utilize them in various businesses. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a construction unit, and a supply unit. The collection unit collects fragrance patterns. The analysis unit analyzes the fragrance patterns collected by the collection unit. The construction unit constructs an LSSM based on the data analyzed by the analysis unit. The supply unit provides fragrances using the LSSM constructed by the construction unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the pattern of fragrance has not been sufficiently modeled on a large scale and utilized in various businesses.

[0005] The system according to the embodiment aims to model the pattern of fragrance on a large scale and utilize it in various businesses.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a construction unit, and a supply unit. The collection unit collects fragrance patterns. The analysis unit analyzes the fragrance patterns collected by the collection unit. The construction unit constructs an LSSM based on the data analyzed by the analysis unit. The supply unit provides fragrances using the LSSM constructed by the construction unit. [Effects of the Invention]

[0007] The system according to this embodiment can model fragrance patterns on a large scale and be utilized in various businesses. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The olfactory sensing system according to an embodiment of the present invention is a system that allows AI to learn various scent patterns, model the sense of smell that humans can perceive on a large scale, and can be utilized in various businesses. By creating an LSSM (Large Sense of Smell Model), the olfactory sensing system can provide new services in the entertainment, room fragrance, and food and beverage fields such as sommelier and sake diploma programs. For example, the olfactory sensing system allows AI to learn various scent patterns. In this process, the olfactory sensing system collects scents that humans can perceive as data and analyzes it. For example, the olfactory sensing system collects various scents as data, such as the scents of flowers, fruits, and food, and the AI ​​learns scent patterns by analyzing this data. Next, the olfactory sensing system creates an LSSM based on the learned scent patterns. An LSSM is a large-scale olfactory sensing model, which allows the AI ​​to reproduce various scents. For example, olfactory sensing systems can provide a more realistic experience in movies and games by recreating scents that match specific scenes. They can also be used as room fragrances, providing scents tailored to specific themes. Furthermore, by utilizing LSSM (Long-Sensory Sound Management), olfactory sensing systems can offer new services in the food and beverage sector, such as sommelier and sake diploma programs. For instance, an AI-powered olfactory sensing system could diagnose the best alcoholic beverage pairings for a meal and suggest the optimal combination, enriching the dining experience. Thus, by training AI with scent patterns and creating large-scale olfactory sensing models, olfactory sensing systems can offer new services in the entertainment, room fragrance, and food and beverage sectors. This will advance the digitalization of smell and is expected to lead to applications in various fields. As a result, olfactory sensing systems can provide new services in a wide range of businesses.

[0029] The olfactory sensing system according to this embodiment comprises a collection unit, an analysis unit, a construction unit, and a provision unit. The collection unit collects scent patterns. The collection unit can collect data such as the scent of flowers, fruits, or food. For example, to collect the scent of flowers, the collection unit analyzes the components of flowers and collects the data. The collection unit can also analyze the components of fruits and collect the data to collect the scent of fruits. Furthermore, the collection unit can analyze the components of food and collect the data to collect the scent of food. The analysis unit analyzes the scent patterns collected by the collection unit. For example, the analysis unit can analyze the collected scent data and learn scent patterns. For example, the analysis unit can use a machine learning algorithm to analyze the collected scent data and learn scent patterns. The analysis unit can also use data analysis techniques to analyze the collected scent data and learn scent patterns. Furthermore, the analysis unit can adjust the algorithm used, analyze the collected scent data, and learn scent patterns. The construction unit constructs an LSSM based on the data analyzed by the analysis unit. For example, the construction unit can construct an LSSM based on the analyzed data. For example, the construction unit can select the data to be used to construct the LSSM and construct the LSSM. The construction unit can also select the algorithm to be used to construct the LSSM and construct the LSSM. Furthermore, the construction unit can adjust the data and algorithm used to construct the LSSM and construct the LSSM. The provision unit provides scents using the LSSM constructed by the construction unit. For example, the provision unit can reproduce scents that match specific scenes in movies or games. For example, the provision unit can provide scents related to a scene to match a specific scene in a movie. The provision unit can also provide scents related to a specific event in a game to match a specific event. Furthermore, the provision unit can also provide scents related to a theme to match a specific themed party. As a result, the olfactory sensing system according to this embodiment can provide new services in various businesses.

[0030] The collection unit collects aroma patterns. For example, it can collect data on floral scents, fruit scents, and food scents. Specifically, to collect floral scents, it analyzes the components of flowers and collects the data. Advanced analytical techniques such as gas chromatography and mass spectrometry can be used for flower component analysis. This allows for detailed identification of the chemical components that make up the floral scent, and the data can be stored digitally. Similarly, to collect fruit scents, it analyzes the components of fruits and collects the data. Fruit aroma components are also analyzed in detail using gas chromatography and mass spectrometry. For example, it can identify the aroma components of fruits such as apples and oranges and collect the data. Furthermore, to collect food scents, it analyzes the components of dishes and collects the data. Since food aroma components are produced by complex chemical reactions during the cooking process, a combination of multiple analytical techniques is necessary to analyze these components in detail. For example, it can collect aroma components from various dishes, such as the scent of freshly baked bread or the scent of spicy curry. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and construction units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the fragrance patterns collected by the collection unit. For example, the analysis unit can analyze collected fragrance data and learn fragrance patterns. Specifically, it can use machine learning algorithms to analyze collected fragrance data and learn fragrance patterns. For instance, it can use machine learning models such as neural networks and support vector machines to analyze fragrance data and identify specific fragrance patterns. It can also use data analysis techniques to analyze collected fragrance data and learn fragrance patterns. For example, it can use principal component analysis or clustering techniques to extract features from fragrance data and group similar fragrance patterns. Furthermore, the analysis unit can adjust the algorithms used to analyze collected fragrance data and learn fragrance patterns. For example, it can combine different algorithms to build hybrid models and improve analysis accuracy. By utilizing these techniques, the analysis unit can rapidly and accurately analyze collected fragrance data and learn fragrance patterns. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term fragrance trends and fluctuations. For example, by analyzing seasonal fluctuations in fragrances and the characteristics of fragrances in specific regions, it is possible to predict future fragrance trends. This allows the analysis unit to handle not only real-time fragrance analysis but also long-term fragrance trend analysis, improving the reliability and accuracy of the entire system.

[0032] The construction unit constructs the LSSM based on the data analyzed by the analysis unit. For example, the construction unit can construct the LSSM based on the analyzed data. Specifically, it selects the data to be used to construct the LSSM and then constructs it. For example, it designs and constructs an LSSM model based on the scent pattern data provided by the analysis unit. The construction unit can also select the algorithm to be used to construct the LSSM and then construct it. For example, it can use neural networks or deep learning algorithms to construct the LSSM model and reproduce scent patterns. Furthermore, the construction unit can adjust the data and algorithms used to construct the LSSM and then construct it. For example, it can combine different scent patterns to construct a model that reproduces more complex scents. By utilizing these methods, the construction unit can construct a highly accurate LSSM and reproduce scent patterns. Furthermore, the construction unit can evaluate the performance of the LSSM and improve or adjust the model as needed. For example, it can compare actual scent data with the scent reproduced by the LSSM and evaluate the reproduction accuracy. It can also improve the LSSM model based on user feedback to reproduce more natural and realistic scents. This allows the construction department to constantly utilize the latest technologies and data to build high-precision LSSMs and improve the overall system performance.

[0033] The service provider utilizes LSSMs (Long Scent Management Systems) built by the development provider to deliver fragrances. For example, the service provider can recreate fragrances tailored to specific scenes in movies and games. Specifically, it can provide fragrances related to specific scenes in movies. For instance, if a movie features a flower field scene, it can recreate the scent of flowers to match the scene, providing the audience with a sense of realism. It can also provide fragrances related to specific events in games. For example, if a game features a cooking scene, it can recreate the scent of cooking to match the scene, providing players with a realistic experience. Furthermore, the service provider can provide fragrances related to specific themed parties. For example, at a Halloween party, it can provide pumpkin and cinnamon scents to create a seasonal atmosphere for participants. The service provider can control these fragrances in real time, delivering them at the appropriate time to match the scene or event. In addition, the service provider can collect user feedback and continuously improve the methods and content of fragrance delivery. For example, it can adjust the strength and duration of the fragrance to provide scents tailored to user preferences. The service provider can also combine multiple fragrances to recreate more complex and realistic scents. This allows the service provider to offer users an immersive fragrance experience and offer new services across various businesses.

[0034] The collection unit can collect data on the scents of flowers, fruits, and food. For example, to collect the scent of flowers, the collection unit can analyze the components of flowers and collect that data. The collection unit can also analyze the components of fruits and collect that data to collect the scent of fruits. Furthermore, the collection unit can analyze the components of food and collect that data to collect the scent of food. By collecting diverse scent data in this way, the accuracy of LSSM is improved. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input flower component data into a generating AI, which can then analyze the flower scent patterns.

[0035] The analysis unit can analyze the collected scent data and learn scent patterns. For example, the analysis unit can analyze the collected scent data and learn scent patterns. For example, the analysis unit can use a machine learning algorithm to analyze the collected scent data and learn scent patterns. The analysis unit can also use data analysis techniques to analyze the collected scent data and learn scent patterns. Furthermore, the analysis unit can adjust the algorithm used to analyze the collected scent data and learn scent patterns. This makes it possible to construct an LSSM by learning scent patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected scent data into a generating AI, which can then learn scent patterns.

[0036] The construction unit can construct LSSMs based on the analyzed data. For example, the construction unit can construct an LSSM based on the analyzed data. For example, the construction unit can select data to be used to construct an LSSM and construct the LSSM. The construction unit can also select an algorithm to be used to construct an LSSM and construct the LSSM. Furthermore, the construction unit can adjust the data and algorithm used to construct an LSSM and construct the LSSM. In this way, various scents can be reproduced by constructing an LSSM. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input the analyzed data into a generating AI, and the generating AI can construct an LSSM.

[0037] The fragrance delivery unit can recreate scents that match specific scenes in movies and games. For example, the unit can provide a scent related to a specific scene in a movie. The unit can also provide a scent related to a specific event in a game. Furthermore, the unit can provide a scent related to a specific themed party. By recreating scents that match specific scenes, a more realistic experience can be provided. Some or all of the above processing in the fragrance delivery unit may be performed using AI, for example, or without AI. For example, the fragrance delivery unit can input movie scene data into a generating AI, and the generating AI can generate a scent that matches the scene.

[0038] The serving department can diagnose which alcoholic beverages are best suited to a dish and suggest the optimal combination. The serving department can, for example, diagnose which alcoholic beverages are best suited to a dish and suggest the optimal combination. The serving department can, for example, diagnose which alcoholic beverages are best suited to a dish and suggest the optimal combination. Furthermore, the serving department can also diagnose which alcoholic beverages are best suited to a dish and suggest the optimal combination. In this way, the suggestion of alcoholic beverages that are best suited to the dish enriches the dining experience. Some or all of the above processing in the serving department may be performed using AI, for example, or without AI. For example, the serving department can input dish data into a generating AI, and the generating AI can suggest the optimal combination.

[0039] The collection unit can select the optimal collection method when collecting fragrances, taking into account the temperature and humidity of the environment. For example, in a high-temperature and high-humidity environment, the collection unit can adjust the collection method considering the volatility of the fragrance. The collection unit can also adjust the collection method considering the preservation of the fragrance in a low-temperature and dry environment. Furthermore, in a moderate temperature and humidity environment, the collection unit can select a collection method considering the balance of the fragrance. This ensures that the quality of the fragrance is maintained by selecting the optimal collection method according to the environmental conditions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input environmental temperature and humidity data into a generating AI, which can then select the optimal collection method.

[0040] The collection unit can filter the types of scents based on specific events or seasons when collecting scents. For example, in the spring, the collection unit may prioritize collecting floral scents. In addition, the collection unit may prioritize collecting fruity scents in the summer. Furthermore, during specific events (for example, Christmas), the collection unit may collect scents related to the event. This allows for the collection of appropriate scents through filtering according to events and seasons. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input event and seasonal data into a generating AI, which can then filter the types of scents.

[0041] The collection unit can prioritize the collection of region-specific scents by considering the user's geographical location information during scent collection. For example, if the user is in Japan, the collection unit can collect scents of flowers and fruits unique to Japan. The collection unit can also collect scents of French cuisine and wine unique to France if the user is in France. Furthermore, if the user is in the United States, the collection unit can collect scents unique to the United States. In this way, region-specific scents can be collected by collecting scents based on geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI, which can then collect region-specific scents.

[0042] The collection unit can analyze the user's social media activity when collecting scents and collect relevant scents. For example, if the user posts many photos of flowers on social media, the collection unit can collect flower scents. The collection unit can also collect food scents if the user posts many photos of food on social media. Furthermore, if the user posts many photos of travel on social media, the collection unit can collect scents of travel destinations. This allows for the collection of scents tailored to the user's preferences through scent collection based on social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI, which can then collect relevant scents.

[0043] The analysis unit can adjust the level of detail of the analysis based on the intensity and duration of the fragrance. For example, in the case of a strong fragrance, the analysis unit performs a detailed analysis, meticulously analyzing the fragrance components. The analysis unit can also perform a simplified analysis in the case of a weak fragrance, analyzing only the main components. Furthermore, in the case of a long-lasting fragrance, the analysis unit can also perform a detailed analysis of the changes in the fragrance over time. This allows for appropriate analysis by adjusting the level of detail of the analysis according to the intensity and duration of the fragrance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fragrance intensity and duration data into a generating AI, which can then adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the fragrance during analysis. For example, in the case of a floral fragrance, the analysis unit can apply an algorithm that analyzes components specific to flowers. The analysis unit can also apply an algorithm that analyzes components specific to fruits in the case of fruit fragrances. Furthermore, in the case of a food fragrance, the analysis unit can apply an algorithm that analyzes components specific to dishes. This enables highly accurate analysis by applying analysis algorithms according to the category of fragrance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fragrance category data into a generating AI, and the generating AI can apply different analysis algorithms.

[0045] The analysis unit can adjust the order of analysis based on the time of fragrance collection. For example, the analysis unit may prioritize the analysis of fragrances collected in spring. The analysis unit may also prioritize the analysis of fragrances collected in summer. Furthermore, the analysis unit may also prioritize the analysis of fragrances collected in autumn. This allows for appropriate analysis by adjusting the order of analysis based on the time of fragrance collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fragrance collection time data into a generating AI, which can then adjust the order of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relationships between scents during the analysis. For example, the analysis unit may prioritize the analysis of scents in the same category. The analysis unit can also group related scents together for analysis. Furthermore, the analysis unit can determine the order of analysis by considering the interrelationships between scents. This allows for appropriate analysis by adjusting the order of analysis based on the relationships between scents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scent relationship data into a generating AI, which can then adjust the order of analysis.

[0047] The construction unit can select the optimal construction algorithm by referring to past scent data when constructing an LSSM. For example, the construction unit can select an algorithm specialized for floral scents by referring to previously collected floral scent data. The construction unit can also select an algorithm specialized for fruit scents by referring to previously collected fruit scent data. Furthermore, the construction unit can select an algorithm specialized for food scents by referring to previously collected food scent data. In this way, the optimal construction algorithm can be selected by referring to past scent data. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input past scent data into a generation AI, and the generation AI can select the optimal construction algorithm.

[0048] The construction unit can improve the accuracy of LSSM construction by considering the interrelationships of scents. For example, the construction unit can construct an LSSM by considering the interrelationships of floral scents and fruit scents. The construction unit can also construct an LSSM by considering the interrelationships of food scents and beverage scents. Furthermore, the construction unit can analyze the interrelationships of scents and construct an LSSM by considering the optimal combination. This improves the accuracy of LSSM construction by considering the interrelationships of scents. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input the interrelationship data of scents into a generating AI, which can then improve the accuracy of the construction.

[0049] The construction unit can construct LSSMs while considering the geographical distribution of scents. For example, the construction unit can construct LSSMs with an emphasis on Japanese scents. The construction unit can also construct LSSMs with an emphasis on French scents. Furthermore, the construction unit can also construct LSSMs with an emphasis on American scents. By considering the geographical distribution of scents, it is possible to construct LSSMs that reflect regionally specific scents. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input geographical distribution data of scents into a generating AI, and the generating AI can perform the construction.

[0050] The construction unit can improve the accuracy of LSSM construction by referring to relevant literature on fragrances. For example, the construction unit can construct an LSSM by referring to literature on floral fragrances. The construction unit can also construct an LSSM by referring to literature on fruit fragrances. Furthermore, the construction unit can construct an LSSM by referring to literature on culinary fragrances. This improves the accuracy of LSSM construction by referring to relevant literature on fragrances. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input data on relevant literature on fragrances into a generating AI, which can then improve the accuracy of the construction.

[0051] The fragrance delivery unit can customize the fragrances it delivers based on specific scenes or events. For example, it can deliver a fragrance related to a specific scene in a movie. It can also deliver a fragrance related to a specific event in a game. Furthermore, it can deliver a fragrance related to a specific themed party. This allows for the delivery of appropriate fragrances by customizing them according to specific scenes or events. Some or all of the above processing in the fragrance delivery unit may be performed using AI, for example, or not. For example, the fragrance delivery unit can input scene or event data into a generating AI, which can then customize the fragrances.

[0052] The fragrance dispenser can provide the most suitable fragrance by referring to the user's past fragrance preferences. For example, the dispenser can provide the fragrance of a flower the user has liked in the past. The dispenser can also provide the fragrance of a fruit the user has liked in the past. Furthermore, the dispenser can provide the fragrance of a dish the user has liked in the past. This allows the dispenser to provide an appropriate fragrance based on the user's past fragrance preferences. Some or all of the above processing in the dispenser may be performed using AI, for example, or without AI. For example, the dispenser can input the user's past fragrance preference data into a generating AI, which can then provide the most suitable fragrance.

[0053] The service provider can provide the most suitable fragrance by considering the user's geographical location. For example, if the user is in Japan, the service provider can provide fragrances of flowers and fruits unique to Japan. The service provider can also provide fragrances of French cuisine and wine unique to France if the user is in France. Furthermore, if the user is in the United States, the service provider can provide fragrances unique to the United States. This allows for the provision of regionally specific fragrances based on geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, which can then provide the most suitable fragrance.

[0054] The service provider can analyze a user's social media activity and provide relevant scents when providing fragrances. For example, if a user posts many photos of flowers on social media, the service provider can provide a floral scent. The service provider can also provide a cooking scent if a user posts many photos of food on social media. Furthermore, if a user posts many photos of travel on social media, the service provider can provide a travel destination scent. This allows the service provider to provide fragrances that match the user's preferences by providing fragrances based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI, which can then provide relevant fragrances.

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

[0056] The olfactory sensing system can also include a personalization unit that learns the user's olfactory preferences. This personalization unit learns the scents the user has previously selected and the patterns of scents they prefer, enabling it to provide the most suitable scent for each individual user. For example, the personalization unit can record the floral scents the user has previously chosen and reflect this in future scent suggestions. It can also learn the scents the user prefers in specific seasons and suggest the most suitable scent for each season. Furthermore, the personalization unit can track changes in the user's sense of smell and adapt to evolving olfactory preferences over time. This makes it possible to provide personalized scents based on the user's olfactory preferences.

[0057] The olfactory sensing system may also include a health monitoring unit that monitors the user's health condition. The health monitoring unit can adjust the scent provided based on the user's health status. For example, if the user has a cold, the health monitoring unit can provide a scent that alleviates cold symptoms. It can also provide a stress-reducing scent if the user is stressed. Furthermore, if the user wants to relax, the health monitoring unit can provide a relaxing scent. This makes it possible to provide scents tailored to the user's health condition.

[0058] The olfactory sensing system can also include an activity monitoring unit that collects user activity data and provides scents tailored to that activity. For example, if the user is exercising, the activity monitoring unit can provide a scent that has a refreshing effect after exercise. It can also provide a scent that enhances concentration if the user is reading. Furthermore, if the user is relaxing, the activity monitoring unit can provide a scent that promotes relaxation. This makes it possible to provide scents that are tailored to the user's activity.

[0059] The olfactory sensing system may further include a sensitivity measuring unit that measures the user's olfactory sensitivity and adjusts the fragrance intensity accordingly. For example, if the user's sense of smell is sensitive, the sensitivity measuring unit may reduce the fragrance intensity. Conversely, if the user's sense of smell is less sensitive, the sensitivity measuring unit may increase the fragrance intensity. Furthermore, the sensitivity measuring unit can adjust the fragrance intensity in response to changes in the user's sense of smell. This makes it possible to provide fragrances tailored to the user's olfactory sensitivity.

[0060] The olfactory sensing system can also include a personalization unit that learns the user's olfactory preferences. This personalization unit learns the scents the user has previously selected and the patterns of scents they prefer, enabling it to provide the most suitable scent for each individual user. For example, the personalization unit can record the floral scents the user has previously chosen and reflect this in future scent suggestions. It can also learn the scents the user prefers in specific seasons and suggest the most suitable scent for each season. Furthermore, the personalization unit can track changes in the user's sense of smell and adapt to evolving olfactory preferences over time. This makes it possible to provide personalized scents based on the user's olfactory preferences.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects scent patterns. The collection unit can collect data on scents such as floral scents, fruit scents, and food scents. The collection unit analyzes the components of flowers and collects that data. It can also analyze the components of fruits and collect that data. Furthermore, it can analyze the components of food and collect that data. Step 2: The analysis unit analyzes the scent patterns collected by the collection unit. The analysis unit analyzes the collected scent data and learns the scent patterns. For example, machine learning algorithms and data analysis techniques can be used to analyze the collected scent data and learn the scent patterns. Step 3: The construction unit constructs the LSSM based on the data analyzed by the analysis unit. The construction unit constructs the LSSM based on the analyzed data and selects and adjusts the data and algorithms to be used in constructing the LSSM. Step 4: The service department provides fragrances using the LSSM built by the construction department. The service department can, for example, recreate fragrances to match specific scenes or events in movies or games. This enables the provision of new services in various businesses.

[0063] (Example of form 2) The olfactory sensing system according to an embodiment of the present invention is a system that allows AI to learn various scent patterns, model the sense of smell that humans can perceive on a large scale, and can be utilized in various businesses. By creating an LSSM (Large Sense of Smell Model), the olfactory sensing system can provide new services in the entertainment, room fragrance, and food and beverage fields such as sommelier and sake diploma programs. For example, the olfactory sensing system allows AI to learn various scent patterns. In this process, the olfactory sensing system collects scents that humans can perceive as data and analyzes it. For example, the olfactory sensing system collects various scents as data, such as the scents of flowers, fruits, and food, and the AI ​​learns scent patterns by analyzing this data. Next, the olfactory sensing system creates an LSSM based on the learned scent patterns. An LSSM is a large-scale olfactory sensing model, which allows the AI ​​to reproduce various scents. For example, olfactory sensing systems can provide a more realistic experience in movies and games by recreating scents that match specific scenes. They can also be used as room fragrances, providing scents tailored to specific themes. Furthermore, by utilizing LSSM (Long-Sensory Sound Management), olfactory sensing systems can offer new services in the food and beverage sector, such as sommelier and sake diploma programs. For instance, an AI-powered olfactory sensing system could diagnose the best alcoholic beverage pairings for a meal and suggest the optimal combination, enriching the dining experience. Thus, by training AI with scent patterns and creating large-scale olfactory sensing models, olfactory sensing systems can offer new services in the entertainment, room fragrance, and food and beverage sectors. This will advance the digitalization of smell and is expected to lead to applications in various fields. As a result, olfactory sensing systems can provide new services in a wide range of businesses.

[0064] The olfactory sensing system according to this embodiment comprises a collection unit, an analysis unit, a construction unit, and a provision unit. The collection unit collects scent patterns. The collection unit can collect data such as the scent of flowers, fruits, or food. For example, to collect the scent of flowers, the collection unit analyzes the components of flowers and collects the data. The collection unit can also analyze the components of fruits and collect the data to collect the scent of fruits. Furthermore, the collection unit can analyze the components of food and collect the data to collect the scent of food. The analysis unit analyzes the scent patterns collected by the collection unit. For example, the analysis unit can analyze the collected scent data and learn scent patterns. For example, the analysis unit can use a machine learning algorithm to analyze the collected scent data and learn scent patterns. The analysis unit can also use data analysis techniques to analyze the collected scent data and learn scent patterns. Furthermore, the analysis unit can adjust the algorithm used, analyze the collected scent data, and learn scent patterns. The construction unit constructs an LSSM based on the data analyzed by the analysis unit. For example, the construction unit can construct an LSSM based on the analyzed data. For example, the construction unit can select the data to be used to construct the LSSM and construct the LSSM. The construction unit can also select the algorithm to be used to construct the LSSM and construct the LSSM. Furthermore, the construction unit can adjust the data and algorithm used to construct the LSSM and construct the LSSM. The provision unit provides scents using the LSSM constructed by the construction unit. For example, the provision unit can reproduce scents that match specific scenes in movies or games. For example, the provision unit can provide scents related to a scene to match a specific scene in a movie. The provision unit can also provide scents related to a specific event in a game to match a specific event. Furthermore, the provision unit can also provide scents related to a theme to match a specific themed party. As a result, the olfactory sensing system according to this embodiment can provide new services in various businesses.

[0065] The collection unit collects aroma patterns. For example, it can collect data on floral scents, fruit scents, and food scents. Specifically, to collect floral scents, it analyzes the components of flowers and collects the data. Advanced analytical techniques such as gas chromatography and mass spectrometry can be used for flower component analysis. This allows for detailed identification of the chemical components that make up the floral scent, and the data can be stored digitally. Similarly, to collect fruit scents, it analyzes the components of fruits and collects the data. Fruit aroma components are also analyzed in detail using gas chromatography and mass spectrometry. For example, it can identify the aroma components of fruits such as apples and oranges and collect the data. Furthermore, to collect food scents, it analyzes the components of dishes and collects the data. Since food aroma components are produced by complex chemical reactions during the cooking process, a combination of multiple analytical techniques is necessary to analyze these components in detail. For example, it can collect aroma components from various dishes, such as the scent of freshly baked bread or the scent of spicy curry. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and construction units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the fragrance patterns collected by the collection unit. For example, the analysis unit can analyze collected fragrance data and learn fragrance patterns. Specifically, it can use machine learning algorithms to analyze collected fragrance data and learn fragrance patterns. For instance, it can use machine learning models such as neural networks and support vector machines to analyze fragrance data and identify specific fragrance patterns. It can also use data analysis techniques to analyze collected fragrance data and learn fragrance patterns. For example, it can use principal component analysis or clustering techniques to extract features from fragrance data and group similar fragrance patterns. Furthermore, the analysis unit can adjust the algorithms used to analyze collected fragrance data and learn fragrance patterns. For example, it can combine different algorithms to build hybrid models and improve analysis accuracy. By utilizing these techniques, the analysis unit can rapidly and accurately analyze collected fragrance data and learn fragrance patterns. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term fragrance trends and fluctuations. For example, by analyzing seasonal fluctuations in fragrances and the characteristics of fragrances in specific regions, it is possible to predict future fragrance trends. This allows the analysis unit to handle not only real-time fragrance analysis but also long-term fragrance trend analysis, improving the reliability and accuracy of the entire system.

[0067] The construction unit constructs the LSSM based on the data analyzed by the analysis unit. For example, the construction unit can construct the LSSM based on the analyzed data. Specifically, it selects the data to be used to construct the LSSM and then constructs it. For example, it designs and constructs an LSSM model based on the scent pattern data provided by the analysis unit. The construction unit can also select the algorithm to be used to construct the LSSM and then construct it. For example, it can use neural networks or deep learning algorithms to construct the LSSM model and reproduce scent patterns. Furthermore, the construction unit can adjust the data and algorithms used to construct the LSSM and then construct it. For example, it can combine different scent patterns to construct a model that reproduces more complex scents. By utilizing these methods, the construction unit can construct a highly accurate LSSM and reproduce scent patterns. Furthermore, the construction unit can evaluate the performance of the LSSM and improve or adjust the model as needed. For example, it can compare actual scent data with the scent reproduced by the LSSM and evaluate the reproduction accuracy. It can also improve the LSSM model based on user feedback to reproduce more natural and realistic scents. This allows the construction department to constantly utilize the latest technologies and data to build high-precision LSSMs and improve the overall system performance.

[0068] The service provider utilizes LSSMs (Long Scent Management Systems) built by the development provider to deliver fragrances. For example, the service provider can recreate fragrances tailored to specific scenes in movies and games. Specifically, it can provide fragrances related to specific scenes in movies. For instance, if a movie features a flower field scene, it can recreate the scent of flowers to match the scene, providing the audience with a sense of realism. It can also provide fragrances related to specific events in games. For example, if a game features a cooking scene, it can recreate the scent of cooking to match the scene, providing players with a realistic experience. Furthermore, the service provider can provide fragrances related to specific themed parties. For example, at a Halloween party, it can provide pumpkin and cinnamon scents to create a seasonal atmosphere for participants. The service provider can control these fragrances in real time, delivering them at the appropriate time to match the scene or event. In addition, the service provider can collect user feedback and continuously improve the methods and content of fragrance delivery. For example, it can adjust the strength and duration of the fragrance to provide scents tailored to user preferences. The service provider can also combine multiple fragrances to recreate more complex and realistic scents. This allows the service provider to offer users an immersive fragrance experience and offer new services across various businesses.

[0069] The collection unit can collect data on the scents of flowers, fruits, and food. For example, to collect the scent of flowers, the collection unit can analyze the components of flowers and collect that data. The collection unit can also analyze the components of fruits and collect that data to collect the scent of fruits. Furthermore, the collection unit can analyze the components of food and collect that data to collect the scent of food. By collecting diverse scent data in this way, the accuracy of LSSM is improved. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input flower component data into a generating AI, which can then analyze the flower scent patterns.

[0070] The analysis unit can analyze the collected scent data and learn scent patterns. For example, the analysis unit can analyze the collected scent data and learn scent patterns. For example, the analysis unit can use a machine learning algorithm to analyze the collected scent data and learn scent patterns. The analysis unit can also use data analysis techniques to analyze the collected scent data and learn scent patterns. Furthermore, the analysis unit can adjust the algorithm used to analyze the collected scent data and learn scent patterns. This makes it possible to construct an LSSM by learning scent patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected scent data into a generating AI, which can then learn scent patterns.

[0071] The construction unit can construct LSSMs based on the analyzed data. For example, the construction unit can construct an LSSM based on the analyzed data. For example, the construction unit can select data to be used to construct an LSSM and construct the LSSM. The construction unit can also select an algorithm to be used to construct an LSSM and construct the LSSM. Furthermore, the construction unit can adjust the data and algorithm used to construct an LSSM and construct the LSSM. In this way, various scents can be reproduced by constructing an LSSM. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input the analyzed data into a generating AI, and the generating AI can construct an LSSM.

[0072] The fragrance delivery unit can recreate scents that match specific scenes in movies and games. For example, the unit can provide a scent related to a specific scene in a movie. The unit can also provide a scent related to a specific event in a game. Furthermore, the unit can provide a scent related to a specific themed party. By recreating scents that match specific scenes, a more realistic experience can be provided. Some or all of the above processing in the fragrance delivery unit may be performed using AI, for example, or without AI. For example, the fragrance delivery unit can input movie scene data into a generating AI, and the generating AI can generate a scent that matches the scene.

[0073] The serving department can diagnose which alcoholic beverages are best suited to a dish and suggest the optimal combination. The serving department can, for example, diagnose which alcoholic beverages are best suited to a dish and suggest the optimal combination. The serving department can, for example, diagnose which alcoholic beverages are best suited to a dish and suggest the optimal combination. Furthermore, the serving department can also diagnose which alcoholic beverages are best suited to a dish and suggest the optimal combination. In this way, the suggestion of alcoholic beverages that are best suited to the dish enriches the dining experience. Some or all of the above processing in the serving department may be performed using AI, for example, or without AI. For example, the serving department can input dish data into a generating AI, and the generating AI can suggest the optimal combination.

[0074] The collection unit can estimate the user's emotions and adjust the timing of scent collection based on the estimated emotions. For example, if the user is relaxed, the collection unit will prioritize collecting scents with relaxing effects. The collection unit can also collect scents with stress-reducing effects if the user is stressed. Furthermore, if the user is excited, the collection unit can collect scents with calming effects. This allows for the collection of more appropriate scents by adjusting the timing of scent collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input the user's emotion data into the generative AI, which can then adjust the timing of scent collection.

[0075] The collection unit can select the optimal collection method when collecting fragrances, taking into account the temperature and humidity of the environment. For example, in a high-temperature and high-humidity environment, the collection unit can adjust the collection method considering the volatility of the fragrance. The collection unit can also adjust the collection method considering the preservation of the fragrance in a low-temperature and dry environment. Furthermore, in a moderate temperature and humidity environment, the collection unit can select a collection method considering the balance of the fragrance. This ensures that the quality of the fragrance is maintained by selecting the optimal collection method according to the environmental conditions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input environmental temperature and humidity data into a generating AI, which can then select the optimal collection method.

[0076] The collection unit can filter the types of scents based on specific events or seasons when collecting scents. For example, in the spring, the collection unit may prioritize collecting floral scents. In addition, the collection unit may prioritize collecting fruity scents in the summer. Furthermore, during specific events (for example, Christmas), the collection unit may collect scents related to the event. This allows for the collection of appropriate scents through filtering according to events and seasons. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input event and seasonal data into a generating AI, which can then filter the types of scents.

[0077] The collection unit can estimate the user's emotions and determine the priority of scents to collect based on the estimated emotions. For example, if the user is relaxed, the collection unit will prioritize collecting scents with relaxing effects. The collection unit can also prioritize collecting scents with stress-reducing effects if the user is stressed. Furthermore, if the user is excited, the collection unit can prioritize collecting scents with calming effects. This allows for the collection of more appropriate scents by prioritizing scents according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user emotion data into a generative AI, which can then determine the priority of scents.

[0078] The collection unit can prioritize the collection of region-specific scents by considering the user's geographical location information during scent collection. For example, if the user is in Japan, the collection unit can collect scents of flowers and fruits unique to Japan. The collection unit can also collect scents of French cuisine and wine unique to France if the user is in France. Furthermore, if the user is in the United States, the collection unit can collect scents unique to the United States. In this way, region-specific scents can be collected by collecting scents based on geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI, which can then collect region-specific scents.

[0079] The collection unit can analyze the user's social media activity when collecting scents and collect relevant scents. For example, if the user posts many photos of flowers on social media, the collection unit can collect flower scents. The collection unit can also collect food scents if the user posts many photos of food on social media. Furthermore, if the user posts many photos of travel on social media, the collection unit can collect scents of travel destinations. This allows for the collection of scents tailored to the user's preferences through scent collection based on social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI, which can then collect relevant scents.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in a visually relaxing way. The analysis unit can also display the analysis results in a simple and easy-to-understand way if the user is stressed. Furthermore, if the user is excited, the analysis unit can display the analysis results in a visually stimulating way. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can then adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the intensity and duration of the fragrance. For example, in the case of a strong fragrance, the analysis unit performs a detailed analysis, meticulously analyzing the fragrance components. The analysis unit can also perform a simplified analysis in the case of a weak fragrance, analyzing only the main components. Furthermore, in the case of a long-lasting fragrance, the analysis unit can also perform a detailed analysis of the changes in the fragrance over time. This allows for appropriate analysis by adjusting the level of detail of the analysis according to the intensity and duration of the fragrance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fragrance intensity and duration data into a generating AI, which can then adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the fragrance during analysis. For example, in the case of a floral fragrance, the analysis unit can apply an algorithm that analyzes components specific to flowers. The analysis unit can also apply an algorithm that analyzes components specific to fruits in the case of fruit fragrances. Furthermore, in the case of a food fragrance, the analysis unit can apply an algorithm that analyzes components specific to dishes. This enables highly accurate analysis by applying analysis algorithms according to the category of fragrance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fragrance category data into a generating AI, and the generating AI can apply different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit may prioritize the analysis of scents with relaxing effects. The analysis unit may also prioritize the analysis of scents with stress-reducing effects if the user is stressed. Furthermore, if the user is agitated, the analysis unit may prioritize the analysis of scents with calming effects. This enables appropriate analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the time of fragrance collection. For example, the analysis unit may prioritize the analysis of fragrances collected in spring. The analysis unit may also prioritize the analysis of fragrances collected in summer. Furthermore, the analysis unit may also prioritize the analysis of fragrances collected in autumn. This allows for appropriate analysis by adjusting the order of analysis based on the time of fragrance collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input fragrance collection time data into a generating AI, which can then adjust the order of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relationships between scents during the analysis. For example, the analysis unit may prioritize the analysis of scents in the same category. The analysis unit can also group related scents together for analysis. Furthermore, the analysis unit can determine the order of analysis by considering the interrelationships between scents. This allows for appropriate analysis by adjusting the order of analysis based on the relationships between scents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input scent relationship data into a generating AI, which can then adjust the order of analysis.

[0086] The construction unit can estimate the user's emotions and adjust the LSSM construction method based on the estimated user emotions. For example, if the user is relaxed, the construction unit can prioritize relaxing scents when constructing the LSSM. The construction unit can also prioritize stress-reducing scents when the user is stressed. Furthermore, if the user is excited, the construction unit can prioritize calming scents when constructing the LSSM. This allows for the construction of an appropriate LSSM by adjusting the LSSM construction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input user emotion data into the generative AI, which can then adjust the LSSM construction method.

[0087] The construction unit can select the optimal construction algorithm by referring to past scent data when constructing an LSSM. For example, the construction unit can select an algorithm specialized for floral scents by referring to previously collected floral scent data. The construction unit can also select an algorithm specialized for fruit scents by referring to previously collected fruit scent data. Furthermore, the construction unit can select an algorithm specialized for food scents by referring to previously collected food scent data. In this way, the optimal construction algorithm can be selected by referring to past scent data. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input past scent data into a generation AI, and the generation AI can select the optimal construction algorithm.

[0088] The construction unit can improve the accuracy of LSSM construction by considering the interrelationships of scents. For example, the construction unit can construct an LSSM by considering the interrelationships of floral scents and fruit scents. The construction unit can also construct an LSSM by considering the interrelationships of food scents and beverage scents. Furthermore, the construction unit can analyze the interrelationships of scents and construct an LSSM by considering the optimal combination. This improves the accuracy of LSSM construction by considering the interrelationships of scents. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input the interrelationship data of scents into a generating AI, which can then improve the accuracy of the construction.

[0089] The construction unit can estimate the user's emotions and adjust the order of LSSM construction based on the estimated user emotions. For example, if the user is relaxed, the construction unit can prioritize scents with relaxing effects when constructing LSSMs. The construction unit can also prioritize scents with stress-reducing effects when the user is stressed. Furthermore, if the user is excited, the construction unit can prioritize scents with calming effects when constructing LSSMs. This allows for the construction of appropriate LSSMs by adjusting the order of LSSM construction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the construction unit may be performed using AI, or not using AI. For example, the construction unit can input user emotion data into the generative AI, which can then adjust the order of LSSM construction.

[0090] The construction unit can construct LSSMs while considering the geographical distribution of scents. For example, the construction unit can construct LSSMs with an emphasis on Japanese scents. The construction unit can also construct LSSMs with an emphasis on French scents. Furthermore, the construction unit can also construct LSSMs with an emphasis on American scents. By considering the geographical distribution of scents, it is possible to construct LSSMs that reflect regionally specific scents. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input geographical distribution data of scents into a generating AI, and the generating AI can perform the construction.

[0091] The construction unit can improve the accuracy of LSSM construction by referring to relevant literature on fragrances. For example, the construction unit can construct an LSSM by referring to literature on floral fragrances. The construction unit can also construct an LSSM by referring to literature on fruit fragrances. Furthermore, the construction unit can construct an LSSM by referring to literature on culinary fragrances. This improves the accuracy of LSSM construction by referring to relevant literature on fragrances. Some or all of the above processing in the construction unit may be performed using AI, for example, or without AI. For example, the construction unit can input data on relevant literature on fragrances into a generating AI, which can then improve the accuracy of the construction.

[0092] The service provider can estimate the user's emotions and adjust the method of providing fragrance based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a relaxing fragrance. The service provider can also provide a stress-reducing fragrance if the user is stressed. Furthermore, if the user is excited, the service provider can provide a calming fragrance. This allows for the provision of an appropriate fragrance by adjusting the method of providing fragrance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI, which can then adjust the method of providing fragrance.

[0093] The fragrance delivery unit can customize the fragrances it delivers based on specific scenes or events. For example, it can deliver a fragrance related to a specific scene in a movie. It can also deliver a fragrance related to a specific event in a game. Furthermore, it can deliver a fragrance related to a specific themed party. This allows for the delivery of appropriate fragrances by customizing them according to specific scenes or events. Some or all of the above processing in the fragrance delivery unit may be performed using AI, for example, or not. For example, the fragrance delivery unit can input scene or event data into a generating AI, which can then customize the fragrances.

[0094] The fragrance dispenser can provide the most suitable fragrance by referring to the user's past fragrance preferences. For example, the dispenser can provide the fragrance of a flower the user has liked in the past. The dispenser can also provide the fragrance of a fruit the user has liked in the past. Furthermore, the dispenser can provide the fragrance of a dish the user has liked in the past. This allows the dispenser to provide an appropriate fragrance based on the user's past fragrance preferences. Some or all of the above processing in the dispenser may be performed using AI, for example, or without AI. For example, the dispenser can input the user's past fragrance preference data into a generating AI, which can then provide the most suitable fragrance.

[0095] The service provider can estimate the user's emotions and adjust the order in which scents are provided based on the estimated emotions. For example, if the user is relaxed, the service provider can first provide a relaxing scent. The service provider can also first provide a stress-reducing scent if the user is stressed. Furthermore, if the user is excited, the service provider can first provide a calming scent. This allows the service provider to provide an appropriate scent by adjusting the order in which scents are provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then adjust the order in which scents are provided.

[0096] The service provider can provide the most suitable fragrance by considering the user's geographical location. For example, if the user is in Japan, the service provider can provide fragrances of flowers and fruits unique to Japan. The service provider can also provide fragrances of French cuisine and wine unique to France if the user is in France. Furthermore, if the user is in the United States, the service provider can provide fragrances unique to the United States. This allows for the provision of regionally specific fragrances based on geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI, which can then provide the most suitable fragrance.

[0097] The service provider can analyze a user's social media activity and provide relevant scents when providing fragrances. For example, if a user posts many photos of flowers on social media, the service provider can provide a floral scent. The service provider can also provide a cooking scent if a user posts many photos of food on social media. Furthermore, if a user posts many photos of travel on social media, the service provider can provide a travel destination scent. This allows the service provider to provide fragrances that match the user's preferences by providing fragrances based on social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI, which can then provide relevant fragrances.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The olfactory sensing system can also include a personalization unit that learns the user's olfactory preferences. This personalization unit learns the scents the user has previously selected and the patterns of scents they prefer, enabling it to provide the most suitable scent for each individual user. For example, the personalization unit can record the floral scents the user has previously chosen and reflect this in future scent suggestions. It can also learn the scents the user prefers in specific seasons and suggest the most suitable scent for each season. Furthermore, the personalization unit can track changes in the user's sense of smell and adapt to evolving olfactory preferences over time. This makes it possible to provide personalized scents based on the user's olfactory preferences.

[0100] The olfactory sensing system may also include a health monitoring unit that monitors the user's health condition. The health monitoring unit can adjust the scent provided based on the user's health status. For example, if the user has a cold, the health monitoring unit can provide a scent that alleviates cold symptoms. It can also provide a stress-reducing scent if the user is stressed. Furthermore, if the user wants to relax, the health monitoring unit can provide a relaxing scent. This makes it possible to provide scents tailored to the user's health condition.

[0101] The olfactory sensing system may further include an emotion estimation unit that estimates the user's emotions and adjusts the scent provided based on those emotions. For example, if the user is relaxed, the emotion estimation unit may provide a relaxing scent. It may also provide a stress-reducing scent if the user is stressed. Furthermore, if the user is excited, the emotion estimation unit may provide a calming scent. This makes it possible to provide scents that correspond to the user's emotions.

[0102] The olfactory sensing system can also include an activity monitoring unit that collects user activity data and provides scents tailored to that activity. For example, if the user is exercising, the activity monitoring unit can provide a scent that has a refreshing effect after exercise. It can also provide a scent that enhances concentration if the user is reading. Furthermore, if the user is relaxing, the activity monitoring unit can provide a scent that promotes relaxation. This makes it possible to provide scents that are tailored to the user's activity.

[0103] The olfactory sensing system may further include a sensitivity measuring unit that measures the user's olfactory sensitivity and adjusts the fragrance intensity accordingly. For example, if the user's sense of smell is sensitive, the sensitivity measuring unit may reduce the fragrance intensity. Conversely, if the user's sense of smell is less sensitive, the sensitivity measuring unit may increase the fragrance intensity. Furthermore, the sensitivity measuring unit can adjust the fragrance intensity in response to changes in the user's sense of smell. This makes it possible to provide fragrances tailored to the user's olfactory sensitivity.

[0104] The olfactory sensing system may further include an emotion estimation unit that estimates the user's emotions and adjusts the order in which scents are provided based on the estimated emotions. For example, if the user is relaxed, the emotion estimation unit may first provide a relaxing scent. It may also first provide a stress-reducing scent if the user is stressed. Furthermore, if the user is excited, the emotion estimation unit may first provide a calming scent. This allows for adjustment of the scent delivery order according to the user's emotions.

[0105] The olfactory sensing system may further include an emotion estimation unit that estimates the user's emotions and adjusts the way scents are delivered based on the estimated emotions. For example, if the user is relaxed, the emotion estimation unit may provide a relaxing scent. It may also provide a stress-reducing scent if the user is stressed. Furthermore, if the user is excited, the emotion estimation unit may provide a calming scent. This allows for adjustment of the scent delivery method according to the user's emotions.

[0106] The olfactory sensing system may further include an emotion estimation unit that estimates the user's emotions and adjusts the timing of scent delivery based on the estimated emotions. For example, if the user is relaxed, the emotion estimation unit can adjust the timing of providing a relaxing scent. It can also adjust the timing of providing a stress-reducing scent if the user is stressed. Furthermore, if the user is excited, the emotion estimation unit can adjust the timing of providing a calming scent. This makes it possible to adjust the timing of scent delivery according to the user's emotions.

[0107] The olfactory sensing system may further include an emotion estimation unit that estimates the user's emotions and adjusts the location of scent delivery based on the estimated emotions. For example, if the user is relaxed, the emotion estimation unit can adjust the location of scents that have a relaxing effect. It can also adjust the location of scents that have a stress-reducing effect if the user is stressed. Furthermore, if the user is excited, the emotion estimation unit can adjust the location of scents that have a calming effect. This makes it possible to adjust the location of scent delivery according to the user's emotions.

[0108] The olfactory sensing system can also include a personalization unit that learns the user's olfactory preferences. This personalization unit learns the scents the user has previously selected and the patterns of scents they prefer, enabling it to provide the most suitable scent for each individual user. For example, the personalization unit can record the floral scents the user has previously chosen and reflect this in future scent suggestions. It can also learn the scents the user prefers in specific seasons and suggest the most suitable scent for each season. Furthermore, the personalization unit can track changes in the user's sense of smell and adapt to evolving olfactory preferences over time. This makes it possible to provide personalized scents based on the user's olfactory preferences.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects scent patterns. The collection unit can collect data on scents such as floral scents, fruit scents, and food scents. The collection unit analyzes the components of flowers and collects that data. It can also analyze the components of fruits and collect that data. Furthermore, it can analyze the components of food and collect that data. Step 2: The analysis unit analyzes the scent patterns collected by the collection unit. The analysis unit analyzes the collected scent data and learns the scent patterns. For example, machine learning algorithms and data analysis techniques can be used to analyze the collected scent data and learn the scent patterns. Step 3: The construction unit constructs the LSSM based on the data analyzed by the analysis unit. The construction unit constructs the LSSM based on the analyzed data and selects and adjusts the data and algorithms to be used in constructing the LSSM. Step 4: The service department provides fragrances using the LSSM built by the construction department. The service department can, for example, recreate fragrances to match specific scenes or events in movies or games. This enables the provision of new services in various businesses.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, construction unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects fragrance data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected fragrance data to learn the fragrance pattern. The construction unit is implemented in the specific processing unit 290 of the data processing unit 12 and constructs an LSSM based on the analyzed data. The provision unit provides the fragrance using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, construction unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects fragrance data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected fragrance data and learns the fragrance pattern. The construction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which constructs an LSSM based on the analyzed data. The provision unit provides the fragrance using, for example, the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, construction unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects fragrance data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected fragrance data and learns the fragrance pattern. The construction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which constructs an LSSM based on the analyzed data. The provision unit provides the fragrance using, for example, the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, construction unit, and supply unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects fragrance data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected fragrance data and learns the fragrance pattern. The construction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which constructs an LSSM based on the analyzed data. The supply unit provides the fragrance using, for example, the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A collection unit that collects scent patterns, An analysis unit analyzes the scent patterns collected by the aforementioned collection unit, A construction unit that constructs an LSSM based on the data analyzed by the aforementioned analysis unit, The system comprises a supply unit that provides fragrance using the LSSM constructed by the aforementioned construction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on the scents of flowers, fruits, and food. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected scent data is analyzed, and scent patterns are learned. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned construction unit is Construct an LSSM based on the analyzed data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Recreating scents that match specific scenes in movies and games. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We diagnose which alcoholic beverages best complement your meal and suggest the optimal pairing. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of scent collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting fragrance samples, the optimal collection method is selected considering the ambient temperature and humidity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting scents, filter the types of scents based on specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of scents to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting scents, the system prioritizes collecting scents specific to a user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting scents, the system analyzes users' social media activity and collects relevant scents. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the intensity and duration of the scent. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the fragrance category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on when the scents were collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between scents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned construction unit is We estimate the user's emotions and adjust how the LSSM is constructed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned construction unit is When constructing an LSSM, the optimal construction algorithm is selected by referring to past fragrance data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned construction unit is When constructing LSSM, consider the interrelationships of scents to improve the accuracy of the construction. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned construction unit is It estimates the user's emotions and adjusts the LSSM construction order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned construction unit is When constructing an LSSM (Large-Scale Scent Spectrum), the geographical distribution of scents should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned construction unit is When constructing an LSSM, refer to relevant literature on fragrances to improve the accuracy of the construction. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way fragrances are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing fragrances, customize the scents offered based on specific scenes or events. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing fragrances, the system refers to the user's past fragrance preferences to select the most suitable scent. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which scents are provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing fragrances, the system takes into account the user's geographical location to provide the most suitable fragrance. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing fragrances, we analyze the user's social media activity and recommend relevant fragrances. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects scent patterns, An analysis unit analyzes the scent patterns collected by the aforementioned collection unit, A construction unit that constructs an LSSM based on the data analyzed by the aforementioned analysis unit, The system comprises a supply unit that provides fragrance using the LSSM constructed by the aforementioned construction unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on the scents of flowers, fruits, and food. The system according to feature 1.

3. The aforementioned analysis unit, The collected scent data is analyzed, and scent patterns are learned. The system according to feature 1.

4. The aforementioned construction unit is Construct an LSSM based on the analyzed data. The system according to feature 1.

5. The aforementioned supply unit is, Recreating scents that match specific scenes in movies and games. The system according to feature 1.

6. The aforementioned supply unit is, We diagnose which alcoholic beverages best complement your meal and suggest the optimal pairing. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of scent collection based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting fragrance samples, the optimal collection method is selected considering the ambient temperature and humidity. The system according to feature 1.

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