system

Abstract

The system according to the embodiment aims to effectively utilize scent elements and efficiently create scents. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a creation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The creation unit creates a fragrance recipe based on the analysis results obtained by the analysis unit. The creation unit creates a fragrance based on the fragrance recipe created by the creation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the process for effectively utilizing fragrance elements is complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to effectively utilize scent elements and efficiently create scents. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a creation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The creation unit creates a fragrance recipe based on the analysis results obtained by the analysis unit. The creation unit creates a fragrance based on the fragrance recipe created by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize scent elements and efficiently create scents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A Solution AI according to an embodiment of the present invention is a system for effectively utilizing scent elements in corporate branding and purchasing behavior. This system collects data related to corporate branding and purchasing behavior, analyzes it using AI to identify optimal scent elements, generates a scent recipe, and provides comprehensive support for the actual scent creation process. For example, a data collection unit collects data such as customer purchase history, survey results, and social media responses, and an analysis unit uses AI to analyze the collected data to identify the scent elements preferred by the customer. A recipe generation unit generates a scent recipe using AI based on the identified scent elements, and a scent creation unit actually creates a scent based on the generated scent recipe. This Solution AI provides comprehensive support for effectively utilizing scent elements in corporate branding and purchasing behavior. This allows companies to increase customer purchasing motivation and improve their brand image. Thus, Solution AI can provide comprehensive support for effectively utilizing scent elements in corporate branding and purchasing behavior.

[0029] A solution AI according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a creation unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, numerical data, and image data. The collection unit can collect data such as customer purchase history, survey results, and reactions on social media. For example, the collection unit collects customer purchase history and acquires information such as purchase date and time, purchased products, and purchase frequency. The collection unit can also collect survey results and acquire information such as question items, response formats, and aggregation methods. The collection unit can also collect reactions on social media and acquire information such as post content, number of likes, and number of comments. The analysis unit uses AI to analyze the collected data. Examples of analysis include, but are not limited to, statistical analysis and the use of machine learning algorithms. For example, the analysis unit statistically analyzes the collected data to identify customer purchasing patterns. The analysis unit can also use machine learning algorithms to identify the elements of fragrances preferred by customers. The analysis unit can also use natural language processing technology to analyze reactions on social media and estimate customer emotions. The generation unit generates a fragrance recipe using AI based on the identified fragrance elements. Examples of generation include, but are not limited to, deep learning and natural language processing. For example, the generation unit generates a fragrance recipe using deep learning. The generation unit can also generate a fragrance recipe using natural language processing technology. Furthermore, the generation unit can determine the type and blend ratio of fragrance ingredients based on the identified fragrance elements. The creation unit actually creates the fragrance based on the generated fragrance recipe. Examples of creation include, but are not limited to, a blending method and equipment to be used. For example, the creation unit blends fragrance ingredients to create the fragrance. The creation unit can also create the fragrance using specific equipment. This allows the solution AI according to the embodiment to provide comprehensive support for effectively utilizing fragrance elements in corporate branding and purchasing behavior.

[0030] The collection unit can collect data including customer purchase history, survey results, and reactions on social media. The collection unit, for example, collects customer purchase history. For example, the collection unit acquires information such as purchase date and time, purchased products, and purchase frequency. The collection unit can also collect survey results. For example, the collection unit acquires information such as question items, answer format, and aggregation method. The collection unit can also collect reactions on social media. For example, the collection unit acquires information such as post content, number of likes, and number of comments. By collecting data such as customer purchase history, survey results, and reactions on social media, more accurate analysis is possible. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input customer purchase history into AI and have the AI ​​identify purchasing patterns.

[0031] The analysis unit can use AI to analyze the collected data and identify the fragrance elements preferred by customers. The analysis unit, for example, performs statistical analysis on the collected data. For example, the analysis unit can identify customer purchasing patterns. The analysis unit can also use machine learning algorithms to identify the fragrance elements preferred by customers. For example, the analysis unit can use deep learning to identify the fragrance elements preferred by customers. The analysis unit can also use natural language processing technology to analyze reactions on social media and estimate customer emotions. For example, the analysis unit can analyze the content of posts on social media and estimate customer emotions. By doing so, the analysis unit can identify the fragrance elements preferred by customers, thereby generating more effective fragrance recipes. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​identify the fragrance elements preferred by customers.

[0032] The generation unit allows the AI ​​to generate a fragrance recipe based on the identified fragrance elements. The generation unit generates the fragrance recipe using, for example, deep learning. For example, the generation unit determines the type and blend ratio of fragrances based on the identified fragrance elements. The generation unit can also generate the fragrance recipe using natural language processing technology. For example, the generation unit generates the fragrance recipe based on the identified fragrance elements. In this way, the AI ​​generates the fragrance recipe, allowing the fragrance recipe to be created efficiently. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the identified fragrance elements into the AI ​​and have the AI ​​generate the fragrance recipe.

[0033] The creation unit can actually create a fragrance based on the generated fragrance recipe. The creation unit, for example, mixes fragrances to create the fragrance. For example, the creation unit creates the fragrance using specific equipment. The creation unit can also create a fragrance based on the generated fragrance recipe. For example, the creation unit creates a fragrance based on the type and blending ratio of fragrances. In this way, the fragrance can be actually provided by creating the fragrance based on the generated fragrance recipe. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the generated fragrance recipe into AI and have the AI ​​create the fragrance.

[0034] The collection unit can analyze the user's past purchase history and survey results and select an appropriate data collection method. The collection unit, for example, collects related data based on products the user has purchased in the past. For example, the collection unit collects data related to products the user has purchased in the past. The collection unit can also collect data on areas of interest based on survey results the user has answered in the past. For example, the collection unit collects data on areas of interest based on survey results the user has answered in the past. The collection unit can also collect data related to a specific season from the user's purchase history. For example, the collection unit collects data related to a specific season based on the user's purchase history. This makes it possible to select an optimal data collection method by analyzing the user's past purchase history and survey results. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past purchase history and survey results into AI and have the AI ​​select an optimal data collection method.

[0035] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is currently raising a child, the collection unit prioritizes collecting data related to childcare. For example, the collection unit collects data related to childcare and provides information about childcare. Furthermore, if the user is interested in health, the collection unit can prioritize collecting data related to health. For example, the collection unit collects data related to health and provides information about health. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to travel. For example, the collection unit collects data related to travel and provides information about travel. In this way, by filtering data based on the user's current living situation and areas of interest, more relevant data can be collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current living situation and areas of interest into AI and have the AI ​​perform data filtering.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is in a specific area, the collection unit collects data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. For example, when the user is traveling, the collection unit collects data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the area around the user's home. For example, when the user is at home, the collection unit collects data related to the area around the user's home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to AI and cause the AI ​​to collect highly relevant data.

[0037] During data collection, the collection unit can analyze the user's social media activity and collect related data. The collection unit, for example, collects related data based on content shared by the user on social media. For example, the collection unit collects data related to content shared by the user on social media. The collection unit can also collect related data based on accounts the user follows on social media. For example, the collection unit collects data related to accounts the user follows on social media. The collection unit can also collect related data based on groups the user joins on social media. For example, the collection unit collects data related to groups the user joins on social media. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activity into AI and cause the AI ​​to collect related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a concise analysis on data of low importance. For example, the analysis unit performs a concise analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a purchase pattern analysis algorithm to purchase history data. For example, the analysis unit applies a purchase pattern analysis algorithm to purchase history data. The analysis unit can also apply a text analysis algorithm to survey result data. For example, the analysis unit applies a text analysis algorithm to survey result data. The analysis unit can also apply a sentiment analysis algorithm to SNS reaction data. For example, the analysis unit applies a sentiment analysis algorithm to SNS reaction data. This enables more accurate analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI and have the AI ​​apply different analysis algorithms.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the most recent data to grasp the latest trends. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit analyzes the most recent data while referring to past data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. For example, the analysis unit prioritizes analyzing data collected during a specific campaign period. This enables efficient analysis by determining the priority of analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI and have the AI ​​determine the priority of analysis.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit prioritizes analysis of data with high relevance to extract important insights. The analysis unit can also analyze data with medium relevance next. For example, the analysis unit analyzes data with medium relevance next to provide supplementary information. The analysis unit can also analyze data with low relevance last. For example, the analysis unit analyzes data with low relevance last to achieve overall balance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0042] The generation unit can adjust the level of detail of the recipe based on the importance of the fragrance elements during generation. For example, the generation unit generates a detailed recipe for a fragrance element with high importance. For example, the generation unit generates a detailed recipe for a fragrance element with high importance. The generation unit can also generate a concise recipe for a fragrance element with low importance. For example, the generation unit generates a concise recipe for a fragrance element with low importance. Furthermore, the generation unit can also generate a recipe with a moderate level of detail for a fragrance element with medium importance. For example, the generation unit generates a recipe with a moderate level of detail for a fragrance element with medium importance. In this way, by adjusting the level of detail of the recipe based on the importance of the fragrance elements, a more effective fragrance recipe can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the fragrance elements to AI and cause the AI ​​to adjust the level of detail of the recipe.

[0043] During generation, the generation unit can apply different generation algorithms depending on the fragrance category. For example, the generation unit applies a floral generation algorithm to a floral fragrance. For example, the generation unit applies a floral generation algorithm to a floral fragrance. The generation unit can also apply a citrus generation algorithm to a citrus fragrance. For example, the generation unit applies a citrus generation algorithm to a citrus fragrance. The generation unit can also apply a woody generation algorithm to a woody fragrance. For example, the generation unit applies a woody generation algorithm to a woody fragrance. In this way, by applying different generation algorithms depending on the fragrance category, it is possible to generate a fragrance recipe with higher accuracy. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the fragrance category to AI and cause the AI ​​to apply different generation algorithms.

[0044] At the time of generation, the generation unit can determine the priority of recipes based on the collection time of fragrance elements. The generation unit, for example, prioritizes incorporating the most recent fragrance elements into the recipe. For example, the generation unit prioritizes incorporating the most recent fragrance elements into the recipe. The generation unit can also emphasize the most recent elements while referring to past fragrance elements. For example, the generation unit incorporates the most recent elements into the recipe while referring to past fragrance elements. Furthermore, the generation unit can also prioritize incorporating fragrance elements collected during a specific period into the recipe. For example, the generation unit prioritizes incorporating fragrance elements collected during a specific period into the recipe. In this way, by determining the priority of recipes based on the collection time of fragrance elements, more effective fragrance recipes can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the collection time of fragrance elements into AI and have the AI ​​determine the priority of recipes.

[0045] The generation unit can adjust the order of the recipe based on the relevance of the scent elements during generation. For example, the generation unit prioritizes incorporating scent elements with high relevance into the recipe. For example, the generation unit prioritizes incorporating scent elements with high relevance into the recipe. The generation unit can also incorporate scent elements with medium relevance into the recipe next. For example, the generation unit can incorporate scent elements with medium relevance into the recipe next. The generation unit can also incorporate scent elements with low relevance into the recipe last. For example, the generation unit can incorporate scent elements with low relevance into the recipe last. In this way, by adjusting the order of the recipe based on the relevance of the scent elements, a more effective scent recipe can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the scent elements into AI and have the AI ​​adjust the order of the recipe.

[0046] When creating a fragrance, the creation unit can analyze the user's past fragrance preferences and select the optimal creation method. The creation unit, for example, creates an optimal fragrance based on fragrance elements that the user previously liked. For example, the creation unit creates an optimal fragrance based on fragrance elements that the user previously liked. The creation unit can also create a fragrance by eliminating fragrance elements that the user previously avoided. For example, the creation unit creates a fragrance by eliminating fragrance elements that the user previously avoided. Furthermore, the creation unit can analyze the user's past fragrance preferences and create a fragrance that matches the season or event. For example, the creation unit analyzes the user's past fragrance preferences and creates a fragrance that matches the season or event. In this way, by analyzing the user's past fragrance preferences, a more appropriate fragrance can be created. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's past fragrance preferences into AI and have the AI ​​select the optimal creation method.

[0047] The creation unit can customize the fragrance creation means based on the user's current living situation at the time of creation. For example, if the user is currently raising a child, the creation unit creates a fragrance suitable for childcare. For example, the creation unit creates a fragrance suitable for childcare when the user is currently raising a child. Furthermore, if the user is interested in health, the creation unit can also create a fragrance that is good for health. For example, the creation unit creates a fragrance that is good for health when the user is interested in health. Furthermore, if the user is traveling, the creation unit can also create a fragrance that is suitable for travel. For example, the creation unit creates a fragrance that is suitable for travel when the user is traveling. In this way, by customizing the fragrance creation means based on the user's current living situation, a more appropriate fragrance can be created. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's current living situation into AI and have the AI ​​customize the fragrance creation means.

[0048] When creating a fragrance, the creation unit can select an appropriate fragrance creation method by taking into account the user's geographical location information. For example, when the user is in a specific area, the creation unit creates a fragrance appropriate for that area. For example, when the user is in a specific area, the creation unit creates a fragrance appropriate for that area. Furthermore, when the user is traveling, the creation unit can also create a fragrance appropriate for the travel destination. For example, when the user is traveling, the creation unit creates a fragrance appropriate for the travel destination. Furthermore, when the user is at home, the creation unit can also create a fragrance appropriate for the area around the user's home. For example, when the user is at home, the creation unit creates a fragrance appropriate for the area around the user's home. In this way, by taking the user's geographical location information into account, a more appropriate fragrance can be created. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's geographical location information into AI and have the AI ​​select an appropriate fragrance creation method.

[0049] During creation, the creation unit can analyze the user's social media activity and suggest a means for creating the fragrance. The creation unit, for example, creates a related fragrance based on content shared by the user on social media. For example, the creation unit creates a fragrance related to content shared by the user on social media. The creation unit can also create a related fragrance based on accounts the user follows on social media. For example, the creation unit creates a fragrance related to accounts the user follows on social media. The creation unit can also create a related fragrance based on groups the user participates in on social media. For example, the creation unit creates a fragrance related to groups the user participates in on social media. This makes it possible to create a more appropriate fragrance by analyzing the user's social media activity. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's social media activity into AI and have the AI ​​suggest a means for creating the fragrance.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can analyze the user's past purchase history and survey results and select an appropriate data collection method. For example, it can collect related data based on products the user has purchased in the past. It can also collect data on areas of interest based on survey results the user has answered in the past. It can also collect data related to specific seasons from the user's purchase history. This makes it possible to select the optimal data collection method by analyzing the user's past purchase history and survey results.

[0052] The collection unit can filter data based on the user's current living situation and areas of interest. For example, if the user is currently raising a child, data related to childcare can be preferentially collected. If the user is interested in health, health-related data can be preferentially collected. Furthermore, if the user is traveling, travel-related data can be preferentially collected. Thus, by filtering data based on the user's current living situation and areas of interest, more relevant data can be collected.

[0053] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data of high importance. A simple analysis can be performed on data of low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0054] The generation unit can adjust the level of detail of the recipe based on the importance of the fragrance elements during generation. For example, a detailed recipe can be generated for a fragrance element with high importance. A concise recipe can be generated for a fragrance element with low importance. Furthermore, a recipe with a moderate level of detail can be generated for a fragrance element with medium importance. In this way, by adjusting the level of detail of the recipe based on the importance of the fragrance elements, a more effective fragrance recipe can be generated.

[0055] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a purchasing pattern analysis algorithm can be applied to purchase history data. A text analysis algorithm can also be applied to survey result data. Furthermore, a sentiment analysis algorithm can be applied to SNS reaction data. In this way, applying different analysis algorithms depending on the data category enables more accurate analysis.

[0056] During generation, the generation unit can apply different generation algorithms depending on the fragrance category. For example, a floral generation algorithm can be applied to a floral fragrance. A citrus generation algorithm can be applied to a citrus fragrance. Furthermore, a woody generation algorithm can be applied to a woody fragrance. In this way, by applying different generation algorithms depending on the fragrance category, it is possible to generate fragrance recipes with higher accuracy.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects data. Data includes text data, numerical data, image data, etc. The collection unit can collect data such as customer purchasing history, survey results, and reactions on social media. For example, the collection unit collects customer purchasing history and obtains information such as purchase date and time, purchased products, and purchase frequency. The collection unit can also collect survey results and obtain information such as question items, response format, and aggregation method. Furthermore, the collection unit can collect reactions on social media and obtain information such as post content, number of likes, and number of comments. Step 2: The analysis unit uses AI to analyze the collected data. Analysis includes statistical analysis and the use of machine learning algorithms. For example, the analysis unit performs statistical analysis on the collected data to identify customer purchasing patterns. The analysis unit can also use machine learning algorithms to identify the scent elements that customers prefer. Furthermore, the analysis unit can use natural language processing technology to analyze reactions on social media and infer customer emotions. Step 3: The generation unit uses AI to generate a fragrance recipe based on the identified fragrance elements. This generation process includes deep learning, natural language processing, and other techniques. For example, the generation unit uses deep learning to generate a fragrance recipe. The generation unit can also use natural language processing technology to generate a fragrance recipe. Furthermore, the generation unit can determine the type of fragrance and its blend ratio based on the identified fragrance elements. Step 4: The creation unit actually creates the fragrance based on the generated fragrance recipe. Creation includes the blending method, the equipment to be used, etc. For example, the creation unit blends fragrance ingredients to create the fragrance. The creation unit can also create the fragrance using specific equipment.

[0059] (Example 2) A Solution AI according to an embodiment of the present invention is a system for effectively utilizing scent elements in corporate branding and purchasing behavior. This system collects data related to corporate branding and purchasing behavior, analyzes it using AI to identify optimal scent elements, generates a scent recipe, and provides comprehensive support for the actual scent creation process. For example, a data collection unit collects data such as customer purchase history, survey results, and social media responses, and an analysis unit uses AI to analyze the collected data to identify the scent elements preferred by the customer. A recipe generation unit generates a scent recipe using AI based on the identified scent elements, and a scent creation unit actually creates a scent based on the generated scent recipe. This Solution AI provides comprehensive support for effectively utilizing scent elements in corporate branding and purchasing behavior. This allows companies to increase customer purchasing motivation and improve their brand image. Thus, Solution AI can provide comprehensive support for effectively utilizing scent elements in corporate branding and purchasing behavior.

[0060] A solution AI according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a creation unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, numerical data, and image data. The collection unit can collect data such as customer purchase history, survey results, and reactions on social media. For example, the collection unit collects customer purchase history and acquires information such as purchase date and time, purchased products, and purchase frequency. The collection unit can also collect survey results and acquire information such as question items, response formats, and aggregation methods. The collection unit can also collect reactions on social media and acquire information such as post content, number of likes, and number of comments. The analysis unit uses AI to analyze the collected data. Examples of analysis include, but are not limited to, statistical analysis and the use of machine learning algorithms. For example, the analysis unit statistically analyzes the collected data to identify customer purchasing patterns. The analysis unit can also use machine learning algorithms to identify the elements of fragrances preferred by customers. The analysis unit can also use natural language processing technology to analyze reactions on social media and estimate customer emotions. The generation unit generates a fragrance recipe using AI based on the identified fragrance elements. Examples of generation include, but are not limited to, deep learning and natural language processing. For example, the generation unit generates a fragrance recipe using deep learning. The generation unit can also generate a fragrance recipe using natural language processing technology. Furthermore, the generation unit can determine the type and blend ratio of fragrance ingredients based on the identified fragrance elements. The creation unit actually creates the fragrance based on the generated fragrance recipe. Examples of creation include, but are not limited to, a blending method and equipment to be used. For example, the creation unit blends fragrance ingredients to create the fragrance. The creation unit can also create the fragrance using specific equipment. This allows the solution AI according to the embodiment to provide comprehensive support for effectively utilizing fragrance elements in corporate branding and purchasing behavior.

[0061] The collection unit can collect data including customer purchase history, survey results, and reactions on social media. The collection unit, for example, collects customer purchase history. For example, the collection unit acquires information such as purchase date and time, purchased products, and purchase frequency. The collection unit can also collect survey results. For example, the collection unit acquires information such as question items, answer format, and aggregation method. The collection unit can also collect reactions on social media. For example, the collection unit acquires information such as post content, number of likes, and number of comments. By collecting data such as customer purchase history, survey results, and reactions on social media, more accurate analysis is possible. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input customer purchase history into AI and have the AI ​​identify purchasing patterns.

[0062] The analysis unit can use AI to analyze the collected data and identify the fragrance elements preferred by customers. The analysis unit, for example, performs statistical analysis on the collected data. For example, the analysis unit can identify customer purchasing patterns. The analysis unit can also use machine learning algorithms to identify the fragrance elements preferred by customers. For example, the analysis unit can use deep learning to identify the fragrance elements preferred by customers. The analysis unit can also use natural language processing technology to analyze reactions on social media and estimate customer emotions. For example, the analysis unit can analyze the content of posts on social media and estimate customer emotions. By doing so, the analysis unit can identify the fragrance elements preferred by customers, thereby generating more effective fragrance recipes. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​identify the fragrance elements preferred by customers.

[0063] The generation unit allows the AI ​​to generate a fragrance recipe based on the identified fragrance elements. The generation unit generates the fragrance recipe using, for example, deep learning. For example, the generation unit determines the type and blend ratio of fragrances based on the identified fragrance elements. The generation unit can also generate the fragrance recipe using natural language processing technology. For example, the generation unit generates the fragrance recipe based on the identified fragrance elements. In this way, the AI ​​generates the fragrance recipe, allowing the fragrance recipe to be created efficiently. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the identified fragrance elements into the AI ​​and have the AI ​​generate the fragrance recipe.

[0064] The creation unit can actually create a fragrance based on the generated fragrance recipe. The creation unit, for example, mixes fragrances to create the fragrance. For example, the creation unit creates the fragrance using specific equipment. The creation unit can also create a fragrance based on the generated fragrance recipe. For example, the creation unit creates a fragrance based on the type and blending ratio of fragrances. In this way, the fragrance can be actually provided by creating the fragrance based on the generated fragrance recipe. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the generated fragrance recipe into AI and have the AI ​​create the fragrance.

[0065] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the user's emotions. For example, when the user is relaxed, the collection unit immediately collects data. For example, the collection unit collects purchase history and survey results when the user is relaxed. The collection unit can also postpone data collection when the user is feeling stressed. For example, the collection unit postpones data collection when the user is feeling stressed. Furthermore, the collection unit can complete data collection in a short time when the user is busy. For example, the collection unit collects data in a short time when the user is busy. This allows data to be collected at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotional data into the AI ​​and have the AI ​​adjust the timing of data collection.

[0066] The collection unit can analyze the user's past purchase history and survey results and select an appropriate data collection method. The collection unit, for example, collects related data based on products the user has purchased in the past. For example, the collection unit collects data related to products the user has purchased in the past. The collection unit can also collect data on areas of interest based on survey results the user has answered in the past. For example, the collection unit collects data on areas of interest based on survey results the user has answered in the past. The collection unit can also collect data related to a specific season from the user's purchase history. For example, the collection unit collects data related to a specific season based on the user's purchase history. This makes it possible to select an optimal data collection method by analyzing the user's past purchase history and survey results. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's past purchase history and survey results into AI and have the AI ​​select an optimal data collection method.

[0067] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, if the user is currently raising a child, the collection unit prioritizes collecting data related to childcare. For example, the collection unit collects data related to childcare and provides information about childcare. Furthermore, if the user is interested in health, the collection unit can prioritize collecting data related to health. For example, the collection unit collects data related to health and provides information about health. Furthermore, if the user is traveling, the collection unit can prioritize collecting data related to travel. For example, the collection unit collects data related to travel and provides information about travel. In this way, by filtering data based on the user's current living situation and areas of interest, more relevant data can be collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current living situation and areas of interest into AI and have the AI ​​perform data filtering.

[0068] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the user's emotions. For example, when the user is excited, the collection unit prioritizes collecting entertainment-related data. For example, when the user is excited, the collection unit collects entertainment-related data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting relaxation-related data. For example, when the user is relaxed, the collection unit collects relaxation-related data. Furthermore, when the user is stressed, the collection unit can prioritize collecting stress-related data. For example, when the user is stressed, the collection unit collects stress-related data. This enables more effective data collection by determining the priority of data to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input user emotional data into the AI ​​and have the AI ​​determine the priority of the data.

[0069] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is in a specific area, the collection unit collects data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. For example, when the user is traveling, the collection unit collects data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the area around the user's home. For example, when the user is at home, the collection unit collects data related to the area around the user's home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to AI and cause the AI ​​to collect highly relevant data.

[0070] During data collection, the collection unit can analyze the user's social media activity and collect related data. The collection unit, for example, collects related data based on content shared by the user on social media. For example, the collection unit collects data related to content shared by the user on social media. The collection unit can also collect related data based on accounts the user follows on social media. For example, the collection unit collects data related to accounts the user follows on social media. The collection unit can also collect related data based on groups the user joins on social media. For example, the collection unit collects data related to groups the user joins on social media. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activity into AI and cause the AI ​​to collect related data.

[0071] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the user's emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. Furthermore, the analysis unit can also provide concise analysis results when the user is in a hurry. For example, the analysis unit provides concise analysis results when the user is in a hurry. Furthermore, the analysis unit can also provide visually appealing analysis results when the user is excited. For example, the analysis unit provides visually appealing analysis results when the user is excited. This allows for adjusting the way the analysis is presented based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the way the analysis is expressed.

[0072] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a concise analysis on data of low importance. For example, the analysis unit performs a concise analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0073] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a purchase pattern analysis algorithm to purchase history data. For example, the analysis unit applies a purchase pattern analysis algorithm to purchase history data. The analysis unit can also apply a text analysis algorithm to survey result data. For example, the analysis unit applies a text analysis algorithm to survey result data. The analysis unit can also apply a sentiment analysis algorithm to SNS reaction data. For example, the analysis unit applies a sentiment analysis algorithm to SNS reaction data. This enables more accurate analysis by applying different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI and have the AI ​​apply different analysis algorithms.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the user's emotions. For example, the analysis unit provides a short analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit provides a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a visually appealing analysis result when the user is excited. For example, the analysis unit provides a visually appealing analysis result when the user is excited. This allows for adjusting the length of the analysis based on the user's emotions to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0075] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the most recent data to grasp the latest trends. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit analyzes the most recent data while referring to past data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. For example, the analysis unit prioritizes analyzing data collected during a specific campaign period. This enables efficient analysis by determining the priority of analysis based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI and have the AI ​​determine the priority of analysis.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit prioritizes analysis of data with high relevance to extract important insights. The analysis unit can also analyze data with medium relevance next. For example, the analysis unit analyzes data with medium relevance next to provide supplementary information. The analysis unit can also analyze data with low relevance last. For example, the analysis unit analyzes data with low relevance last to achieve overall balance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0077] The generation unit can estimate the user's emotions and adjust the expression method of the generated fragrance recipe based on the user's emotions. For example, when the user is relaxed, the generation unit generates a calming fragrance recipe. For example, when the user is relaxed, the generation unit generates a calming fragrance recipe. Furthermore, when the user is excited, the generation unit can generate a stimulating fragrance recipe. For example, when the user is excited, the generation unit generates a stimulating fragrance recipe. Furthermore, when the user is stressed, the generation unit can generate a fragrance recipe with a relaxation effect. For example, when the user is stressed, the generation unit generates a fragrance recipe with a relaxation effect. This allows for adjusting the expression method of the fragrance recipe based on the user's emotions, thereby providing a more appropriate fragrance recipe. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the way the fragrance recipe is expressed.

[0078] The generation unit can adjust the level of detail of the recipe based on the importance of the fragrance elements during generation. For example, the generation unit generates a detailed recipe for a fragrance element with high importance. For example, the generation unit generates a detailed recipe for a fragrance element with high importance. The generation unit can also generate a concise recipe for a fragrance element with low importance. For example, the generation unit generates a concise recipe for a fragrance element with low importance. Furthermore, the generation unit can also generate a recipe with a moderate level of detail for a fragrance element with medium importance. For example, the generation unit generates a recipe with a moderate level of detail for a fragrance element with medium importance. In this way, by adjusting the level of detail of the recipe based on the importance of the fragrance elements, a more effective fragrance recipe can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the fragrance elements to AI and cause the AI ​​to adjust the level of detail of the recipe.

[0079] During generation, the generation unit can apply different generation algorithms depending on the fragrance category. For example, the generation unit applies a floral generation algorithm to a floral fragrance. For example, the generation unit applies a floral generation algorithm to a floral fragrance. The generation unit can also apply a citrus generation algorithm to a citrus fragrance. For example, the generation unit applies a citrus generation algorithm to a citrus fragrance. The generation unit can also apply a woody generation algorithm to a woody fragrance. For example, the generation unit applies a woody generation algorithm to a woody fragrance. In this way, by applying different generation algorithms depending on the fragrance category, it is possible to generate a fragrance recipe with higher accuracy. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the fragrance category to AI and cause the AI ​​to apply different generation algorithms.

[0080] The generation unit can estimate the user's emotions and adjust the length of the fragrance recipe to be generated based on the user's emotions. For example, when the user is in a hurry, the generation unit generates a short and to-the-point fragrance recipe. For example, when the user is in a hurry, the generation unit generates a short and to-the-point fragrance recipe. The generation unit can also generate a longer fragrance recipe with detailed descriptions when the user is relaxed. For example, when the user is relaxed, the generation unit generates a longer fragrance recipe with detailed descriptions. Furthermore, when the user is excited, the generation unit can also generate a fragrance recipe with visually stimulating effects. For example, when the user is excited, the generation unit generates a fragrance recipe with visually stimulating effects. This allows the user to adjust the length of the fragrance recipe based on the user's emotions and provide a more appropriate fragrance recipe. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data into AI and have the AI ​​adjust the length of the fragrance recipe.

[0081] At the time of generation, the generation unit can determine the priority of recipes based on the collection time of fragrance elements. The generation unit, for example, prioritizes incorporating the most recent fragrance elements into the recipe. For example, the generation unit prioritizes incorporating the most recent fragrance elements into the recipe. The generation unit can also emphasize the most recent elements while referring to past fragrance elements. For example, the generation unit incorporates the most recent elements into the recipe while referring to past fragrance elements. Furthermore, the generation unit can also prioritize incorporating fragrance elements collected during a specific period into the recipe. For example, the generation unit prioritizes incorporating fragrance elements collected during a specific period into the recipe. In this way, by determining the priority of recipes based on the collection time of fragrance elements, more effective fragrance recipes can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the collection time of fragrance elements into AI and have the AI ​​determine the priority of recipes.

[0082] The generation unit can adjust the order of the recipe based on the relevance of the scent elements during generation. For example, the generation unit prioritizes incorporating scent elements with high relevance into the recipe. For example, the generation unit prioritizes incorporating scent elements with high relevance into the recipe. The generation unit can also incorporate scent elements with medium relevance into the recipe next. For example, the generation unit can incorporate scent elements with medium relevance into the recipe next. The generation unit can also incorporate scent elements with low relevance into the recipe last. For example, the generation unit can incorporate scent elements with low relevance into the recipe last. In this way, by adjusting the order of the recipe based on the relevance of the scent elements, a more effective scent recipe can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the scent elements into AI and have the AI ​​adjust the order of the recipe.

[0083] The creation unit can estimate the user's emotions and adjust the fragrance creation method based on the user's emotions. For example, when the user is relaxed, the creation unit creates a calming fragrance. For example, when the user is relaxed, the creation unit creates a calming fragrance. Furthermore, when the user is excited, the creation unit can create a stimulating fragrance. For example, when the user is excited, the creation unit creates a stimulating fragrance. Furthermore, when the user is stressed, the creation unit can create a fragrance with a relaxing effect. For example, when the user is stressed, the creation unit creates a fragrance with a relaxing effect. This allows the creation of a more appropriate fragrance by adjusting the fragrance creation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the creation unit may be performed using an AI, or may be performed without an AI. For example, the creation unit can input the user's emotion data into an AI and have the AI ​​adjust the fragrance creation method.

[0084] When creating a fragrance, the creation unit can analyze the user's past fragrance preferences and select the optimal creation method. The creation unit, for example, creates an optimal fragrance based on fragrance elements that the user previously liked. For example, the creation unit creates an optimal fragrance based on fragrance elements that the user previously liked. The creation unit can also create a fragrance by eliminating fragrance elements that the user previously avoided. For example, the creation unit creates a fragrance by eliminating fragrance elements that the user previously avoided. Furthermore, the creation unit can analyze the user's past fragrance preferences and create a fragrance that matches the season or event. For example, the creation unit analyzes the user's past fragrance preferences and creates a fragrance that matches the season or event. In this way, by analyzing the user's past fragrance preferences, a more appropriate fragrance can be created. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's past fragrance preferences into AI and have the AI ​​select the optimal creation method.

[0085] The creation unit can customize the fragrance creation means based on the user's current living situation at the time of creation. For example, if the user is currently raising a child, the creation unit creates a fragrance suitable for childcare. For example, the creation unit creates a fragrance suitable for childcare when the user is currently raising a child. Furthermore, if the user is interested in health, the creation unit can also create a fragrance that is good for health. For example, the creation unit creates a fragrance that is good for health when the user is interested in health. Furthermore, if the user is traveling, the creation unit can also create a fragrance that is suitable for travel. For example, the creation unit creates a fragrance that is suitable for travel when the user is traveling. In this way, by customizing the fragrance creation means based on the user's current living situation, a more appropriate fragrance can be created. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's current living situation into AI and have the AI ​​customize the fragrance creation means.

[0086] The creation unit can estimate the user's emotions and determine the priority of scent creation based on the user's emotions. For example, when the user is excited, the creation unit prioritizes creating a stimulating scent. For example, when the user is excited, the creation unit prioritizes creating a stimulating scent. The creation unit can also prioritize creating a calming scent when the user is relaxed. For example, when the user is relaxed, the creation unit prioritizes creating a calming scent. Furthermore, when the user is stressed, the creation unit can prioritize creating a scent with a relaxing effect. For example, when the user is stressed, the creation unit prioritizes creating a scent with a relaxing effect. This allows for determining the priority of scent creation based on the user's emotions, thereby creating a more appropriate scent. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or without AI. For example, the creation unit can input the user's emotional data into the AI ​​and have the AI ​​determine the priority of fragrance creation.

[0087] When creating a fragrance, the creation unit can select an appropriate fragrance creation method by taking into account the user's geographical location information. For example, when the user is in a specific area, the creation unit creates a fragrance appropriate for that area. For example, when the user is in a specific area, the creation unit creates a fragrance appropriate for that area. Furthermore, when the user is traveling, the creation unit can also create a fragrance appropriate for the travel destination. For example, when the user is traveling, the creation unit creates a fragrance appropriate for the travel destination. Furthermore, when the user is at home, the creation unit can also create a fragrance appropriate for the area around the user's home. For example, when the user is at home, the creation unit creates a fragrance appropriate for the area around the user's home. In this way, by taking the user's geographical location information into account, a more appropriate fragrance can be created. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's geographical location information into AI and have the AI ​​select an appropriate fragrance creation method.

[0088] During creation, the creation unit can analyze the user's social media activity and suggest a means for creating the fragrance. The creation unit, for example, creates a related fragrance based on content shared by the user on social media. For example, the creation unit creates a fragrance related to content shared by the user on social media. The creation unit can also create a related fragrance based on accounts the user follows on social media. For example, the creation unit creates a fragrance related to accounts the user follows on social media. The creation unit can also create a related fragrance based on groups the user participates in on social media. For example, the creation unit creates a fragrance related to groups the user participates in on social media. This makes it possible to create a more appropriate fragrance by analyzing the user's social media activity. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the user's social media activity into AI and have the AI ​​suggest a means for creating the fragrance. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and creation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect data such as customer purchase history, survey results, and reactions on social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI generates a fragrance recipe based on the identified fragrance elements. The creation unit is realized, for example, by the control unit 46A of the smart device 14, and actually creates the fragrance based on the generated fragrance recipe. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and creation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect data such as customer purchase history, survey results, and reactions on social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI generates a fragrance recipe based on the identified fragrance elements. The creation unit is realized, for example, by the control unit 46A of the smart glasses 214, and actually creates the fragrance based on the generated fragrance recipe. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and creation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset-type terminal 314 to collect data such as customer purchase history, survey results, and reactions on social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI generates a fragrance recipe based on the identified fragrance elements. The creation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and actually creates the fragrance based on the generated fragrance recipe. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and creation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect data such as customer purchase history, survey results, and reactions on social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI generates a fragrance recipe based on the identified fragrance elements. The creation unit is realized, for example, by the control unit 46A of the robot 414, and actually creates the fragrance based on the generated fragrance recipe.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. Furthermore, if the user is excited, visually appealing analysis results can be provided. This makes it possible to present analysis results according to the user's emotions, thereby improving user satisfaction.

[0091] The collection unit can analyze the user's past purchase history and survey results and select an appropriate data collection method. For example, it can collect related data based on products the user has purchased in the past. It can also collect data on areas of interest based on survey results the user has answered in the past. It can also collect data related to specific seasons from the user's purchase history. This makes it possible to select the optimal data collection method by analyzing the user's past purchase history and survey results.

[0092] The generation unit can estimate the user's emotions and adjust the expression method of the generated fragrance recipe based on the estimated user emotions. For example, if the user is relaxed, a calming fragrance recipe can be generated. If the user is excited, a stimulating fragrance recipe can be generated. Furthermore, if the user is stressed, a fragrance recipe with a relaxation effect can be generated. In this way, by adjusting the expression method of the fragrance recipe based on the user's emotions, a more appropriate fragrance recipe can be provided.

[0093] The creation unit can estimate the user's emotions and adjust the fragrance creation method based on the estimated user emotions. For example, if the user is relaxed, a calming fragrance can be created. If the user is excited, a stimulating fragrance can be created. Furthermore, if the user is stressed, a fragrance with a relaxing effect can be created. In this way, by adjusting the fragrance creation method based on the user's emotions, a more appropriate fragrance can be created.

[0094] The collection unit can filter data based on the user's current living situation and areas of interest. For example, if the user is currently raising a child, data related to childcare can be preferentially collected. If the user is interested in health, health-related data can be preferentially collected. Furthermore, if the user is traveling, travel-related data can be preferentially collected. Thus, by filtering data based on the user's current living situation and areas of interest, more relevant data can be collected.

[0095] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data of high importance. A simple analysis can be performed on data of low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0096] The generation unit can adjust the level of detail of the recipe based on the importance of the fragrance elements during generation. For example, a detailed recipe can be generated for a fragrance element with high importance. A concise recipe can be generated for a fragrance element with low importance. Furthermore, a recipe with a moderate level of detail can be generated for a fragrance element with medium importance. In this way, by adjusting the level of detail of the recipe based on the importance of the fragrance elements, a more effective fragrance recipe can be generated.

[0097] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is excited, data related to entertainment can be collected with priority. If the user is relaxed, data related to relaxation can be collected with priority. Furthermore, if the user is stressed, data related to stress reduction can be collected with priority. Thus, by determining the priority of data to be collected based on the user's emotions, more effective data collection is possible.

[0098] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a purchasing pattern analysis algorithm can be applied to purchase history data. A text analysis algorithm can also be applied to survey result data. Furthermore, a sentiment analysis algorithm can be applied to SNS reaction data. In this way, applying different analysis algorithms depending on the data category enables more accurate analysis.

[0099] During generation, the generation unit can apply different generation algorithms depending on the fragrance category. For example, a floral generation algorithm can be applied to a floral fragrance. A citrus generation algorithm can be applied to a citrus fragrance. Furthermore, a woody generation algorithm can be applied to a woody fragrance. In this way, by applying different generation algorithms depending on the fragrance category, it is possible to generate fragrance recipes with higher accuracy.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects data. Data includes text data, numerical data, image data, etc. The collection unit can collect data such as customer purchasing history, survey results, and reactions on social media. For example, the collection unit collects customer purchasing history and obtains information such as purchase date and time, purchased products, and purchase frequency. The collection unit can also collect survey results and obtain information such as question items, response format, and aggregation method. Furthermore, the collection unit can collect reactions on social media and obtain information such as post content, number of likes, and number of comments. Step 2: The analysis unit uses AI to analyze the collected data. Analysis includes statistical analysis and the use of machine learning algorithms. For example, the analysis unit performs statistical analysis on the collected data to identify customer purchasing patterns. The analysis unit can also use machine learning algorithms to identify the scent elements that customers prefer. Furthermore, the analysis unit can use natural language processing technology to analyze reactions on social media and infer customer emotions. Step 3: The generation unit uses AI to generate a fragrance recipe based on the identified fragrance elements. This generation process includes deep learning, natural language processing, and other techniques. For example, the generation unit uses deep learning to generate a fragrance recipe. The generation unit can also use natural language processing technology to generate a fragrance recipe. Furthermore, the generation unit can determine the type of fragrance and its blend ratio based on the identified fragrance elements. Step 4: The creation unit actually creates the fragrance based on the generated fragrance recipe. Creation includes the blending method, the equipment to be used, etc. For example, the creation unit blends fragrance ingredients to create the fragrance. The creation unit can also create the fragrance using specific equipment.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a fragrance recipe based on the analysis results obtained by the analysis unit; a creation unit that creates a fragrance based on the fragrance recipe created by the creation unit; Equipped with A system characterized by:

2. The collecting unit Collect data including customer purchase history, survey results, and social media responses 2. The system of claim 1.

3. The analysis unit AI analyzes the collected data to identify the scent elements that customers prefer.

2. The system of claim 1.

4. The generation unit AI generates a fragrance recipe based on the identified fragrance elements.

2. The system of claim 1.

5. The creation unit Create a fragrance based on the generated fragrance recipe 2. The system of claim 1.

6. The collecting unit Infer user emotions and adjust data collection timing based on user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past purchase history and survey results to select the appropriate data collection method 2. The system of claim 1.

8. The collecting unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

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