system

Abstract

The system according to this embodiment aims to provide the optimal fragrance based on the user's preferences and values. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a sending unit, and a providing unit. The collection unit collects information on the user's preferences, values, and outlook on life. The analysis unit analyzes the information collected by the collection unit and blends the optimal fragrance for the user. The sending unit sends the fragrance blended by the analysis unit to the user as a fragrance sample. The providing unit allows the user to try the fragrance sample sent by the sending unit, and provides the fragrance if they like it.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 prior art, there was a problem that it was difficult to provide scents based on individual preferences and values of users.

[0005] The system according to the embodiment aims to provide an optimal scent based on the preferences and values of a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a sending unit, and a providing unit. The collection unit collects information on the user's preferences, values, and outlook on life. The analysis unit analyzes the information collected by the collection unit and blends a fragrance that is optimal for the user. The sending unit sends the fragrance blended by the analysis unit to the user as a fragrance sample. The providing unit allows the user to try the fragrance sample sent by the sending unit, and provides the fragrance if they like it. [Effects of the Invention]

[0007] The system according to this embodiment can provide the optimal fragrance based on the user's preferences and values. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 fragrance blending system according to an embodiment of the present invention is a system that uses AI to understand the preferences, values, and worldview of individual users, and based on that, blends and provides the user with the most suitable fragrance. The fragrance blending system collects information such as the user's preferences, values, and worldview through an interview format, analyzes the collected information, and blends the most suitable fragrance for the user. The blended fragrance is sent to the user as a fragrance sample, and if the user likes it, they are provided with the fragrance. For example, the fragrance blending system collects information such as the user's preferences, values, and worldview through an interview format. At this time, the user answers questions to the AI ​​about their preferences, values, and worldview. For example, they may be asked about their favorite types of fragrances, past fragrance experiences, and how they use fragrances in their daily lives. This allows the AI ​​to understand detailed information about the user. Next, the fragrance blending system analyzes the collected information. Based on the information such as the user's preferences, values, and worldview, the AI ​​blends the most suitable fragrance for the user. For example, if the user wants to relax, the system blends fragrances with relaxing effects such as lavender and chamomile. Furthermore, if a user wants to feel energetic, a citrus scent will be blended. In this way, the AI ​​can blend the optimal scent for the user. The blended scent is sent to the user as a scent sample. The user tries the scent sample, and if they like it, they can subscribe to that scent. For example, if a user tries a scent sample and likes it, they can subscribe to a service that provides that scent regularly. This allows the scent blending system to help users find the scent that is best suited to them. Because the AI ​​understands the user's preferences, values, and outlook on life and blends scents based on that, users can enjoy their own unique scent. Also, by trying scent samples, users can find a scent that suits them. This allows users to enjoy the perfect scent for themselves in their daily lives. In this way, the scent blending system can provide the optimal scent based on the user's preferences, values, and outlook on life.

[0029] The fragrance blending system according to this embodiment comprises a collection unit, an analysis unit, a transmission unit, and a provision unit. The collection unit collects information on the user's preferences, values, and outlook on life. The collection unit can collect information on the user's preferences, values, and outlook on life, for example, through an interview format. The interview format includes, but is not limited to, face-to-face interviews, online interviews, and the content of the questions. The collection unit can collect detailed information on the user through, for example, a face-to-face interview. The collection unit can also collect information on the user through an online interview. Furthermore, the collection unit can dynamically change the content of the questions and collect detailed information based on the user's responses. The analysis unit analyzes the information collected by the collection unit and blends the most suitable fragrance for the user. The analysis unit can, for example, blend relaxing or energetic fragrances based on information on the user's preferences, values, and outlook on life. For example, the analysis unit can blend lavender and chamomile as relaxing fragrances. The analysis unit can also blend citrus fragrances as energetic fragrances. Furthermore, the analysis unit can also blend the optimal fragrance according to the user's condition. The delivery unit sends the fragrance blended by the analysis unit to the user as a fragrance sample. The delivery unit can, for example, send the blended fragrance to the user as a fragrance sample. The delivery unit can send the fragrance sample by, for example, postal mail or courier service. The delivery unit can also send the fragrance sample by email. Furthermore, the delivery unit can arrange for the user to receive the fragrance sample at a specific location, according to the user's preference. The supply unit provides the fragrance to the user if they try the fragrance sample sent by the delivery unit and like it. The supply unit can, for example, provide a service that regularly provides the fragrance if the user tries the fragrance sample and likes it. The supply unit can, for example, provide a service that regularly provides the fragrance. The supply unit can also provide the fragrance at a specific time according to the user's preference. Furthermore, the supply unit can adjust the method of providing the fragrance based on user feedback.As a result, the fragrance blending system according to this embodiment can provide the optimal fragrance based on the user's preferences, values, and outlook on life.

[0030] The data collection unit collects information about users' preferences, values, and outlook on life. For example, the data collection unit can collect this information through interviews. Interview formats include, but are not limited to, face-to-face interviews, online interviews, and the content of the questions. For example, the data collection unit can collect detailed information about users through face-to-face interviews. It can also collect information through online interviews. Furthermore, the data collection unit can dynamically change the content of the questions and collect detailed information based on the user's responses. Specifically, in face-to-face interviews, a professional interviewer directly interacts with the user, collecting non-verbal information such as facial expressions and tone of voice. In online interviews, video calls or chat formats are used to allow users to answer in a relaxed environment. The questions cover a wide range of topics, including the user's hobbies, daily life, stress levels, and relaxation methods, and the next questions dynamically change based on the user's answers. For example, if a user answers "I like natural scents," the next question might be a more specific one, such as "What kind of natural scents do you particularly like?" In this way, the data collection unit can delve deeper into users' detailed preferences and values, enabling it to collect more accurate information. Furthermore, the data collection unit respects user privacy and implements security measures to properly manage the collected information. For example, collected data is encrypted and stored on secure servers. This allows the data collection unit to efficiently collect detailed information while gaining user trust.

[0031] The analysis unit analyzes the information collected by the data collection unit and blends the optimal fragrance for the user. For example, the analysis unit can blend relaxing or energizing fragrances based on information about the user's preferences, values, and outlook on life. For instance, the analysis unit might blend lavender and chamomile as relaxing fragrances. It can also blend citrus fragrances as energizing fragrances. Furthermore, the analysis unit can blend the optimal fragrance according to the user's state of mind. Specifically, the analysis unit uses AI to analyze the collected information from multiple perspectives. The AI ​​uses natural language processing technology to analyze the text data of interviews and extract the user's emotions and values. For example, if a user states that they "feel stressed at work," the AI ​​will suggest a relaxing fragrance based on that information. If a user states that they "want a fragrance that energizes them in the morning," the AI ​​will suggest an energizing fragrance. In addition, the analysis unit optimizes the fragrance blend based on the user's past fragrance preference data and feedback. For example, if a user has previously preferred the scent of lavender, a more personalized fragrance can be provided by blending lavender with other scents. The analysis unit comprehensively analyzes this information and generates a recipe for blending the optimal fragrance for the user. This recipe details the fragrance components, blending ratios, and blending methods, allowing for accurate reproduction of the blending process. As a result, the analysis unit can blend fragrances with high precision to meet the individual needs of each user.

[0032] The shipping department sends fragrance samples of the fragrance blended by the analysis department to the user. The shipping department can, for example, send the blended fragrance as a fragrance sample to the user. The shipping department can send the fragrance samples by mail or courier service. The shipping department can also send fragrance samples by email. Furthermore, the shipping department can arrange for users to receive fragrance samples at a specific location, according to their preference. Specifically, the shipping department packs the fragrance samples in small bottles or trial-sized packages and uses appropriate packaging to prevent leakage or deterioration of the fragrance. When using mail or courier service, the shipping department provides a tracking number so that the user can check the delivery status. When using email, it is also possible to provide fragrance samples in digital format. For example, a digital recipe containing the fragrance components and blending method can be sent so that the user can recreate the fragrance at home. Furthermore, the shipping department can arrange for users to receive fragrance samples at a specific location, according to their preference. For example, a service can be provided that allows users to receive fragrance samples at partner stores or cafes. This allows users to receive fragrance samples at their convenience. The distribution department can provide users with fragrance samples quickly and reliably through these services, thereby increasing user satisfaction.

[0033] The service provider will provide users with a fragrance sample sent by the delivery service if they like it after trying it. The service provider can, for example, offer a service that provides users with a fragrance on a regular basis if they like the sample. The service provider can also provide fragrances at specific times according to the user's wishes. Furthermore, the service provider can adjust the fragrance delivery method based on user feedback. Specifically, after users try the fragrance sample, the service provider collects feedback through an online platform. Users evaluate the fragrance's strength, duration, and degree of preference, and send this information to the service provider. Based on this feedback, the service provider adjusts the fragrance's ingredients and blending ratios to provide the user with the optimal fragrance. The service provider can also offer a service that provides fragrances on a regular basis. For example, they can deliver a new fragrance to the user's home once a month, allowing the user to always enjoy a new scent. Additionally, the service provider can offer a service that delivers fragrances at specific times. For example, they can provide a special fragrance to coincide with a user's birthday or a special event, allowing the user to enjoy special moments even more. Through these services, the service provider can offer users a personalized fragrance experience and increase user satisfaction.

[0034] The data collection unit can collect information on users' preferences, values, and outlook on life through interviews. The data collection unit can collect detailed information on users, for example, through face-to-face interviews. The data collection unit can also collect information on users through online interviews, for example. The data collection unit can also dynamically change the content of interview questions and collect detailed information based on user responses, for example. This allows for the collection of detailed information through interviews. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the information collected through interviews into AI, which can then analyze the information to collect detailed information.

[0035] The analysis unit can blend relaxing and energizing scents based on information about the user's preferences, values, and outlook on life. For example, the analysis unit can blend lavender and chamomile as relaxing scents. For example, the analysis unit can also blend citrus scents as energizing scents. The analysis unit can also blend the optimal scent according to the user's state. This allows for the blending of scents tailored to the user's state. 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 information about the user's preferences, values, and outlook on life into the AI, which can then analyze the information and blend the optimal scent.

[0036] The delivery unit can send the blended fragrance to the user as a fragrance sample. The delivery unit can send the fragrance sample by mail or courier, for example. The delivery unit can also send the fragrance sample by email, for example. The delivery unit can also arrange for the user to receive the fragrance sample at a specific location, for example, according to the user's preference. This allows the user to try the fragrance. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the blended fragrance into the AI, and the AI ​​can select a method for sending the fragrance sample.

[0037] The service provider can offer a service that provides users with a fragrance sample they like on a regular basis. For example, the service provider can offer a service that provides fragrances on a regular basis. The service provider can also offer fragrances at specific times according to the user's wishes. The service provider can also adjust the method of fragrance delivery based on user feedback. This allows users to continuously use their favorite fragrances. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback into AI, which can then select the optimal delivery method.

[0038] The data collection unit can analyze the user's past fragrance selection history and generate optimal questions. For example, the data collection unit can generate relevant questions based on the user's past fragrance selection trends. The data collection unit can also generate questions about fragrances to avoid based on fragrances the user has avoided in the past. The data collection unit can also generate questions about new fragrance suggestions based on the user's past selection history. This allows for the generation of more appropriate questions based on past selection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past fragrance selection history into AI, which can then generate optimal questions.

[0039] The data collection unit can collect data on users' daily activities in addition to interviews, allowing for a more detailed understanding of their preferences, values, and outlook on life. For example, the data collection unit can analyze users' smartphone usage history to understand their daily activity patterns. It can also analyze users' social media posts to understand their preferences and values. It can also understand places visited and activities based on users' location information. This allows for a more detailed understanding of information by collecting data on daily activities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input users' daily activity data into AI, which can then analyze the data to understand more detailed information.

[0040] The data collection unit can analyze a user's social media activity and supplement information about their preferences, values, and outlook on life. For example, the data collection unit can analyze a user's posts to understand their preferences and values. For example, the data collection unit can analyze the accounts a user follows to understand their areas of interest. For example, the data collection unit can analyze a user's "likes" and comment history to understand their preferences. In this way, by analyzing social media activity, information about preferences, values, and outlook on life can be supplemented. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into AI, which can analyze the data and supplement the information.

[0041] The data collection unit can collect regional scent preferences, taking into account the user's geographical location. For example, the data collection unit can collect scents specific to the area where the user lives. The data collection unit can also collect scent preferences for areas the user has visited. For example, the data collection unit can analyze regional scent trends based on the user's location information. This allows for the collection of regional scent preferences by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then analyze the data to collect regional scent preferences.

[0042] The analysis unit can improve the accuracy of the blend by referring to the user's past fragrance selection history during analysis. For example, the analysis unit can improve the accuracy of the blend based on the user's past fragrance selection trends. The analysis unit can also improve the accuracy of the blend based on fragrances the user has avoided in the past. For example, the analysis unit can perform blending based on the user's past selection history to suggest new fragrances. This allows for improved blending accuracy based on past selection history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past fragrance selection history into AI, and the AI ​​can analyze the data to improve blending accuracy.

[0043] The analysis unit can customize the fragrance blend based on the user's lifestyle and activity patterns during analysis. For example, the analysis unit can blend a fragrance to match the time of day when the user wants to relax. The analysis unit can also blend a fragrance to match the time of day when the user wants to be energetic. The analysis unit can also blend the optimal fragrance based on the user's activity patterns. This allows for the customization of the fragrance blend based on lifestyle and activity 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 data on the user's lifestyle and activity patterns into the AI, which can then analyze the data to customize the fragrance blend.

[0044] The analysis unit can blend fragrances while considering the user's health condition and allergy information during analysis. For example, if the user has allergies, the analysis unit will blend fragrances while avoiding those ingredients. For example, the analysis unit can also blend fragrances with relaxing effects according to the user's health condition. For example, the analysis unit can also blend energetic fragrances according to the user's health condition. This allows for the blending of the optimal fragrance while considering health condition and allergy information. 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 user's health condition and allergy information into the AI, which can then analyze the data and blend the optimal fragrance.

[0045] The analysis unit can blend fragrances while considering the user's cultural background and religious beliefs during analysis. For example, the analysis unit can blend specific fragrances to match the user's cultural background. For example, the analysis unit can also blend fragrances while avoiding specific fragrances to match the user's religious beliefs. For example, the analysis unit can blend the optimal fragrance based on the user's cultural background and religious beliefs. This allows for the blending of the optimal fragrance while considering cultural background and religious beliefs. 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 data on the user's cultural background and religious beliefs into the AI, which can then analyze the data and blend the optimal fragrance.

[0046] The sending unit can select the optimal sending timing by referring to the user's past sample receipt history at the time of sending. For example, the sending unit can select the optimal sending timing based on the timing of samples the user has received in the past. The sending unit can also predict the optimal sending timing from the user's past receipt history. For example, the sending unit can analyze the user's past receipt history and select the optimal sending timing. This allows the optimal sending timing to be selected based on past receipt history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's past sample receipt history into AI, and the AI ​​can analyze the data to select the optimal sending timing.

[0047] The delivery unit can customize the delivery schedule based on the user's daily rhythm at the time of delivery. For example, the delivery unit can send fragrance samples to coincide with the time when the user is relaxing. For example, the delivery unit can also send fragrance samples to coincide with the time when the user wants to be energetic. For example, the delivery unit can customize the optimal delivery schedule based on the user's daily rhythm. This allows the delivery unit to provide an optimal delivery schedule based on the user's daily rhythm. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data on the user's daily rhythm into AI, and the AI ​​can analyze the data to customize the delivery schedule.

[0048] The shipping unit can select the optimal shipping method at the time of shipping, taking into account the user's geographical location information. For example, the shipping unit can select the optimal shipping method based on the shipping company in the area where the user lives. The shipping unit can also select the optimal shipping method based on the shipping company in the area the user has visited. The shipping unit can also select the optimal shipping method based on the user's geographical location information. This allows the optimal shipping method to be selected based on geographical location information. Some or all of the above processing in the shipping unit may be performed using AI, for example, or without AI. For example, the shipping unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to select the optimal shipping method.

[0049] The sending unit can analyze the user's social media activity at the time of sending to personalize the content of the message. For example, the sending unit can personalize the content based on the user's posts. For example, the sending unit can personalize the content based on the accounts the user follows. For example, the sending unit can personalize the content based on the user's "likes" and comment history. This allows the sending unit to personalize the content based on social media activity. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's social media activity data into AI, which can then analyze the data to personalize the content of the message.

[0050] The dispensing unit can select the optimal dispensing method by referring to the user's past fragrance usage history at the time of dispensing. For example, the dispensing unit can select the optimal dispensing method based on the frequency of fragrances the user has used in the past. For example, the dispensing unit can predict the optimal dispensing method from the user's past usage history. For example, the dispensing unit can analyze the user's past usage history and select the optimal dispensing method. This allows the optimal dispensing method to be selected based on past usage history. Some or all of the above processing in the dispensing unit may be performed using AI, for example, or without AI. For example, the dispensing unit can input the user's past fragrance usage history into AI, and the AI ​​can analyze the data to select the optimal dispensing method.

[0051] The service provider can customize the fragrance delivery schedule based on the user's lifestyle at the time of delivery. For example, the service provider can deliver fragrance according to the time of day when the user wants to relax. For example, the service provider can also deliver fragrance according to the time of day when the user wants to be energetic. For example, the service provider can customize the optimal delivery schedule based on the user's lifestyle. This allows for the provision of an optimal delivery schedule based on the user's lifestyle. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user lifestyle data into AI, and the AI ​​can analyze the data to customize the delivery schedule.

[0052] The service provider can provide the optimal fragrance by considering the user's geographical location information at the time of delivery. For example, the service provider can provide a fragrance specific to the area where the user lives. For example, the service provider can also provide a fragrance specific to the area the user has visited. For example, the service provider can provide the optimal fragrance based on the user's geographical location information. This allows the service provider to provide the optimal fragrance 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 AI, and the AI ​​can analyze the data to provide the optimal fragrance.

[0053] The service provider can analyze the user's social media activity at the time of delivery to personalize the content offered. For example, the service provider can personalize the content based on the user's posts. For example, the service provider can personalize the content based on the accounts the user follows. For example, the service provider can personalize the content based on the user's "likes" and comment history. This allows the service provider to personalize the content 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 activity data into AI, which can then analyze the data to personalize the content offered.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The fragrance blending system collects information on the user's preferences, values, and outlook on life, as well as their health status and allergies. The analysis unit then considers this information when blending fragrances. For example, the collection unit gathers information to avoid ingredients the user is allergic to. It also collects information on the user's health status, which can be considered when blending relaxing or energetic fragrances. Furthermore, the collection unit can emphasize or avoid specific ingredients depending on the user's health condition. This enables fragrance blending that takes into account the user's health status and allergy information.

[0056] The fragrance blending system can collect information on the user's preferences, values, and worldview, as well as their cultural background and religious beliefs, and then use this information in its analysis unit to blend fragrances. For example, the collection unit can gather information on the user's cultural background and consider it when blending specific fragrances. It can also collect information on the user's religious beliefs and provide information to avoid certain ingredients. Furthermore, the collection unit can emphasize or avoid certain fragrances based on the user's cultural background and religious beliefs. This makes it possible to blend fragrances that take the user's cultural background and religious beliefs into consideration.

[0057] The fragrance blending system collects data on the user's daily activities, in addition to their preferences, values, and outlook on life. The analysis unit then considers this information to blend fragrances. For example, the data collection unit analyzes the user's smartphone usage history to understand their daily activity patterns. It also analyzes the user's social media posts to understand their preferences and values. Furthermore, the data collection unit uses the user's location information to understand the places they have visited and the activities they have engaged in. By collecting this data on daily activities, the system can obtain more detailed information.

[0058] The fragrance blending system collects the user's preferences, values, and outlook on life, as well as their past fragrance selection history, and the analysis unit considers this information when blending fragrances. For example, the collection unit generates relevant questions based on the user's past fragrance choices. It can also generate questions about fragrances to avoid based on fragrances the user has avoided in the past. Furthermore, the collection unit can generate questions regarding new fragrance suggestions based on the user's past selection history. This allows for the generation of more appropriate questions based on past selection history.

[0059] The fragrance blending system collects the user's preferences, values, and outlook on life, as well as their geographical location information, and the analysis unit considers this information when blending fragrances. For example, the collection unit can collect the unique scents of the area where the user lives. It can also collect the user's fragrance preferences from areas they have visited. Furthermore, the collection unit can analyze regional fragrance trends based on the user's location information. This allows the system to collect regional fragrance preferences by considering geographical location information.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects information about the user's preferences, values, and outlook on life. The data collection unit collects information using methods such as face-to-face interviews, online interviews, and dynamically changing the content of the questions. Step 2: The analysis unit analyzes the information collected by the data collection unit and blends the optimal fragrance for the user. For example, the analysis unit can blend fragrances that have a relaxing effect or fragrances that are energizing. Step 3: The sending unit sends the fragrance blended by the analysis unit to the user as a fragrance sample. The sending unit can send the fragrance sample by mail, courier service, or email, for example. Step 4: The supplying department allows the user to try the fragrance samples sent by the sending department, and provides the fragrance if the user likes it. The supplying department can, for example, provide fragrances regularly or at specific times.

[0062] (Example of form 2) The fragrance blending system according to an embodiment of the present invention is a system that uses AI to understand the preferences, values, and worldview of individual users, and based on that, blends and provides the user with the most suitable fragrance. The fragrance blending system collects information such as the user's preferences, values, and worldview through an interview format, analyzes the collected information, and blends the most suitable fragrance for the user. The blended fragrance is sent to the user as a fragrance sample, and if the user likes it, they are provided with the fragrance. For example, the fragrance blending system collects information such as the user's preferences, values, and worldview through an interview format. At this time, the user answers questions to the AI ​​about their preferences, values, and worldview. For example, they may be asked about their favorite types of fragrances, past fragrance experiences, and how they use fragrances in their daily lives. This allows the AI ​​to understand detailed information about the user. Next, the fragrance blending system analyzes the collected information. Based on the information such as the user's preferences, values, and worldview, the AI ​​blends the most suitable fragrance for the user. For example, if the user wants to relax, the system blends fragrances with relaxing effects such as lavender and chamomile. Furthermore, if a user wants to feel energetic, a citrus scent will be blended. In this way, the AI ​​can blend the optimal scent for the user. The blended scent is sent to the user as a scent sample. The user tries the scent sample, and if they like it, they can subscribe to that scent. For example, if a user tries a scent sample and likes it, they can subscribe to a service that provides that scent regularly. This allows the scent blending system to help users find the scent that is best suited to them. Because the AI ​​understands the user's preferences, values, and outlook on life and blends scents based on that, users can enjoy their own unique scent. Also, by trying scent samples, users can find a scent that suits them. This allows users to enjoy the perfect scent for themselves in their daily lives. In this way, the scent blending system can provide the optimal scent based on the user's preferences, values, and outlook on life.

[0063] The fragrance blending system according to this embodiment comprises a collection unit, an analysis unit, a transmission unit, and a provision unit. The collection unit collects information on the user's preferences, values, and outlook on life. The collection unit can collect information on the user's preferences, values, and outlook on life, for example, through an interview format. The interview format includes, but is not limited to, face-to-face interviews, online interviews, and the content of the questions. The collection unit can collect detailed information on the user through, for example, a face-to-face interview. The collection unit can also collect information on the user through an online interview. Furthermore, the collection unit can dynamically change the content of the questions and collect detailed information based on the user's responses. The analysis unit analyzes the information collected by the collection unit and blends the most suitable fragrance for the user. The analysis unit can, for example, blend relaxing or energetic fragrances based on information on the user's preferences, values, and outlook on life. For example, the analysis unit can blend lavender and chamomile as relaxing fragrances. The analysis unit can also blend citrus fragrances as energetic fragrances. Furthermore, the analysis unit can also blend the optimal fragrance according to the user's condition. The delivery unit sends the fragrance blended by the analysis unit to the user as a fragrance sample. The delivery unit can, for example, send the blended fragrance to the user as a fragrance sample. The delivery unit can send the fragrance sample by, for example, postal mail or courier service. The delivery unit can also send the fragrance sample by email. Furthermore, the delivery unit can arrange for the user to receive the fragrance sample at a specific location, according to the user's preference. The supply unit provides the fragrance to the user if they try the fragrance sample sent by the delivery unit and like it. The supply unit can, for example, provide a service that regularly provides the fragrance if the user tries the fragrance sample and likes it. The supply unit can, for example, provide a service that regularly provides the fragrance. The supply unit can also provide the fragrance at a specific time according to the user's preference. Furthermore, the supply unit can adjust the method of providing the fragrance based on user feedback.As a result, the fragrance blending system according to this embodiment can provide the optimal fragrance based on the user's preferences, values, and outlook on life.

[0064] The data collection unit collects information about users' preferences, values, and outlook on life. For example, the data collection unit can collect this information through interviews. Interview formats include, but are not limited to, face-to-face interviews, online interviews, and the content of the questions. For example, the data collection unit can collect detailed information about users through face-to-face interviews. It can also collect information through online interviews. Furthermore, the data collection unit can dynamically change the content of the questions and collect detailed information based on the user's responses. Specifically, in face-to-face interviews, a professional interviewer directly interacts with the user, collecting non-verbal information such as facial expressions and tone of voice. In online interviews, video calls or chat formats are used to allow users to answer in a relaxed environment. The questions cover a wide range of topics, including the user's hobbies, daily life, stress levels, and relaxation methods, and the next questions dynamically change based on the user's answers. For example, if a user answers "I like natural scents," the next question might be a more specific one, such as "What kind of natural scents do you particularly like?" In this way, the data collection unit can delve deeper into users' detailed preferences and values, enabling it to collect more accurate information. Furthermore, the data collection unit respects user privacy and implements security measures to properly manage the collected information. For example, collected data is encrypted and stored on secure servers. This allows the data collection unit to efficiently collect detailed information while gaining user trust.

[0065] The analysis unit analyzes the information collected by the data collection unit and blends the optimal fragrance for the user. For example, the analysis unit can blend relaxing or energizing fragrances based on information about the user's preferences, values, and outlook on life. For instance, the analysis unit might blend lavender and chamomile as relaxing fragrances. It can also blend citrus fragrances as energizing fragrances. Furthermore, the analysis unit can blend the optimal fragrance according to the user's state of mind. Specifically, the analysis unit uses AI to analyze the collected information from multiple perspectives. The AI ​​uses natural language processing technology to analyze the text data of interviews and extract the user's emotions and values. For example, if a user states that they "feel stressed at work," the AI ​​will suggest a relaxing fragrance based on that information. If a user states that they "want a fragrance that energizes them in the morning," the AI ​​will suggest an energizing fragrance. In addition, the analysis unit optimizes the fragrance blend based on the user's past fragrance preference data and feedback. For example, if a user has previously preferred the scent of lavender, a more personalized fragrance can be provided by blending lavender with other scents. The analysis unit comprehensively analyzes this information and generates a recipe for blending the optimal fragrance for the user. This recipe details the fragrance components, blending ratios, and blending methods, allowing for accurate reproduction of the blending process. As a result, the analysis unit can blend fragrances with high precision to meet the individual needs of each user.

[0066] The shipping department sends fragrance samples of the fragrance blended by the analysis department to the user. The shipping department can, for example, send the blended fragrance as a fragrance sample to the user. The shipping department can send the fragrance samples by mail or courier service. The shipping department can also send fragrance samples by email. Furthermore, the shipping department can arrange for users to receive fragrance samples at a specific location, according to their preference. Specifically, the shipping department packs the fragrance samples in small bottles or trial-sized packages and uses appropriate packaging to prevent leakage or deterioration of the fragrance. When using mail or courier service, the shipping department provides a tracking number so that the user can check the delivery status. When using email, it is also possible to provide fragrance samples in digital format. For example, a digital recipe containing the fragrance components and blending method can be sent so that the user can recreate the fragrance at home. Furthermore, the shipping department can arrange for users to receive fragrance samples at a specific location, according to their preference. For example, a service can be provided that allows users to receive fragrance samples at partner stores or cafes. This allows users to receive fragrance samples at their convenience. The distribution department can provide users with fragrance samples quickly and reliably through these services, thereby increasing user satisfaction.

[0067] The service provider will provide users with a fragrance sample sent by the delivery service if they like it after trying it. The service provider can, for example, offer a service that provides users with a fragrance on a regular basis if they like the sample. The service provider can also provide fragrances at specific times according to the user's wishes. Furthermore, the service provider can adjust the fragrance delivery method based on user feedback. Specifically, after users try the fragrance sample, the service provider collects feedback through an online platform. Users evaluate the fragrance's strength, duration, and degree of preference, and send this information to the service provider. Based on this feedback, the service provider adjusts the fragrance's ingredients and blending ratios to provide the user with the optimal fragrance. The service provider can also offer a service that provides fragrances on a regular basis. For example, they can deliver a new fragrance to the user's home once a month, allowing the user to always enjoy a new scent. Additionally, the service provider can offer a service that delivers fragrances at specific times. For example, they can provide a special fragrance to coincide with a user's birthday or a special event, allowing the user to enjoy special moments even more. Through these services, the service provider can offer users a personalized fragrance experience and increase user satisfaction.

[0068] The data collection unit can collect information on users' preferences, values, and outlook on life through interviews. The data collection unit can collect detailed information on users, for example, through face-to-face interviews. The data collection unit can also collect information on users through online interviews, for example. The data collection unit can also dynamically change the content of interview questions and collect detailed information based on user responses, for example. This allows for the collection of detailed information through interviews. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the information collected through interviews into AI, which can then analyze the information to collect detailed information.

[0069] The analysis unit can blend relaxing and energizing scents based on information about the user's preferences, values, and outlook on life. For example, the analysis unit can blend lavender and chamomile as relaxing scents. For example, the analysis unit can also blend citrus scents as energizing scents. The analysis unit can also blend the optimal scent according to the user's state. This allows for the blending of scents tailored to the user's state. 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 information about the user's preferences, values, and outlook on life into the AI, which can then analyze the information and blend the optimal scent.

[0070] The delivery unit can send the blended fragrance to the user as a fragrance sample. The delivery unit can send the fragrance sample by mail or courier, for example. The delivery unit can also send the fragrance sample by email, for example. The delivery unit can also arrange for the user to receive the fragrance sample at a specific location, for example, according to the user's preference. This allows the user to try the fragrance. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the blended fragrance into the AI, and the AI ​​can select a method for sending the fragrance sample.

[0071] The service provider can offer a service that provides users with a fragrance sample they like on a regular basis. For example, the service provider can offer a service that provides fragrances on a regular basis. The service provider can also offer fragrances at specific times according to the user's wishes. The service provider can also adjust the method of fragrance delivery based on user feedback. This allows users to continuously use their favorite fragrances. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback into AI, which can then select the optimal delivery method.

[0072] The data collection unit can estimate the user's emotions and dynamically change the interview questions based on the estimated emotions. For example, if the user is relaxed, the data collection unit can add more detailed questions to gather deeper information. For example, if the user is stressed, the data collection unit can change to simpler questions to reduce the burden. For example, if the user is excited, the data collection unit can add more engaging questions to facilitate information gathering. This allows for the collection of more detailed information by providing questions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can estimate the emotions and dynamically change the questions.

[0073] The data collection unit can analyze the user's past fragrance selection history and generate optimal questions. For example, the data collection unit can generate relevant questions based on the user's past fragrance selection trends. The data collection unit can also generate questions about fragrances to avoid based on fragrances the user has avoided in the past. The data collection unit can also generate questions about new fragrance suggestions based on the user's past selection history. This allows for the generation of more appropriate questions based on past selection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past fragrance selection history into AI, which can then generate optimal questions.

[0074] The data collection unit can collect data on users' daily activities in addition to interviews, allowing for a more detailed understanding of their preferences, values, and outlook on life. For example, the data collection unit can analyze users' smartphone usage history to understand their daily activity patterns. It can also analyze users' social media posts to understand their preferences and values. It can also understand places visited and activities based on users' location information. This allows for a more detailed understanding of information by collecting data on daily activities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input users' daily activity data into AI, which can then analyze the data to understand more detailed information.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of interviews based on the estimated emotions. For example, the data collection unit can conduct interviews during times when the user is relaxed. The data collection unit can also avoid conducting interviews during times when the user is stressed. For example, the data collection unit can conduct interviews during times when the user is excited to facilitate information gathering. This allows for more effective information gathering by conducting interviews at times that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can estimate the emotions and adjust the timing of interviews.

[0076] The data collection unit can analyze a user's social media activity and supplement information about their preferences, values, and outlook on life. For example, the data collection unit can analyze a user's posts to understand their preferences and values. For example, the data collection unit can analyze the accounts a user follows to understand their areas of interest. For example, the data collection unit can analyze a user's "likes" and comment history to understand their preferences. In this way, by analyzing social media activity, information about preferences, values, and outlook on life can be supplemented. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into AI, which can analyze the data and supplement the information.

[0077] The data collection unit can collect regional scent preferences, taking into account the user's geographical location. For example, the data collection unit can collect scents specific to the area where the user lives. The data collection unit can also collect scent preferences for areas the user has visited. For example, the data collection unit can analyze regional scent trends based on the user's location information. This allows for the collection of regional scent preferences by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then analyze the data to collect regional scent preferences.

[0078] The analysis unit can estimate the user's emotions and adjust the fragrance blending parameters based on the estimated emotions. For example, if the user is relaxed, the analysis unit can emphasize relaxing scents. For example, if the user is stressed, the analysis unit can also emphasize stress-reducing scents. For example, if the user is excited, the analysis unit can also emphasize energetic scents. This makes it possible to blend fragrances according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. 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, or not using AI. For example, the analysis unit can input user emotion data into AI, which can estimate the emotions and adjust the fragrance blending parameters.

[0079] The analysis unit can improve the accuracy of the blend by referring to the user's past fragrance selection history during analysis. For example, the analysis unit can improve the accuracy of the blend based on the user's past fragrance selection trends. The analysis unit can also improve the accuracy of the blend based on fragrances the user has avoided in the past. For example, the analysis unit can perform blending based on the user's past selection history to suggest new fragrances. This allows for improved blending accuracy based on past selection history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past fragrance selection history into AI, and the AI ​​can analyze the data to improve blending accuracy.

[0080] The analysis unit can customize the fragrance blend based on the user's lifestyle and activity patterns during analysis. For example, the analysis unit can blend a fragrance to match the time of day when the user wants to relax. The analysis unit can also blend a fragrance to match the time of day when the user wants to be energetic. The analysis unit can also blend the optimal fragrance based on the user's activity patterns. This allows for the customization of the fragrance blend based on lifestyle and activity 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 data on the user's lifestyle and activity patterns into the AI, which can then analyze the data to customize the fragrance blend.

[0081] The analysis unit can estimate the user's emotions and adjust the fragrance blending order based on the estimated emotions. For example, if the user is relaxed, the analysis unit may blend relaxing fragrances first. If the user is stressed, the analysis unit may also blend stress-reducing fragrances first. If the user is excited, the analysis unit may also blend energetic fragrances first. This allows the fragrance blending order to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not using AI. For example, the analysis unit can input user emotion data into AI, which can estimate the emotions and adjust the fragrance blending order.

[0082] The analysis unit can blend fragrances while considering the user's health condition and allergy information during analysis. For example, if the user has allergies, the analysis unit will blend fragrances while avoiding those ingredients. For example, the analysis unit can also blend fragrances with relaxing effects according to the user's health condition. For example, the analysis unit can also blend energetic fragrances according to the user's health condition. This allows for the blending of the optimal fragrance while considering health condition and allergy information. 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 user's health condition and allergy information into the AI, which can then analyze the data and blend the optimal fragrance.

[0083] The analysis unit can blend fragrances while considering the user's cultural background and religious beliefs during analysis. For example, the analysis unit can blend specific fragrances to match the user's cultural background. For example, the analysis unit can also blend fragrances while avoiding specific fragrances to match the user's religious beliefs. For example, the analysis unit can blend the optimal fragrance based on the user's cultural background and religious beliefs. This allows for the blending of the optimal fragrance while considering cultural background and religious beliefs. 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 data on the user's cultural background and religious beliefs into the AI, which can then analyze the data and blend the optimal fragrance.

[0084] The delivery unit can estimate the user's emotions and adjust the method of sending fragrance samples based on the estimated emotions. For example, if the user is relaxed, the delivery unit can send fragrance samples at a leisurely pace. If the user is stressed, the delivery unit can also send fragrance samples quickly. If the user is excited, the delivery unit can also send fragrance samples in special packaging. This allows for a delivery method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. 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 delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into an AI, which can estimate the emotions and adjust the delivery method.

[0085] The sending unit can select the optimal sending timing by referring to the user's past sample receipt history at the time of sending. For example, the sending unit can select the optimal sending timing based on the timing of samples the user has received in the past. The sending unit can also predict the optimal sending timing from the user's past receipt history. For example, the sending unit can analyze the user's past receipt history and select the optimal sending timing. This allows the optimal sending timing to be selected based on past receipt history. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's past sample receipt history into AI, and the AI ​​can analyze the data to select the optimal sending timing.

[0086] The delivery unit can customize the delivery schedule based on the user's daily rhythm at the time of delivery. For example, the delivery unit can send fragrance samples to coincide with the time when the user is relaxing. For example, the delivery unit can also send fragrance samples to coincide with the time when the user wants to be energetic. For example, the delivery unit can customize the optimal delivery schedule based on the user's daily rhythm. This allows the delivery unit to provide an optimal delivery schedule based on the user's daily rhythm. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input data on the user's daily rhythm into AI, and the AI ​​can analyze the data to customize the delivery schedule.

[0087] The delivery unit can estimate the user's emotions and adjust the package design of the fragrance sample based on the estimated emotions. For example, if the user is relaxed, the delivery unit can provide a calming package design. If the user is stressed, the delivery unit can also provide a simple and highly visible package design. If the user is excited, the delivery unit can also provide a visually stimulating package design. This allows for the provision of package designs that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. 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 delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into an AI, which can estimate the emotion and adjust the package design.

[0088] The shipping unit can select the optimal shipping method at the time of shipping, taking into account the user's geographical location information. For example, the shipping unit can select the optimal shipping method based on the shipping company in the area where the user lives. The shipping unit can also select the optimal shipping method based on the shipping company in the area the user has visited. The shipping unit can also select the optimal shipping method based on the user's geographical location information. This allows the optimal shipping method to be selected based on geographical location information. Some or all of the above processing in the shipping unit may be performed using AI, for example, or without AI. For example, the shipping unit can input the user's geographical location information into AI, and the AI ​​can analyze the data to select the optimal shipping method.

[0089] The sending unit can analyze the user's social media activity at the time of sending to personalize the content of the message. For example, the sending unit can personalize the content based on the user's posts. For example, the sending unit can personalize the content based on the accounts the user follows. For example, the sending unit can personalize the content based on the user's "likes" and comment history. This allows the sending unit to personalize the content based on social media activity. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the user's social media activity data into AI, which can then analyze the data to personalize the content of the message.

[0090] The service provider can estimate the user's emotions and adjust the frequency of scent delivery based on the estimated emotions. For example, if the user is relaxed, the service provider can reduce the frequency of scent delivery. For example, if the user is stressed, the service provider can increase the frequency of scent delivery. For example, if the user is excited, the service provider can adjust the frequency of scent delivery. This allows for adjustment of the scent delivery frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into AI, which can estimate the emotions and adjust the delivery frequency.

[0091] The dispensing unit can select the optimal dispensing method by referring to the user's past fragrance usage history at the time of dispensing. For example, the dispensing unit can select the optimal dispensing method based on the frequency of fragrances the user has used in the past. For example, the dispensing unit can predict the optimal dispensing method from the user's past usage history. For example, the dispensing unit can analyze the user's past usage history and select the optimal dispensing method. This allows the optimal dispensing method to be selected based on past usage history. Some or all of the above processing in the dispensing unit may be performed using AI, for example, or without AI. For example, the dispensing unit can input the user's past fragrance usage history into AI, and the AI ​​can analyze the data to select the optimal dispensing method.

[0092] The service provider can customize the fragrance delivery schedule based on the user's lifestyle at the time of delivery. For example, the service provider can deliver fragrance according to the time of day when the user wants to relax. For example, the service provider can also deliver fragrance according to the time of day when the user wants to be energetic. For example, the service provider can customize the optimal delivery schedule based on the user's lifestyle. This allows for the provision of an optimal delivery schedule based on the user's lifestyle. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user lifestyle data into AI, and the AI ​​can analyze the data to customize the delivery schedule.

[0093] The service provider can estimate the user's emotions and adjust the variety of scents offered based on the estimated emotions. For example, if the user is relaxed, the service provider can offer a relaxing scent. For example, if the user is stressed, the service provider can also offer a stress-reducing scent. For example, if the user is excited, the service provider can also offer an energetic scent. This allows for the provision of scent variations that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into AI, which can estimate the emotions and adjust the variety of scents offered.

[0094] The service provider can provide the optimal fragrance by considering the user's geographical location information at the time of delivery. For example, the service provider can provide a fragrance specific to the area where the user lives. For example, the service provider can also provide a fragrance specific to the area the user has visited. For example, the service provider can provide the optimal fragrance based on the user's geographical location information. This allows the service provider to provide the optimal fragrance 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 AI, and the AI ​​can analyze the data to provide the optimal fragrance.

[0095] The service provider can analyze the user's social media activity at the time of delivery to personalize the content offered. For example, the service provider can personalize the content based on the user's posts. For example, the service provider can personalize the content based on the accounts the user follows. For example, the service provider can personalize the content based on the user's "likes" and comment history. This allows the service provider to personalize the content 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 activity data into AI, which can then analyze the data to personalize the content offered.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The fragrance blending system collects information on the user's preferences, values, and outlook on life, as well as their health status and allergies. The analysis unit then considers this information when blending fragrances. For example, the collection unit gathers information to avoid ingredients the user is allergic to. It also collects information on the user's health status, which can be considered when blending relaxing or energetic fragrances. Furthermore, the collection unit can emphasize or avoid specific ingredients depending on the user's health condition. This enables fragrance blending that takes into account the user's health status and allergy information.

[0098] The fragrance blending system can collect information on the user's preferences, values, and worldview, as well as their cultural background and religious beliefs, and then use this information in its analysis unit to blend fragrances. For example, the collection unit can gather information on the user's cultural background and consider it when blending specific fragrances. It can also collect information on the user's religious beliefs and provide information to avoid certain ingredients. Furthermore, the collection unit can emphasize or avoid certain fragrances based on the user's cultural background and religious beliefs. This makes it possible to blend fragrances that take the user's cultural background and religious beliefs into consideration.

[0099] The fragrance blending system collects data on the user's daily activities, in addition to their preferences, values, and outlook on life. The analysis unit then considers this information to blend fragrances. For example, the data collection unit analyzes the user's smartphone usage history to understand their daily activity patterns. It also analyzes the user's social media posts to understand their preferences and values. Furthermore, the data collection unit uses the user's location information to understand the places they have visited and the activities they have engaged in. By collecting this data on daily activities, the system can obtain more detailed information.

[0100] The fragrance blending system collects the user's preferences, values, and outlook on life, as well as their past fragrance selection history, and the analysis unit considers this information when blending fragrances. For example, the collection unit generates relevant questions based on the user's past fragrance choices. It can also generate questions about fragrances to avoid based on fragrances the user has avoided in the past. Furthermore, the collection unit can generate questions regarding new fragrance suggestions based on the user's past selection history. This allows for the generation of more appropriate questions based on past selection history.

[0101] The fragrance blending system collects the user's preferences, values, and outlook on life, as well as their geographical location information, and the analysis unit considers this information when blending fragrances. For example, the collection unit can collect the unique scents of the area where the user lives. It can also collect the user's fragrance preferences from areas they have visited. Furthermore, the collection unit can analyze regional fragrance trends based on the user's location information. This allows the system to collect regional fragrance preferences by considering geographical location information.

[0102] The fragrance blending system can estimate the user's emotions and dynamically change the interview questions based on those emotions. For example, if the user is relaxed, the data collection unit can add more detailed questions to gather deeper information. If the user is stressed, the data collection unit can change to simpler questions to reduce the burden. Furthermore, if the user is excited, the data collection unit can add intriguing questions to facilitate information gathering. By providing questions tailored to the user's emotions, more detailed information can be gathered.

[0103] The fragrance blending system can estimate the user's emotions and adjust the timing of interviews based on those estimates. For example, the data collection unit can conduct interviews when the user is relaxed. It can also avoid conducting interviews when the user is stressed. Furthermore, the data collection unit can conduct interviews when the user is excited to facilitate information gathering. This allows for more effective information gathering by conducting interviews at times that align with the user's emotions.

[0104] The fragrance blending system can estimate the user's emotions and adjust the fragrance blending parameters based on those emotions. For example, if the user is relaxed, the analysis unit can emphasize relaxing scents. If the user is stressed, the analysis unit can emphasize stress-reducing scents. Furthermore, if the user is excited, the analysis unit can emphasize energetic scents. This makes it possible to blend fragrances according to the user's emotions.

[0105] The fragrance blending system can estimate the user's emotions and adjust the method of sending fragrance samples based on those emotions. For example, if the user is relaxed, the system will send fragrance samples at a leisurely pace. If the user is stressed, the system can send fragrance samples quickly. Furthermore, if the user is excited, the system can send fragrance samples in special packaging. This allows the system to provide a delivery method tailored to the user's emotions.

[0106] The fragrance blending system can estimate the user's emotions and adjust the packaging design of the fragrance sample based on those emotions. For example, if the user is relaxed, the system can provide a calming package design. If the user is stressed, it can provide a simple and highly visible package design. Furthermore, if the user is excited, it can provide a visually stimulating package design. This allows the system to provide package designs that match the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects information about the user's preferences, values, and outlook on life. The data collection unit collects information using methods such as face-to-face interviews, online interviews, and dynamically changing the content of the questions. Step 2: The analysis unit analyzes the information collected by the data collection unit and blends the optimal fragrance for the user. For example, the analysis unit can blend fragrances that have a relaxing effect or fragrances that are energizing. Step 3: The sending unit sends the fragrance blended by the analysis unit to the user as a fragrance sample. The sending unit can send the fragrance sample by mail, courier service, or email, for example. Step 4: The supplying department allows the user to try the fragrance samples sent by the sending department, and provides the fragrance if the user likes it. The supplying department can, for example, provide fragrances regularly or at specific times.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, transmission unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information on the user's preferences, values, and outlook on life using the microphone 38B and touch panel 38A of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and blends the optimal fragrance based on the collected information. The transmission unit sends fragrance samples to the user using the communication I / F 44 of the smart device 14. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and periodically provides the user with their favorite fragrance. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, transmission 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 uses the microphone 238 of the smart glasses 214 to collect information on the user's preferences, values, and outlook on life. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and blends the optimal fragrance based on the collected information. The transmission unit sends fragrance samples to the user using the communication I / F 44 of the smart glasses 214. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and periodically provides the user with their favorite fragrances. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, transmission unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the microphone 238 of the headset terminal 314 to collect information on the user's preferences, values, and outlook on life. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and blends the optimal fragrance based on the collected information. The transmission unit sends fragrance samples to the user using the communication I / F 44 of the headset terminal 314. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and periodically provides the user with their favorite fragrances. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, sending unit, and providing unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the microphone 238 of the robot 414 to collect information on the user's preferences, values, and outlook on life. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and blends the optimal fragrance based on the collected information. The sending unit sends fragrance samples to the user using the communication I / F 44 of the robot 414. The providing unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and periodically provides the user with their favorite fragrance. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection department that collects information on users' preferences, values, and outlook on life, The analysis unit analyzes the information collected by the collection unit and blends the optimal fragrance for the user, A sending unit that sends the fragrance blended by the analysis unit to the user as a fragrance sample, The system includes a supply unit that allows the user to try fragrance samples sent by the aforementioned supply unit and, if they like the fragrance, provides that fragrance to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information about users' preferences, values, and outlook on life through interviews. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on information about the user's preferences, values, and outlook on life, we blend fragrances that have a relaxing effect or energizing scents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned transmission unit is The blended fragrance is sent to the user as a fragrance sample. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, A service that provides users with a regular supply of fragrance samples if they like the one they try. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and dynamically changes the interview questions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past fragrance selection history to generate optimal questions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is In addition to interviews, we collect data on users' daily behavior to gain a more detailed understanding of their preferences, values, and outlook on life. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze users' social media activity to supplement information about their preferences, values, and outlook on life. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system collects regionally specific scent preferences, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the fragrance blending parameters based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the system improves blending accuracy by referencing the user's past fragrance selection history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the fragrance blend is customized based on the user's lifestyle and activity patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the fragrance blending order based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the fragrance is blended taking into account the user's health status and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the fragrance is blended taking into account the user's cultural background and religious beliefs. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned transmission unit is The system estimates the user's emotions and adjusts the method of sending fragrance samples based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned transmission unit is When sending samples, the system will refer to the user's past sample receipt history to select the optimal sending timing. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned transmission unit is When sending, the delivery schedule is customized based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned transmission unit is The system estimates the user's emotions and adjusts the packaging design of the fragrance samples based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned transmission unit is When shipping, the system selects the most suitable delivery method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned transmission unit is When sending messages, the system analyzes the user's social media activity to personalize the content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the frequency of scents provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the product, the system will refer to the user's past fragrance usage history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, At the time of delivery, the fragrance delivery schedule is customized based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the range of fragrances offered based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the product, the optimal scent is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, At the time of delivery, the content delivered will be personalized by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 department that collects information on users' preferences, values, and outlook on life, The analysis unit analyzes the information collected by the collection unit and blends the optimal fragrance for the user, A sending unit that sends the fragrance blended by the analysis unit to the user as a fragrance sample, The system includes a supply unit that allows the user to try fragrance samples sent by the aforementioned supply unit and, if they like the fragrance, provides that fragrance to the user. A system characterized by the following features.

2. The aforementioned collection unit is We collect information about users' preferences, values, and outlook on life through interviews. The system according to feature 1.

3. The aforementioned analysis unit, Based on information about the user's preferences, values, and outlook on life, we blend fragrances that have a relaxing effect or energizing scents. The system according to feature 1.

4. The aforementioned transmission unit is The blended fragrance is sent to the user as a fragrance sample. The system according to feature 1.

5. The aforementioned supply unit is, A service that provides users with a regular supply of fragrance samples if they like the one they try. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and dynamically changes the interview questions based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past fragrance selection history to generate optimal questions. The system according to feature 1.

8. The aforementioned collection unit is In addition to interviews, we collect data on users' daily behavior to gain a more detailed understanding of their preferences, values, and outlook on life. The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of interviews based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze users' social media activity to supplement information about their preferences, values, and outlook on life. The system according to feature 1.

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