System

Abstract

To provide a system.SOLUTION: A system includes: means for analyzing existing scent and acquiring component data on the existing scent; means for causing a generative AI to learn a generation pattern of the existing scent on the basis of the acquired component data; feeling engine means for analyzing a feeling state of a user; means for generating a prompt sentence for instructing the learned generative AI to generate the generation pattern of the scent provided on the basis of the analyzed feeling state; means for controlling the generation pattern of the provided scent using the learned generative AI on the basis of the prompt sentence; and scent generation means for mixing a compound to generate the provided scent on the basis of the generated generation pattern of the provided scent.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, AI has succeeded in generating sounds that are comparable to those created by humans when it comes to the five human senses of sight (text, pictures) and hearing (sound). On the other hand, the areas of taste, touch, and smell are still in the early stages of development, and AI has not yet been able to intervene. In particular, it is difficult to distinguish scents in detail and generate them without being bound by time and space. [Means for solving the problem]

[0005] The present invention provides a sensor means for performing detailed identification of scents, a means for inputting scent data identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, and a scent generation means for generating a scent based on the generation pattern. This makes it possible to generate scents beyond the constraints of time and space. Furthermore, the generation AI can adjust the scent generation pattern according to the user's preferences and circumstances. [Brief explanation of the drawings]

[0006] [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. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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 (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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), Bluetooth (registered trademark), etc.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0018] 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.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention provides an IoT device equipped with a sensor means for performing detailed scent identification, a generation AI, and a scent generation means. The sensor means analyzes compounds in the air using, for example, gas chromatography to identify the scent components in detail. The identified scent data is input to the generation AI. The generation AI uses machine learning techniques such as deep learning to learn and generate a scent generation pattern from the scent data. The scent generation means mixes the necessary compounds based on the generation pattern output from the generation AI to generate a scent.

[0029] "Example 2"

[0030] Furthermore, the generation AI generates scent patterns that take into account time and space constraints. For example, if the user uses it in the morning, it will generate a refreshing forest scent. If the user uses it during relaxation time, it will generate a relaxing lavender scent. This makes it possible to provide scents that suit the user's living environment and time of day. "Example 3"

[0031] Furthermore, the generation AI adjusts the scent generation pattern according to the user's preferences and the situation. For example, if there is information that the user likes the scent of coffee, the generation AI will prioritize the coffee scent generation pattern. Also, if there is information that the user has a cold, the generation AI will generate a eucalyptus scent generation pattern, which is said to relieve cold symptoms. This makes it possible to provide a scent optimized for the user's preferences and situation.

[0032] The processing flow of each embodiment will be described below.

[0033] "Example 1"

[0034] Step 1: The sensor analyzes the compounds in the air and identifies the scent components in detail. This identification is performed using, for example, gas chromatography.

[0035] Step 2: The identified scent data is input into the generative AI.

[0036] Step 3: The generation AI uses machine learning techniques such as deep learning to learn and generate scent generation patterns from scent data.

[0037] Step 4: The fragrance generation means mixes the necessary compounds to generate the fragrance based on the generation pattern output from the generation AI.

[0038] "Example 2"

[0039] Step 1: The generative AI generates a scent generation pattern that takes into account time and space constraints.

[0040] Step 2: For example, if the user uses it in the morning, the generation AI will generate a pattern for generating a refreshing forest scent.

[0041] Step 3: If the user uses it during relaxation time, the generation AI will generate a pattern of lavender scent that has a relaxing effect.

[0042] "Example 3"

[0043] Step 1: The AI ​​adjusts the aroma generation pattern based on the user's preferences and the situation. Step 2: For example, if the user has information that they like the aroma of coffee, the AI ​​will prioritize the coffee aroma generation pattern.

[0044] Step 3: If the user has a cold, the AI ​​generates a pattern for the scent of eucalyptus, which is said to relieve cold symptoms.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0047] Conventional scent generation systems have difficulty identifying scent components in detail and generating scents that suit the user's preferences and circumstances. It is also difficult to generate scents that take into account time and space constraints, making it impossible to meet the diverse needs of users.

[0048] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0049] In this invention, the server includes a sensor means for analyzing compounds in the air and identifying the fragrance components in detail, a means for inputting the identified fragrance component data into a generative AI model, which then learns a fragrance generation pattern based on the data, and a fragrance generation means for mixing the necessary compounds based on the generation pattern to generate a fragrance. This makes it possible to identify the fragrance components in detail and generate a fragrance that suits the user's preferences and situation. It is also possible to generate fragrances that take into account time and spatial constraints.

[0050] The "sensor means" is a device that analyzes compounds in the air and identifies the scent components in detail.

[0051] A "generative AI model" is an artificial intelligence model that uses machine learning technology to learn fragrance generation patterns from fragrance component data.

[0052] The "fragrance generation means" is a device that generates a fragrance by mixing the necessary compounds based on the generation pattern obtained from the generative AI model.

[0053] "Fragrance component data" refers to data relating to the types and concentrations of compounds that make up the fragrance identified by the sensor means.

[0054] A "generative pattern" is information about the combination and proportion of compounds required to produce a specific fragrance, learned by the generative AI model based on fragrance component data.

[0055] "Temporal and spatial constraints" refer to the temporal and spatial conditions and limitations that affect the generation and diffusion of scent.

[0056] "User preferences and circumstances" refers to individual requirements and conditions such as the type and strength of the scent desired by the user, and the situation in which it will be used.

[0057] MODE FOR CARRYING OUT THE INVENTION

[0058] The present invention is a system for detailed identification and generation of scents, which includes a sensor means, a generation AI model, and a scent generation means. Specific embodiments of this system will be described below.

[0059] Sensor Means

[0060] The server activates the sensor and analyzes the compounds in the air using a gas chromatography device. The gas chromatography device detects trace amounts of compounds in the air and identifies the concentration and type of each compound. The analysis results are sent to the server as fragrance component data.

[0061] Generative AI Models

[0062] The server inputs the identified scent component data into a generative AI model. The generative AI model is built using a machine learning framework such as TENSORFLOW (registered trademark) or PyTorch. The generative AI model learns a scent generation pattern based on the scent component data using a deep learning algorithm. The learned generation pattern is sent to the scent generation means.

[0063] Fragrance generating means

[0064] The server controls the scent generator based on the generation pattern obtained from the generative AI model. The scent generator mixes the necessary compounds in the correct proportions to create the specified scent. This process uses specialized hardware to control chemical reactions. The generated scent is then provided to the user.

[0065] Specific examples

[0066] For example, suppose a user wants to create a "rose scent." The server uses sensor means to identify in detail the compounds that make up the rose scent and inputs that data into the generative AI model. The generative AI model learns the pattern for generating rose scent based on past data and sends that pattern to the scent generation means. The server controls the scent generation means and mixes the necessary compounds to generate the rose scent. Finally, the server provides the generated rose scent to the user.

[0067] Prompt Sentence Examples

[0068] "Please explain the process by which the generative AI learns the patterns of scent generation based on the detailed compounds that produce the rose scent."

[0069] In this way, a system is realized in which the sensor means, the generative AI model, and the scent generation means work together to perform detailed scent identification and generation.

[0070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0071] Step 1:

[0072] The server activates the sensor means and analyzes the compounds in the air using a gas chromatography device. It receives the air sample as input and generates scent component data as output. Specifically, the gas chromatography device separates the compounds in the air and identifies the concentration and type of each compound. This data is sent to the server as scent component data.

[0073] Step 2:

[0074] The server inputs the identified scent component data into the generative AI model. It receives the scent component data as input and obtains a generated pattern as output. Specifically, the generative AI model uses machine learning frameworks such as TensorFlow and PyTorch to preprocess the scent component data and learns a scent generation pattern using a deep learning algorithm. The learned generated pattern is stored on the server.

[0075] Step 3:

[0076] The server controls the scent generator based on the generation pattern obtained from the generative AI model. It receives the generation pattern as input and generates the scent as output. Specifically, the scent generator mixes the necessary compounds in the correct proportions to create the specified scent. This process uses dedicated hardware to control the chemical reactions.

[0077] Step 4:

[0078] The server provides the generated scent to the user. It receives the generated scent as input and provides the scent to the user as output. Specifically, it provides the scent generated by the scent generation means through an interface that allows the user to check the scent. The user can check the operation of the system by smelling the generated scent.

[0079] In this way, a system is realized in which the sensor means, the generative AI model, and the scent generation means work together to perform detailed scent identification and generation.

[0080] (Application example 1)

[0081] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] Conventional scent generation systems are effective in identifying scents in detail and learning scent generation patterns, but they have limitations in generating scents that meet customer preferences and providing scents effectively within physical stores. Furthermore, it is difficult to generate scents that meet customer preferences and situations, making it difficult to contribute to improving the store atmosphere and customer satisfaction. This has led to a demand for new technologies to improve the quality of the customer experience.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0084] In this invention, the server includes a sensor means for performing detailed identification of scents, a means for inputting data on the scent identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, an input means for generating a scent according to customer preferences, and a control means for generating a scent based on the data from the input means. This makes it possible to generate scents according to customer preferences, thereby improving the quality of the customer experience in a physical store.

[0085] "Sensor means" refers to a device that analyzes compounds in the air to provide detailed scent identification.

[0086] "Generative AI" is an artificial intelligence that learns and generates scent generation patterns based on identified scent data.

[0087] The "fragrance generation means" is a device that generates a fragrance by mixing the necessary compounds based on the generation pattern output from the generation AI.

[0088] The "input means" is a device or interface for inputting data for generating a fragrance according to the customer's preferences.

[0089] The "control means" is a device or system for controlling the fragrance generating means based on data from the input means and generating a fragrance.

[0090] As an embodiment of the present invention, a smart scent display system installed in a brick-and-mortar store will be described as an example. This system includes a sensor means for detailed scent identification, a generation AI, a scent generation means, an input means for generating scents according to customer preferences, and a control means.

[0091] System configuration

[0092] 1. Sensor means:

[0093] A gas chromatography sensor is used to analyze compounds in the air and identify the fragrance components in detail.

[0094] The sensor data includes detailed information about the components of the scent.

[0095] 2. Generation AI:

[0096] Using deep learning technology, scent generation patterns are learned and generated based on scent data acquired from sensor means.

[0097] The software used is Keras (deep learning library) and Scikit-learn (data preprocessing).

[0098] 3. Scent generating means:

[0099] This device mixes the necessary compounds to generate fragrance based on the generation pattern output by the generation AI.

[0100] The scent generator precisely mixes compounds according to a generation pattern.

[0101] 4. Input method:

[0102] It is an interface that allows customers to input their preferred scents via smartphones or smart glasses.

[0103] The data entered reflects the customer's preferences and circumstances.

[0104] 5. Control measures:

[0105] This is a system for controlling a fragrance generating means based on data from an input means and generating a fragrance.

[0106] The control means operates the fragrance generation means according to instructions from the generation AI.

[0107] System Operation

[0108] 1. Acquiring sensor data:

[0109] The sensor means analyzes compounds in the air and identifies scent components.

[0110] The acquired sensor data is sent to the generation AI.

[0111] 2. Data preprocessing:

[0112] The generative AI standardizes the sensor data using Scikit-learn's StandardScaler.

[0113] The standardized data is then fed into a generative AI model.

[0114] 3. Training the generative AI model:

[0115] The generative AI defines a deep learning model using Keras and learns scent generation patterns based on sensor data.

[0116] The trained model is used to predict scent production patterns.

[0117] 4. Aroma generation:

[0118] Based on the generation pattern output by the generation AI, the fragrance generation means mixes the necessary compounds to generate the fragrance.

[0119] The control means operates the fragrance generating means based on data from the input means.

[0120] Specific examples

[0121] Physical store: In an apparel shop, customers can select a "relaxing scent" using their smartphone, and that scent will spread throughout the store.

[0122] Cafe: Customers select a "fresh scent" through smart glasses, and the scent spreads throughout the store.

[0123] Prompt Sentence Examples

[0124] When a customer uses their smartphone to select a "relaxing scent," that scent will fill the store.

[0125] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0126] Step 1:

[0127] The sensor means analyzes compounds in the air and identifies scent components.

[0128] Input: Compounds in the air

[0129] Data processing: Compounds are analyzed using a gas chromatography sensor to obtain data on fragrance components.

[0130] Output: Fragrance component data

[0131] Step 2:

[0132] The server transmits the scent component data acquired from the sensor means to the generation AI.

[0133] Input: Fragrance ingredient data

[0134] Data processing: Converting data into an appropriate format for input into the generative AI.

[0135] Output: Fragrance component data input into the generation AI

[0136] Step 3:

[0137] The generation AI learns and generates fragrance generation patterns based on fragrance component data.

[0138] Input: Fragrance ingredient data input into the generation AI

[0139] Data computation: Deep learning technology is used to learn and generate scent generation patterns.

[0140] Output: Scent generation pattern

[0141] Step 4:

[0142] The server sends the generated pattern output from the generation AI to the scent generation means.

[0143] Input: scent generation pattern

[0144] Data processing: Convert the generated pattern into a suitable format for input to the scent generation means.

[0145] Output: Generated pattern input to the scent generation means

[0146] Step 5:

[0147] The scent generating means mixes necessary compounds based on the generation pattern to generate the scent.

[0148] Input: A generated pattern input to the scent generation means

[0149] Data calculation: Accurately mix compounds according to the generated patterns.

[0150] Output: Generated scent

[0151] Step 6:

[0152] Users input their preferred scent via their smartphone or smart glasses.

[0153] Input: User's preferred scent data

[0154] Data processing: Converting user input data into an appropriate format for sending to the server.

[0155] Output: User's preferred scent data sent to the server

[0156] Step 7:

[0157] The server controls the fragrance generating means based on the fragrance data of the user's preference.

[0158] Input: User's preferred scent data

[0159] Data calculation: Adjusts the generation pattern from the generation AI based on the user's preferred scent data and controls the scent generation method.

[0160] Output: Scent generated based on the adjusted generation pattern

[0161] Step 8:

[0162] The fragrance generating means generates a fragrance based on the adjusted generation pattern and spreads it throughout the physical store.

[0163] Input: Adjusted generation pattern

[0164] Data calculation: Compounds are mixed according to a coordinated production pattern to generate a scent.

[0165] Output: Generated scent spreads throughout the physical store

[0166] Example 2

[0167] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0168] Conventional scent generation systems have difficulty generating scents that suit the user's living environment and time of day, and also have difficulty adjusting them to suit the user's preferences and circumstances. As a result, it has not been possible to provide the optimal scent desired by the user. Furthermore, there has been a demand for efficient and accurate methods for detailed scent identification and generation of scent generation patterns.

[0169] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0170] In this invention, the server includes a sensor means for performing detailed identification of scents, a means for inputting scent data identified by the sensor means into a generative AI model and for the generative AI model to generate a scent generation pattern based on the data, a means for a user to input information necessary for scent generation into a terminal, a means for the terminal to send the input information to the server, a means for the server to generate a prompt sentence and input the prompt sentence to the generative AI model, a means for the generative AI model to generate a scent pattern, a means for the server to provide the generated scent pattern to the user, and a scent generation means for generating a scent based on the generation pattern. This makes it possible to generate a scent according to the user's living environment and time of day, and also to adjust the scent according to the user's preferences and situation.

[0171] The "sensor means" is a device for performing detailed identification of scents.

[0172] A "generative AI model" is an artificial intelligence algorithm that generates a scent generation pattern based on input data.

[0173] A "terminal" is a device through which a user inputs information necessary for generating a fragrance.

[0174] The "server" is a computer system that receives information sent from the terminal, generates prompt sentences, and inputs them into the generative AI model.

[0175] A "prompt sentence" is an instruction sentence that instructs the generative AI model to generate a scent generation pattern.

[0176] The "fragrance generating means" is a device that actually generates a fragrance based on the generated fragrance pattern.

[0177] A "fragrance pattern" is a combination of ingredients and blending ratios that reproduces a specific fragrance.

[0178] A "user" is an individual or organization that uses the scent generation system.

[0179] This invention is a system that generates fragrances according to the user's living environment and time of day. The system includes a sensor means for detailed identification of fragrances, a generation AI model, a terminal, a server, a prompt sentence, and a fragrance generation means.

[0180] Hardware and software used

[0181] Hardware

[0182] Sensor means: A device for identifying scents in detail. For example, a gas sensor or an electronic nose is included.

[0183] Terminal: A device through which the user inputs the information necessary to generate the scent. This includes smartphones and tablets.

[0184] Server: A computer system that receives information sent from the device, generates prompts, and inputs them into the generative AI model.

[0185] Scent generation means: A device that actually generates a scent based on the generated scent pattern. For example, an aroma diffuser.

[0186] software

[0187] Generative AI model: An artificial intelligence algorithm that generates scent patterns based on input data. Examples include natural language processing models and machine learning algorithms.

[0188] Data processing and calculation

[0189] server

[0190] The server receives the user's input information sent from the device and generates a prompt based on that information. The generated prompt is input into the generative AI model. The generative AI model analyzes the prompt and generates an appropriate scent pattern. The generated scent pattern is provided to the user via the server.

[0191] Terminal

[0192] The terminal provides an interface for the user to input information necessary for generating the fragrance. The information input by the user is transmitted from the terminal to the server.

[0193] User

[0194] The user inputs the information necessary to generate the fragrance through the device. For example, if the user desires a fragrance to use in the morning, they input "fragrance to use in the morning" into the input field on the device. If the user desires a fragrance to use during relaxation time, they input "fragrance to use during relaxation time."

[0195] Specific examples

[0196] 1. If the user wants a scent to use in the morning:

[0197] The user enters "fragrance for morning use" into the terminal.

[0198] The terminal transmits the information to the server.

[0199] The server inputs the prompt statement "Generate a refreshing forest scent to use in the morning" into the generative AI model.

[0200] The generative AI model generates patterns of refreshing forest scents, and the server provides the results to the user.

[0201] 2. If the user wishes to use a fragrance for relaxation:

[0202] The user enters "scent to use during relaxation time" into the terminal.

[0203] The terminal transmits the information to the server.

[0204] The server inputs the prompt sentence "Generate a relaxing lavender scent to use during relaxation time" into the generative AI model.

[0205] The generative AI model generates patterns of relaxing lavender scents, and the server provides the results to the user.

[0206] Prompt Sentence Examples

[0207] 1. "Produce a refreshing forest scent for morning use."

[0208] 2. "Produce a relaxing lavender scent for use during relaxation time."

[0209] This allows users to easily create fragrances that suit their living environment and time of day.

[0210] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0211] Step 1:

[0212] The user enters information into the terminal

[0213] The user inputs the information necessary to generate the fragrance through the device's interface. For example, if the user desires a fragrance to be used in the morning, they input "fragrance to be used in the morning" into the device's input field. The input data includes information such as the type of fragrance and the time of day it will be used. The input information is temporarily stored in the device's internal memory.

[0214] Step 2:

[0215] The device sends the input information to the server

[0216] The device sends the information entered by the user to the server. Specifically, the device converts the user's input data into JSON format data and sends it to the server as an HTTP POST request. This request includes information such as the type of fragrance the user desires and the time of day it will be used. The server analyzes the received data and proceeds to the next processing step.

[0217] Step 3:

[0218] The server generates a prompt

[0219] The server generates a prompt based on the user's input information received from the terminal. For example, if the user requests a "scent to use in the morning," the server generates a prompt such as "Please generate a refreshing forest scent to use in the morning." The generated prompt is stored in the server's internal memory, and the server proceeds to the next processing step.

[0220] Step 4:

[0221] The server inputs a prompt to the generative AI model.

[0222] The server inputs the generated prompt sentence into the generative AI model. Specifically, the server sends a request including the generated prompt sentence to the API endpoint of the generative AI model. The generative AI model analyzes the prompt sentence and starts internal processing to generate an appropriate scent pattern.

[0223] Step 5:

[0224] Generative AI model generates scent patterns

[0225] The generative AI model generates a scent pattern based on a prompt sent from the server. For example, for a prompt such as "A refreshing forest scent for use in the morning," the generative AI model calculates the ingredients and blending ratios required to recreate the refreshing forest scent and returns the results to the server. The generated scent pattern is sent to the server as data including the ingredient list and blending ratios.

[0226] Step 6:

[0227] The server provides the generated scent pattern to the user.

[0228] The server receives the scent pattern returned by the generative AI model and provides it to the user. Specifically, the server sends the scent pattern to the device, which then displays the information to the user. The user can check the displayed scent pattern and input it into the device to generate the scent as needed.

[0229] This allows users to easily create fragrances that suit their living environment and time of day.

[0230] (Application example 2)

[0231] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0232] Conventional aroma generation systems have difficulty generating aromas that suit the user's living environment and time of day, and are unable to provide aromas that match the ordered food, particularly in food delivery services. For this reason, there is a demand for aroma generation technology that can further enrich users' dining experiences.

[0233] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0234] In this invention, the server includes a sensor means for performing detailed identification of aromas, a means for inputting data on the aromas identified by the sensor means to a generation AI, which then generates an aroma generation pattern based on the data, an aroma generation means for generating an aroma based on the generation pattern, and a means for generating an aroma based on user order information. This makes it possible to provide aromas that match the dishes ordered by the user, further enriching the dining experience.

[0235] The "sensor means for detailed identification of scents" is a sensor device for detecting and identifying the components and characteristics of scents in detail.

[0236] "Generative AI" is an artificial intelligence model that generates scent generation patterns based on input data.

[0237] "Means for generating a scent generation pattern" refers to the function by which the generation AI creates a pattern for generating a specific scent based on scent data.

[0238] The "scent generating means" is a device or system that actually generates a scent based on the generated scent pattern.

[0239] The "means for generating aroma based on user order information" is a function that generates an appropriate aroma based on information such as the dish ordered by the user and the time of day.

[0240] A system for implementing this invention includes a sensor means for performing detailed scent identification, a generative AI model, a scent generation means, and a means for generating a scent based on user order information. Specific embodiments of the system are described below.

[0241] System configuration

[0242] 1. Sensor means for detailed scent identification:

[0243] The sensor means is a device for detecting and identifying the components and characteristics of a scent in detail, such as a gas sensor or an electronic nose.

[0244] 2. Generative AI Model:

[0245] The generative AI model is an artificial intelligence model that generates scent patterns based on input scent data. Specifically, it uses a natural language processing model such as GPT-3 (registered trademark).

[0246] 3. Scent generating means:

[0247] The scent generating means is a device or system that actually generates a scent based on the generated scent pattern, such as a scent diffuser or aroma diffuser.

[0248] 4. Means for generating scents based on user order information:

[0249] This method has the function of generating an appropriate scent based on information such as the dish ordered by the user and the time of day. For example, it works in conjunction with a food delivery application.

[0250] Program processing explanation

[0251] The server first acquires scent data from a sensor means for detailed scent identification. This data is input into a generative AI model, which generates a scent generation pattern. The generated pattern is then sent to the scent generation means, which generates the actual scent.

[0252] Furthermore, the system obtains the user's order information (e.g., type of food and time of day), and the generative AI model adjusts the aroma generation pattern based on this information, thereby providing aromas that match the food ordered by the user.

[0253] Specific examples

[0254] For example, if a user orders Italian food for breakfast, the server will input the following prompt into the generative AI model:

[0255] "If the user orders Italian food for breakfast, generate the scent of basil."

[0256] Based on this prompt sentence, the generative AI model creates a pattern for generating the scent of basil and sends it to the scent generation means, which then generates the scent of basil based on this pattern and provides it to the user.

[0257] As described above, the present invention can generate an appropriate aroma according to the user's order information and time of day, thereby enriching the dining experience.

[0258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0259] Step 1:

[0260] The server acquires scent data from a sensor means for detailed scent identification. The sensor means detects the scent components and characteristics and sends the data to the server. The input is scent component data, and the output is the scent data sent to the server.

[0261] Step 2:

[0262] The server inputs the acquired scent data into the generative AI model, which then generates a scent generation pattern based on this data. The input is the scent data, and the output is the generated scent pattern.

[0263] Step 3:

[0264] The server transmits the generated scent pattern to the scent generation means, which generates the actual scent based on this pattern. The input is the scent generation pattern, and the output is the generated scent.

[0265] Step 4:

[0266] A user orders food through a food delivery application. The terminal sends the user's order information (type of food and time slot) to the server. The input is the user's order information, and the output is the order information sent to the server.

[0267] Step 5:

[0268] The server inputs a prompt sentence to the generative AI model based on the user's order information. For example, the prompt sentence "If the user orders Italian food for breakfast, please generate the scent of basil." The input is the prompt sentence, and the output is the generated scent pattern.

[0269] Step 6:

[0270] The server sends the scent pattern obtained from the generative AI model to the scent generation means. The scent generation means generates an appropriate scent based on this pattern and provides it to the user. The input is the scent generation pattern, and the output is the scent provided to the user.

[0271] Example 3

[0272] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0273] Conventional scent generation systems have difficulty generating scents that suit the user's preferences and circumstances, making it impossible to provide scents optimized for each individual user. It is also difficult to generate scents that take into account time and space constraints. This has limited the utility of scent generation systems, making it difficult to increase user satisfaction.

[0274] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0275] In this invention, the server includes means for collecting information on the user's preferences and circumstances, means for generating prompt sentences to be input to the generative AI model based on the collected information, and means for inputting the generated prompt sentences to the generative AI model and calculating a scent generation pattern, thereby enabling the generation of scents according to the user's preferences and circumstances.

[0276] "Information about the user's preferences and situation" refers to individual information such as the user's preferred scent, current physical condition, and environment.

[0277] A "generative AI model" refers to an artificial intelligence model that calculates scent generation patterns based on input data.

[0278] A "prompt" is an instruction to be input into a generative AI model, and refers to text generated based on the user's preferences and situation.

[0279] "Scent production pattern" refers to the specific steps and combination of ingredients used to produce a particular scent.

[0280] The term "fragrance generating means" refers to a device or mechanism for actually generating a fragrance based on the generated fragrance generation pattern.

[0281] The present invention relates to a system for generating a fragrance according to a user's preferences and circumstances. Specific embodiments of this system will be described below.

[0282] Server Roles

[0283] The server collects information about the user's preferences and circumstances. Specifically, it uses information the user enters into the application and health data obtained from the wearable device. For example, if the user enters "I like the smell of coffee" into the application, that information is stored in a database. It may also obtain information from the wearable device that "the user has a cold."

[0284] Next, the server generates a prompt sentence to be input into the generative AI model based on the collected information. For example, if the information indicates that the user likes the smell of coffee, the server generates a prompt sentence such as, "The user likes the smell of coffee, so please generate a coffee smell." If the user has a cold, the server generates a prompt sentence such as, "The user has a cold, so please generate a eucalyptus scent that will relieve cold symptoms."

[0285] The generated prompt text is sent to the device using an HTTP request, for example, by packaging the generated prompt text in JSON format and sending it to the device's API endpoint.

[0286] Device Role

[0287] The terminal receives the prompt sentence sent from the server. Specifically, the terminal receives the HTTP request and analyzes the prompt sentence. For example, it analyzes the received JSON data and extracts the prompt sentence.

[0288] Next, the device inputs the received prompt into a generative AI model to calculate the aroma generation pattern. Specifically, the prompt is input into a generative AI model (e.g., OpenAI (registered trademark)'s GPT-3), and the generated text is analyzed to extract the aroma generation pattern. For example, in response to the prompt "Please generate a coffee aroma," the generative AI model generates the text "The components of coffee aroma are caffeine, chlorogenic acid, and pyrrolidine."

[0289] The calculated scent generation pattern is again packaged in JSON format and sent to the API endpoint of the scent generation device. For example, the generation pattern is sent in the format "{ 'ingredients': ['caffeine', 'chlorogenic acid', 'pyrrolidine']}".

[0290] User Roles

[0291] The user receives the scent provided by the scent generating device. Specifically, the scent generating device generates a scent based on the received scent generation pattern and provides it to the user. For example, a smart diffuser mixes the ingredients "caffeine, chlorogenic acid, and pyrrolidine" to generate a coffee scent and release it to the user.

[0292] Specific examples

[0293] Example 1: If the user likes the smell of coffee

[0294] 1. The server collects information that "the user likes the smell of coffee."

[0295] 2. The server sends the prompt "Generate the aroma of coffee" to the generative AI model.

[0296] 3. The device uses the generative AI model to calculate the coffee aroma generation pattern.

[0297] 4. The device sends a pattern to the smart diffuser to create the coffee aroma.

[0298] 5. The user receives the aroma of coffee from the smart diffuser.

[0299] Example 2: If the user has a cold

[0300] 1. The server collects information that "the user has a cold."

[0301] 2. The server sends the prompt to the generative AI model: "Generate a eucalyptus scent that will relieve cold symptoms."

[0302] 3. The device uses a generative AI model to calculate the eucalyptus scent generation pattern.

[0303] 4. The device sends a pattern to the smart diffuser to generate a eucalyptus scent.

[0304] 5. The user receives the eucalyptus scent from the smart diffuser.

[0305] Prompt Sentence Examples

[0306] "Users like the smell of coffee, so generate a coffee smell."

[0307] "The user has a cold, so generate a eucalyptus scent to soothe cold symptoms."

[0308] In this way, a system can be realized that can generate a fragrance according to the user's preferences and circumstances. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0309] Step 1:

[0310] The server collects information about the user's preferences and circumstances. Specifically, it uses information the user enters into the application and health data obtained from the wearable device. For example, if the user enters "I like the smell of coffee," that information is stored in a database. It may also obtain information from the wearable device that "the user has a cold."

[0311] Input: User application input data, health data from wearable devices

[0312] Output: Information data about the user's preferences and situation

[0313] Step 2:

[0314] The server generates prompts to be input into the generative AI model based on the collected information. Specifically, it uses a natural language processing algorithm to create prompts that match the user's preferences and the situation. For example, it generates a prompt such as, "The user likes the smell of coffee, so please generate the smell of coffee."

[0315] Input: User preferences and contextual information data

[0316] Output: Prompt sentence to be input to the generative AI model

[0317] Step 3:

[0318] The server sends the generated prompt text to the device. Specifically, it sends the prompt text to the device using an HTTP POST request. For example, it packages the generated prompt text in JSON format and sends it to the device's API endpoint.

[0319] Input: A prompt to be input to the generative AI model

[0320] Output: Prompt sent to the terminal

[0321] Step 4:

[0322] The terminal receives the prompt sentence sent from the server. Specifically, the terminal receives the HTTP request and analyzes the prompt sentence. For example, it analyzes the received JSON data and extracts the prompt sentence.

[0323] Input: Prompt sent from the server

[0324] Output: Parsed prompt statement

[0325] Step 5:

[0326] The device inputs the received prompt into a generative AI model and calculates the aroma generation pattern. Specifically, the prompt is input into a generative AI model (for example, OpenAI's GPT-3), and the generated text is analyzed to extract the aroma generation pattern. For example, in response to the prompt "Please generate the aroma of coffee," the generative AI model generates the text "The components of coffee aroma are caffeine, chlorogenic acid, and pyrrolidine."

[0327] Input: Parsed prompt statement

[0328] Output: Scent generation pattern

[0329] Step 6:

[0330] The terminal sends the calculated scent generation pattern to the scent generation device. Specifically, the generation pattern is packaged in JSON format and sent to the scent generation device's API endpoint. For example, the generation pattern is sent in the format "{ 'ingredients': ['caffeine', 'chlorogenic acid', 'pyrrolidine']}".

[0331] Input: scent generation pattern

[0332] Output: Generated pattern sent to the scent generating device

[0333] Step 7:

[0334] The user receives the scent provided by the scent generating device. Specifically, the scent generating device generates a scent based on the received scent generation pattern and provides it to the user. For example, a smart diffuser mixes the ingredients "caffeine, chlorogenic acid, and pyrrolidine" to generate a coffee scent and release it to the user.

[0335] Input: Generated pattern sent to the scent generating device

[0336] Output: The scent provided to the user

[0337] (Application example 3)

[0338] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0339] Conventional scent generation systems have difficulty generating scents that suit user preferences and situations, and have not been able to provide optimal scents to improve customer experience in physical stores. Therefore, there is a need for an effective scent generation system that can increase customer satisfaction.

[0340] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0341] In this invention, the server includes a sensor means for performing detailed identification of the scent, a means for inputting scent data identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, a means for adjusting the scent generation pattern according to the user's preferences and circumstances, and a means for generating and providing an optimal scent in a physical store based on the customer's preferences and circumstances. This makes it possible to provide an optimal scent in a physical store according to the customer's preferences and circumstances.

[0342] The "sensor means for detailed identification of scents" is a sensor device for detecting and identifying the components and characteristics of scents in detail.

[0343] "Generative AI" is an artificial intelligence system that generates scent generation patterns based on input data.

[0344] A "scent generation pattern" is a data set that shows instructions and steps for generating a specific scent.

[0345] The "fragrance generation means" is a device that actually generates a fragrance based on the fragrance generation pattern generated by the generation AI.

[0346] A "means for adjusting the scent generation pattern according to the user's preferences and circumstances" is a device or system that has the function of dynamically changing the scent generation pattern based on the user's individual preferences and current circumstances.

[0347] "Means for generating and providing optimal fragrances based on customer preferences and circumstances within a physical store" refers to a device or system that detects customer preferences and circumstances within a physical store and generates and provides optimal fragrances based on those preferences and circumstances.

[0348] A "smartphone" is a multi-functional mobile device that can connect to the Internet and run applications in addition to the functions of a mobile phone.

[0349] "Smart glasses" are glasses-type devices that have the ability to display visual information, connect to the Internet, and run applications.

[0350] A "head-mounted display" is a display device worn on the head to display visual information.

[0351] A system for implementing this invention includes a sensor means for performing detailed scent identification, a generation AI, a scent generation means, a means for adjusting the scent generation pattern according to the user's preferences and circumstances, and a means for generating and providing the optimal scent based on the customer's preferences and circumstances within a physical store.

[0352] System program generation

[0353] The server receives data from the sensor means for detailed scent identification and inputs that data into the generation AI. The generation AI generates a scent generation pattern based on the input data. The generated pattern is sent to the scent generation means, which actually generates the scent.

[0354] Program processing explanation

[0355] The server uses sensor means (e.g., gas sensors or electronic noses) to identify the scent in detail and detect the scent's components and characteristics in detail. This data is input into the generation AI. The generation AI uses software such as Python or TensorFlow to generate a scent generation pattern based on the input data. This generation pattern is sent to the scent generation means (e.g., an aroma diffuser), which actually generates the scent.

[0356] The means to adjust the scent generation pattern according to the user's preferences and circumstances is realized through applications installed on devices such as smartphones, smart glasses, and head-mounted displays. These devices detect the user's preferences and current circumstances and send that information to a server. The server then provides prompts to the generation AI based on this information and generates the optimal scent generation pattern.

[0357] Specific examples

[0358] For example, when a user enters a coffee shop, the smartphone app automatically launches and detects the user's preferences and current situation. If the user likes the smell of coffee, the AI ​​will prioritize generating a coffee aroma. If the user has a cold, it will generate a eucalyptus scent.

[0359] Prompt Sentence Examples

[0360] "The user likes coffee and vanilla and is currently healthy. Use this information to generate the optimal scent."

[0361] In this way, it becomes possible to provide optimal scents in physical stores according to customer preferences and circumstances.

[0362] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0363] Step 1:

[0364] The server receives data from a sensor means for detailed scent identification. The sensor means uses a gas sensor, electronic nose, or the like to detect the components and characteristics of the surrounding scent in detail. The input data is scent component information and concentration information. The server receives this data and passes it on to the next processing step.

[0365] Step 2:

[0366] The server inputs the received scent data into the generation AI. The generation AI uses software such as Python and TensorFlow to generate a scent generation pattern based on the input data. Specifically, the generation AI analyzes the scent component information and determines the optimal scent combination and concentration. The output is a scent generation pattern.

[0367] Step 3:

[0368] The server sends the scent generation pattern generated by the generation AI to the scent generation means. The scent generation means is a device such as an aroma diffuser, which actually generates the scent based on the received generation pattern. Specifically, the scent generation means mixes the specified ingredients at the appropriate concentration and releases the scent. The output is the generated scent.

[0369] Step 4:

[0370] The device (smartphone, smart glasses, head-mounted display) detects the user's preferences and situation. The device collects user input and sensor data (e.g., body temperature and heart rate) to determine the user's current state. The input data is the user's preference information and health status information. The device transmits this information to a server.

[0371] Step 5:

[0372] The server provides prompts to the generation AI based on information about the user's preferences and situation received from the device. The generation AI adjusts the scent generation pattern based on these prompts. Specifically, the generation AI takes into account the user's preferences and health condition and recalculates the optimal scent combination and concentration. The output is an adjusted scent generation pattern.

[0373] Step 6:

[0374] The server transmits the adjusted scent generation pattern to the scent generation means again. The scent generation means regenerates the scent based on the received generation pattern. Specifically, the scent generation means mixes the recalculated ingredients at appropriate concentrations and releases the scent. The output is the adjusted scent.

[0375] Step 7:

[0376] Users can experience the scents generated in the physical store. By smelling the scents generated, users can experience effects such as relaxation and improved concentration. Specifically, users can enjoy the changing scents as they walk around the store.

[0377] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0378] "Example 1"

[0379] In one embodiment of the present invention, an emotion engine recognizes a user's emotion and provides that information to a generation AI. The generation AI generates a scent generation pattern that takes this emotion information into account. For example, if the emotion engine recognizes that the user is feeling joy, the generation AI generates a scent generation pattern that amplifies that joy, such as a citrus scent.

[0380] "Example 2"

[0381] The generation AI also generates a scent generation pattern that takes into account the user's emotions recognized by the emotion engine as well as time and space constraints. For example, if the emotion engine recognizes that the user wants to relax at night, the generation AI will generate a scent generation pattern for lavender, which has a relaxing effect, taking into account the user's desire to relax and the time constraints of nighttime.

[0382] "Example 3"

[0383] Furthermore, the generation AI adjusts the scent generation pattern taking into account the user's emotions recognized by the emotion engine, as well as the user's preferences and situation. For example, if the emotion engine recognizes that the user is feeling sad and there is information that the user likes the scent of roses, the generation AI will generate a rose scent generation pattern that alleviates the sadness and at the same time satisfies the user's preferences.

[0384] The processing flow of each embodiment will be described below.

[0385] "Example 1"

[0386] Step 1: The emotion engine recognizes the user's emotion.

[0387] Step 2: The emotion engine provides the recognized emotion information to the generation AI.

[0388] Step 3: The generation AI generates a scent generation pattern taking into account the provided emotional information.

[0389] Step 4: Generate a scent based on the scent generation pattern generated by the generation AI.

[0390] "Example 2"

[0391] Step 1: The emotion engine recognizes the user's emotions and time and space constraints.

[0392] Step 2: The emotion engine provides the recognized emotions and time and space constraint information to the generation AI.

[0393] Step 3: The generation AI generates a scent generation pattern taking into account the provided information. Step 4: The generation AI generates a scent based on the scent generation pattern generated.

[0394] "Example 3"

[0395] Step 1: The emotion engine recognizes the user's emotions, preferences, and situation.

[0396] Step 2: The emotion engine provides the recognized information to the generation AI.

[0397] Step 3: The generation AI adjusts the scent generation pattern taking into account the provided information. Step 4: The generation AI generates a scent based on the adjusted scent generation pattern.

[0398] Example 1

[0399] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0400] Conventional scent generation systems have difficulty identifying scent components in detail and generating scents that match the user's emotions and preferences. Furthermore, they have not been able to generate scents that reflect the user's emotional state in real time. This makes it impossible to optimize the user experience and maximize the effects of the scent.

[0401] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0402] In this invention, the server includes a sensor means for performing detailed scent identification, a means for inputting scent data identified by the sensor means into a generation AI and having the generation AI generate a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, an emotion engine for recognizing a user's emotion, and a means for providing emotion information recognized by the emotion engine to the generation AI and having the generation AI generate a scent generation pattern taking the emotion information into consideration. This makes it possible to generate a scent that reflects the user's emotional state in real time, optimizing the user experience and maximizing the effect of the scent.

[0403] The "sensor means" is a device for identifying the scent in detail, and analyzes compounds in the air to identify the scent components.

[0404] "Generative AI" is an artificial intelligence that uses machine learning technology to learn and generate scent generation patterns from scent data.

[0405] The "fragrance generation means" is a device that mixes the necessary compounds to generate a fragrance based on the generation pattern output from the generation AI.

[0406] An "emotion engine" is a device or software that recognizes a user's emotions, analyzing the user's facial expressions and tone of voice through sensor devices such as cameras and microphones.

[0407] "Scent generation patterns" refer to the combinations and blending ratios of compounds that create a specific scent, generated by the generation AI based on scent data and emotional information.

[0408] "Emotion information" is data that indicates the user's emotional state, obtained by the emotion engine by analyzing the user's facial expression, tone of voice, and the like.

[0409] MODE FOR CARRYING OUT THE INVENTION

[0410] This invention provides an IoT device equipped with a sensor means for detailed scent identification, a scent generation AI, and a scent generation means. Specific embodiments of this system are described below.

[0411] Scent discrimination by sensor means

[0412] The server uses gas chromatography to analyze compounds in the air and identify the scent components in detail. The gas chromatography device separates and detects each compound by passing the air sample through the device. The data on the detected compounds is sent to the server.

[0413] Inputting scent data into AI

[0414] The server inputs the identified fragrance component data into the generative AI. Specifically, the server retrieves the fragrance component data stored in the database and converts it into the input format of the generative AI. This data is then fed into the generative AI model.

[0415] Learning and generating scent generation patterns

[0416] The server uses generative AI to learn and generate scent generation patterns. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to train a model based on scent component data. The trained model then outputs new scent generation patterns.

[0417] Generation of fragrance by fragrance generating means

[0418] The server controls the scent generation means based on the generation pattern output by the generation AI. Specifically, the server operates a precision chemical mixing device to mix the required compounds in the correct proportions, thereby generating the specified scent.

[0419] User Emotion Recognition

[0420] The device uses an emotion engine to recognize the user's emotions. Specifically, the device captures the user's facial expressions and tone of voice through a camera and microphone, and the emotion engine analyzes this data. As a result of the analysis, the user's emotional state is identified.

[0421] Providing emotional information to AI

[0422] The device provides the recognized emotion information to the generative AI. Specifically, the device sends the emotion data obtained from the emotion engine to the server and uses it as input data for the generative AI. This data is fed into the generative AI model.

[0423] Emotion-based scent generation pattern generation

[0424] The server generates a scent generation pattern that takes emotional information into account. Specifically, the generation AI receives emotional data as input and outputs a scent generation pattern that is appropriate for the user's emotional state. For example, if the user feels like relaxing, the generation AI will generate a scent generation pattern that has a relaxing effect, such as lavender or chamomile.

[0425] Examples and prompts

[0426] Specific examples

[0427] If the user feels like relaxing, the device uses a camera and microphone to capture the user's facial expressions and tone of voice. The emotion engine analyzes this data and recognizes that the user wants to relax. The device then sends this emotional information to the server, which inputs it into the generation AI. The generation AI outputs a pattern for generating a scent that has a relaxing effect, and the server controls the scent generation means based on this pattern. Ultimately, a lavender or chamomile scent is generated and provided to the user.

[0428] Prompt Sentence Examples

[0429] "If the user feels like they want to relax, what kind of scent generation pattern will the generative AI generate?"

[0430] By inputting this prompt into the generative AI model, a specific answer can be obtained regarding the pattern of generating a relaxing scent.

[0431] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0432] Step 1:

[0433] The server uses gas chromatography to analyze compounds in the air and identify the scent components in detail. Specifically, the server controls the gas chromatography device, which separates and detects each compound by passing the air sample through the device. It receives the air sample as input and obtains data on each compound as output. This data is sent to the server.

[0434] Step 2:

[0435] The server inputs the identified fragrance component data into the generative AI. Specifically, the server retrieves the fragrance component data stored in the database and converts it into the input format of the generative AI. It receives the fragrance component data as input and generates data in a format suitable for the generative AI as output. This data is fed into the generative AI model.

[0436] Step 3:

[0437] The server uses generative AI to learn and generate scent generation patterns. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to train a model based on scent component data. It receives scent component data as input and obtains scent generation patterns as output. The trained model then outputs new scent generation patterns.

[0438] Step 4:

[0439] The server controls the scent generation means based on the generation pattern output by the generation AI. Specifically, the server operates a precision chemical mixing device to mix the required compounds in the correct proportions. It receives the generation pattern as input and generates the specified scent as output.

[0440] Step 5:

[0441] The device uses an emotion engine to recognize the user's emotions. Specifically, the device captures the user's facial expressions and tone of voice through a camera and microphone, and the emotion engine analyzes this data. It receives the user's facial and voice data as input and identifies the user's emotional state as output.

[0442] Step 6:

[0443] The device provides the recognized emotion information to the generative AI. Specifically, the device sends the emotion data obtained from the emotion engine to the server, which uses it as input data for the generative AI. It receives emotion data as input and generates data in a format suitable for the generative AI as output. This data is fed into the generative AI model.

[0444] Step 7:

[0445] The server generates a scent generation pattern taking emotional information into consideration. Specifically, the generation AI receives emotional data as input and outputs a scent generation pattern appropriate for the user's emotional state. It receives emotional data as input and obtains an emotion-based scent generation pattern as output. For example, if the user feels like relaxing, the generation AI generates a scent generation pattern that has a relaxing effect, such as lavender or chamomile.

[0446] (Application example 1)

[0447] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0448] Conventional scent generation systems have difficulty generating scents that correspond to the user's emotions and circumstances, and have not provided enough scents to optimize the user experience, especially in physical stores. In addition, there is a lack of technology to generate scents based on the user's emotions, making it difficult to provide personalized scents to individual users.

[0449] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes sensor means for performing detailed identification of scents, means for inputting scent data identified by the sensor means to the generation AI and causing the generation AI to generate a scent generation pattern based on the data, scent generation means for generating a scent based on the generation pattern, emotion recognition means for recognizing the user's emotion, and means for providing emotion information recognized by the emotion recognition means to the generation AI and causing the generation AI to generate a scent generation pattern taking the emotion information into consideration. This makes it possible to generate a personalized scent according to the user's emotion and situation.

[0450] "Detailed scent identification" involves analyzing compounds in the air using techniques such as gas chromatography to identify the scent components in detail.

[0451] The "sensor means" refers to a device or equipment for performing detailed identification of scents.

[0452] "Generative AI" is an artificial intelligence that uses machine learning technology to learn and generate scent generation patterns from scent data.

[0453] "Fragrance generation means" refers to a device or equipment that generates a fragrance by mixing the necessary compounds based on the generation pattern output from the generation AI.

[0454] An "emotion recognition means" is a device or equipment that recognizes the user's emotions and provides that information to the generating AI.

[0455] A "generated pattern" is a pattern of fragrance composition and blending that the generative AI generates based on fragrance data and emotional information.

[0456] "User emotion" refers to the emotional state that the user is feeling, such as happiness, sadness, or relaxation.

[0457] The system for implementing this invention includes a sensor means for detailed scent identification, a generation AI, a scent generation means, and an emotion recognition means. The specific configuration and operation of each means will be described below.

[0458] Sensor Means

[0459] The sensor means is a device that uses techniques such as gas chromatography to analyze compounds in the air and identify the fragrance components in detail. This sensor means is installed in a physical store and samples the surrounding air to obtain fragrance component data.

[0460] Generation AI

[0461] The generation AI is an artificial intelligence that uses machine learning technology to learn and generate scent generation patterns from scent data. The scent component data acquired from the sensor means is input into the generation AI. The generation AI generates a scent generation pattern based on this data.

[0462] Fragrance generating means

[0463] The scent generation means is a device that generates a scent by mixing the necessary compounds based on the generation pattern output by the generation AI. This device is installed in a physical store and diffuses the generated scent throughout the store.

[0464] emotion recognition means

[0465] The emotion recognition means is a device that recognizes the user's emotions and provides that information to the generation AI. When the user inputs their emotions via their smartphone, the information is analyzed by the emotion recognition means and sent to the generation AI.

[0466] System Operation

[0467] 1. Acquiring data on fragrance components: Sensors installed in physical stores analyze compounds in the air and acquire data on fragrance components.

[0468] 2. Generation of the generation pattern: The acquired fragrance component data is input into the generation AI, which then generates a fragrance generation pattern.

[0469] 3. Acquiring emotional information: The user inputs their emotions through their smartphone, and the information is analyzed by the emotion recognition means.

[0470] 4. Scent generation: The generation AI adjusts the scent generation pattern taking into account emotional information, and the scent generation means generates the scent based on that pattern.

[0471] 5. Scent diffusion: The generated scent is diffused within the physical store to optimize the user experience.

[0472] Specific examples

[0473] For example, if a user feels like relaxing in a store, the emotion recognition means recognizes that emotion, and the generation AI creates a pattern for generating a lavender scent. The scent generation means generates a lavender scent based on that pattern and diffuses it throughout the store.

[0474] Prompt Sentence Examples

[0475] User ID: 12345

[0476] Emotion: Relaxed

[0477] Generative AI: Creating a lavender scent generation pattern

[0478] Fragrance generation method: Generates lavender scent and spreads it throughout the store

[0479] In this way, a scent can be generated in a physical store that corresponds to the user's emotions, providing an optimal atmosphere.

[0480] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0481] Step 1:

[0482] The sensor means analyzes compounds in the air and obtains data on the components of the scent. The sensor means uses gas chromatography to identify the compounds in the air in detail and sends that data to the generation AI. The input is the compounds in the air, and the output is data on the components of the scent.

[0483] Step 2:

[0484] The generation AI generates a scent generation pattern based on the scent component data received from the sensor means. The generation AI uses machine learning technology to analyze the input scent component data and output the optimal scent generation pattern. The input is scent component data, and the output is a scent generation pattern.

[0485] Step 3:

[0486] The user inputs their emotions through a smartphone. The user inputs their emotional state using a dedicated application. The input is the user's emotional information, and the output is emotional data sent to the emotion recognition means.

[0487] Step 4:

[0488] The emotion recognition means analyzes the user's emotional information and provides that information to the generation AI. The emotion recognition means analyzes the input emotional information and sends it to the generation AI. The input is the user's emotional information, and the output is analyzed emotional data.

[0489] Step 5:

[0490] The generation AI adjusts the scent generation pattern taking into account emotional information. The generation AI integrates scent component data from the sensor means and emotional data from the emotion recognition means to regenerate the optimal scent generation pattern. The input is scent component data and emotional data, and the output is the adjusted scent generation pattern.

[0491] Step 6:

[0492] The scent generation means generates a scent based on the generation pattern received from the generation AI. The scent generation means mixes the necessary compounds and diffuses the generated scent throughout the physical store. The input is the adjusted scent generation pattern, and the output is the generated scent.

[0493] Step 7:

[0494] The generated scent is diffused throughout the physical store to optimize the user experience. The scent generation means diffuses the generated scent throughout the store, and the user experiences the scent. The input is the generated scent, and the output is the scent diffused throughout the store.

[0495] In this way, a scent can be generated that matches the user's emotions, providing an optimal atmosphere within the physical store.

[0496] Example 2

[0497] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0498] Conventional scent generation systems generate scents without considering the user's emotional state or time and space constraints, and therefore are unable to fully meet the user's needs. Furthermore, because scents cannot be generated based on the user's emotional state, it is difficult to achieve specific effects such as relaxation or refreshment. This has led to the issue of reduced user satisfaction.

[0499] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0500] In this invention, the server includes sensor means for performing detailed identification of scents, means for inputting scent data identified by the sensor means to a generation AI and having the generation AI generate a scent generation pattern based on the data, scent generation means for generating a scent based on the generation pattern, emotion engine means for analyzing a user's emotional state, and means for inputting the emotional state analyzed by the emotion engine means to the generation AI and having the generation AI generate a scent generation pattern taking the emotional state and time and space constraints into consideration. This makes it possible to generate a scent that takes the user's emotional state and time and space constraints into consideration.

[0501] The "sensor means for detailed scent identification" is a device for detecting scent components and intensity and outputting the results as data.

[0502] "Generative AI" is an artificial intelligence model that creates scent generation patterns based on input data.

[0503] An "aroma generation pattern" is data that includes instructions and settings for generating a specific aroma.

[0504] The "fragrance generating means" is a device that actually emits a fragrance based on the generated fragrance pattern.

[0505] The "emotion engine means" is a device or software for analyzing the user's emotional state and outputting the results as data.

[0506] "Time and space constraints" refer to the time and location conditions that must be taken into consideration when creating a scent.

[0507] The "user's emotional state" refers to the emotional and mood state that the user is currently experiencing.

[0508] MODE FOR CARRYING OUT THE INVENTION

[0509] The present invention relates to a system for generating a scent taking into consideration the emotional state of a user and time and space constraints. Specific embodiments of this system will be described below.

[0510] System configuration

[0511] The system consists of the following main components:

[0512] 1. Sensor means: A device for detailed identification of scents. This sensor means detects the components and strength of the scent and outputs it as data.

[0513] 2. Generative AI: An artificial intelligence model that creates a scent generation pattern based on input data. Generative AI generates a scent generation pattern taking into account the user's emotional state and time and space constraints.

[0514] 3. Scent generating means: A device that actually emits the scent based on the generated scent pattern.

[0515] 4. Emotion engine means: A device or software that analyzes the user's emotional state and outputs the results as data.

[0516] Program processing

[0517] The server inputs scent data acquired from the sensor means into the generation AI. The generation AI creates a scent generation pattern based on the data. Furthermore, the server uses the emotion engine means to analyze the user's emotional state and inputs the results into the generation AI. The generation AI generates a scent generation pattern taking into account the user's emotional state and time and space constraints.

[0518] The generated scent pattern is sent from the server to the scent generator, which then emits the scent, providing the optimal scent according to the user's emotional state and time and space constraints.

[0519] Specific examples

[0520] For example, consider a case where a user feels like they want to refresh themselves in the morning. The user opens a dedicated app and enters "I want to refresh myself in the morning." The device sends this request to the server. The server uses the emotion engine means to analyze the user's desire to refresh themselves. Next, the server uses the generation AI to create a generation pattern for a refreshing forest scent, taking into account the time of day (morning) and the feeling of wanting to refresh themselves. The server sends this generation pattern to the scent generation means, which then uses a built-in diffuser to emit a refreshing forest scent.

[0521] Prompt Sentence Examples

[0522] If the user is feeling refreshed in the morning, create a fresh forest scent.

[0523] In this way, the server and the terminal can work together to provide the optimal scent according to the user's living environment, time of day, and emotional state.

[0524] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0525] Step 1:

[0526] The user operates the device to request the generation of a scent.

[0527] The user opens the app on a smartphone, tablet, or other device and enters, "I want to refresh myself in the morning." This input is sent to the device.

[0528] Step 2:

[0529] The terminal sends the user's request to the server.

[0530] The device sends the user's request to the server. This request includes information such as the user's emotional state, time of day, and location. The input is the user's request data, and the output is the request sent to the server.

[0531] Step 3:

[0532] The server uses an emotion engine to analyze the user's emotions.

[0533] The server uses an emotion engine to analyze the user's emotional state. The emotion engine determines the user's current emotional state based on the user's input and past data. The input is the user's request data, and the output is the analyzed emotional state data.

[0534] Step 4:

[0535] The server uses a generative AI model to create a scent generation pattern.

[0536] The server uses the generative AI model to create a scent generation pattern that takes into account the user's emotional state and time and space constraints. For example, if the user feels like they want to refresh themselves in the morning, the generative AI model will create a pattern for generating a refreshing forest scent. The input is the analyzed emotional state data and time and space constraint information, and the output is a scent generation pattern.

[0537] Step 5:

[0538] The server sends the generated scent pattern to the terminal.

[0539] The server sends the generated scent pattern to the device. This pattern includes information such as the type of scent, its intensity, and its duration. The input is the scent generation pattern, and the output is the pattern sent to the device.

[0540] Step 6:

[0541] The device receives the scent pattern and generates the scent.

[0542] The device actually generates the scent based on the scent pattern received from the server. For example, a diffuser built into the device emits a refreshing forest scent. The input is the scent generation pattern, and the output is the generated scent.

[0543] (Application example 2)

[0544] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0545] Conventional scent generation systems have difficulty adjusting scents according to the time of day and the user's emotions, making it impossible to provide the optimal scent for a specific situation. Furthermore, scents have not been effectively generated to increase customer purchasing motivation in physical stores. This has resulted in insufficient improvements to customer experience and sales growth.

[0546] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0547] In this invention, the server includes a sensor means for performing detailed identification of scents, a means for inputting scent data identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, an emotion recognition means for recognizing customer emotions, and a time recognition means for adjusting the scent generation pattern according to the time of day. This allows for the generation of an optimal scent according to the time of day and the customer's emotions, improving the customer experience in physical stores and increasing purchasing motivation.

[0548] The "sensor means for detailed identification of scents" is a device for detecting the components and intensity of scents and identifying them as detailed data.

[0549] "Generative AI" is an artificial intelligence system that creates scent generation patterns based on input data.

[0550] The "fragrance generation means" is a device that actually generates a fragrance based on the fragrance generation pattern created by the generation AI.

[0551] An "emotion recognition means" is a device or system for analyzing a customer's facial expressions and behavior and identifying their emotional state.

[0552] The "time recognition means" is a device or system that recognizes the current time and adjusts the scent generation pattern based on that information.

[0553] The system for implementing this invention includes a sensor means for detailed scent identification, a generation AI, a scent generation means, an emotion recognition means, and a time recognition means. The specific operation of each means and their cooperation will be explained below.

[0554] First, sensors are installed in the store to identify the fragrance components and intensity in detail. This generates fragrance data, which is then input into the generation AI.

[0555] The generation AI creates a scent generation pattern based on the input scent data. In doing so, the generation AI takes into account time information from the time recognition means and emotional information from the emotion recognition means. For example, if it is morning and the customer feels like relaxing, the generation AI will create a scent generation pattern that has a relaxing effect.

[0556] The scent generator actually generates the scent based on the scent generation pattern created by the generation AI. The generated scent is then diffused throughout the store to improve the customer experience.

[0557] The emotion recognition system uses in-store camera sensors to analyze customers' facial expressions and behaviors to identify their emotional state. For example, if a customer is feeling stressed, that information is sent to the generation AI.

[0558] The time recognition means recognizes the current time and provides that information to the generation AI, which then can adjust the scent generation pattern according to the time of day.

[0559] As a concrete example, the following prompt sentence is input to the generative AI model:

[0560] "The current time is 10:00 AM. What scent would you produce if your customer is stressed?"

[0561] "It's currently 6pm. What scent would you produce if your customer is feeling relaxed?"

[0562] This allows the generative AI model to provide an appropriate scent generation pattern.

[0563] This system generates the optimal scent according to the time of day and the customer's emotions, improving the customer experience in physical stores and increasing purchasing motivation.

[0564] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0565] Step 1:

[0566] The sensor means identifies the scent in the store in detail and generates data on its components and intensity.

[0567] Input: Scent inside the store

[0568] Output: Fragrance component data

[0569] Step 2:

[0570] The generation AI receives scent component data from the sensor means and creates a scent generation pattern taking into account time information from the time recognition means and emotion information from the emotion recognition means.

[0571] Input: Fragrance component data, time information, emotional information

[0572] Output: Scent generation pattern

[0573] Step 3:

[0574] The emotion recognition means uses camera sensors in the store to analyze the customer's facial expressions and behavior to identify their emotional state.

[0575] Input: Customer's facial expressions and behavior

[0576] Output: Emotional information

[0577] Step 4:

[0578] The time recognition means recognizes the current time and provides that information to the generation AI.

[0579] Input: current time

[0580] Output:Time information

[0581] Step 5:

[0582] The scent generation means actually generates the scent based on the scent generation pattern from the generation AI and spreads it throughout the store.

[0583] Input: scent generation pattern

[0584] Output: Generated scent

[0585] Step 6:

[0586] The generation AI uses prompt text to adjust the scent generation pattern. For example, by entering a prompt text such as "It is now 10:00 AM. What kind of scent would you generate if the customer is feeling stressed?", it will provide an appropriate scent generation pattern.

[0587] Input: prompt statement

[0588] Output: Adjusted scent generation pattern

[0589] In this way, the system generates the optimal scent according to the time of day and the customer's emotions, improving the customer experience in physical stores and increasing purchasing motivation.

[0590] Example 3

[0591] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0592] Conventional scent generation systems have difficulty generating scents that match the user's preferences and circumstances, making it impossible to provide the optimal scent for each individual user. Furthermore, the collection and analysis of information needed to adjust scent generation patterns was insufficient, making it difficult to provide scents that match the user's emotions and health status. This resulted in an inability to increase user satisfaction.

[0593] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0594] In this invention, the server includes means for inputting information about a user's preferences and situation, means for transmitting the information to the server, means for the server to analyze the information and generate a prompt sentence, means for inputting the prompt sentence into a generative AI model and generating a scent generation pattern, and means for transmitting the generation pattern to the terminal. This makes it possible to generate a scent according to the user's preferences and situation, and to provide the optimal scent for each individual user.

[0595] The "sensor means" is a device for performing detailed identification of scents.

[0596] A "generative AI model" is an artificial intelligence algorithm that generates a scent generation pattern based on input data.

[0597] The "fragrance generating means" is a device that actually generates a fragrance based on the generated fragrance generation pattern.

[0598] The "means for inputting information relating to the user's preferences and circumstances" is an interface that allows the user to input information relating to his or her preferences and circumstances into the system.

[0599] The "means for transmitting the information to the server" refers to a communication means for transmitting the information input by the user to the server.

[0600] "Means for the server to analyze the information and generate a prompt sentence" refers to the function by which the server analyzes the user's input information and generates a prompt sentence to be input into the generative AI model based on that information.

[0601] "Means for inputting a prompt sentence into a generative AI model and generating a scent generation pattern" refers to a function for inputting a prompt sentence into a generative AI model and generating a scent generation pattern based on that sentence.

[0602] The "means for transmitting the generation pattern to the terminal" refers to a communication means for transmitting the generated scent generation pattern to the terminal.

[0603] The present invention relates to a system for generating a fragrance according to a user's preferences and circumstances. Specific embodiments of this system will be described below.

[0604] System configuration

[0605] The system mainly consists of the following elements:

[0606] 1. Sensor means: A device for identifying scents in detail. For example, a gas sensor or an electronic nose is one example.

[0607] 2. Generative AI model: An artificial intelligence algorithm that generates a scent generation pattern based on input data. For example, a machine learning model or neural network is used.

[0608] 3. Scent generation means: A device that actually generates a scent based on the generated scent generation pattern. Examples include scent cartridges and diffusers.

[0609] 4. A means for inputting user preferences and context information: An interface through which users can input information about their preferences and context into the system. For example, a smartphone app or a web interface.

[0610] 5. Means for transmitting the information to the server: This is a communication means for transmitting the information entered by the user to the server. For example, Wi-Fi or Bluetooth is used.

[0611] 6. Means for the server to analyze the information and generate a prompt sentence: This is a function that allows the server to analyze the user's input information and generate a prompt sentence to be input into the generative AI model based on that information.

[0612] 7. A means for inputting a prompt sentence into the generative AI model and generating a scent generation pattern: This is a function for inputting a prompt sentence into the generative AI model and generating a scent generation pattern based on it.

[0613] 8. Means for transmitting the generated pattern to a terminal: A communication means for transmitting the generated scent generation pattern to a terminal.

[0614] System Operation

[0615] User operations

[0616] Users input information about their preferences and circumstances through a smartphone app or web interface, such as "I like the smell of coffee" or "I have a cold."

[0617] Sending information

[0618] The terminal transmits the information input by the user to the server, converts the input information into an appropriate format, and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[0619] Server Processing

[0620] The server receives and analyzes the user information sent from the device. Based on the analyzed information, the server generates a prompt sentence to input into the generative AI model. For example, it generates a prompt sentence such as "The user likes the smell of coffee."

[0621] How generative AI models work

[0622] The server inputs a prompt statement into the generative AI model and generates a scent generation pattern. The generative AI model outputs the optimal scent generation pattern based on the prompt statement. For example, in response to the prompt statement "The user likes the scent of coffee," the generative AI model outputs a coffee scent generation pattern.

[0623] Sending generated patterns and generating scents

[0624] The server transmits the generated scent generation pattern to the terminal. The terminal receives the generation pattern and generates the scent based on it. The terminal is equipped with hardware for generating the scent, such as a scent cartridge or diffuser. The terminal selects an appropriate scent according to the generation pattern and provides it to the user.

[0625] Specific examples

[0626] Example 1: If the user likes the smell of coffee

[0627] 1. The user types "I love the smell of coffee" through the application.

[0628] 2. The device sends this information to the server.

[0629] 3. The server receives the information and generates a prompt saying, "The user likes the smell of coffee."

[0630] 4. The server inputs the prompt sentence into the generative AI model and generates a pattern for generating the coffee aroma.

[0631] 5. The server sends the generated pattern to the terminal.

[0632] 6. The device receives the generated pattern and generates the coffee aroma.

[0633] Example 2: When the user has a cold

[0634] 1. The user enters "I have a cold" through the application.

[0635] 2. The device sends this information to the server.

[0636] 3. The server receives the information and generates a prompt saying, "The user has a cold."

[0637] 4. The server inputs the prompt sentence into the generative AI model and generates a pattern for generating the eucalyptus scent.

[0638] 5. The server sends the generated pattern to the terminal.

[0639] 6. The device receives the generated pattern and generates a eucalyptus scent.

[0640] This makes it possible to provide a scent that is optimized for the user's preferences and circumstances. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0641] Step 1:

[0642] Users input information about their preferences and circumstances through a smartphone app or web interface, such as "I like the smell of coffee" or "I have a cold."

[0643] Input: User preferences and context information

[0644] Output: Input information is saved on the device

[0645] Step 2:

[0646] The terminal transmits the information input by the user to the server, converts the input information into an appropriate format, and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[0647] Input: User input information

[0648] Output: User information sent to the server

[0649] Step 3:

[0650] The server receives and analyzes the user information sent from the device. Based on the analyzed information, the server generates a prompt sentence to input into the generative AI model. For example, it generates a prompt sentence such as "The user likes the smell of coffee."

[0651] Input: User information sent to the server

[0652] Output: Generated prompt statement

[0653] Step 4:

[0654] The server inputs a prompt statement into the generative AI model and generates a scent generation pattern. The generative AI model outputs the optimal scent generation pattern based on the prompt statement. For example, in response to the prompt statement "The user likes the scent of coffee," the generative AI model outputs a coffee scent generation pattern.

[0655] Input: Generated prompt text

[0656] Output: Generated scent pattern

[0657] Step 5:

[0658] The server transmits the generated scent generation pattern to the terminal, converts the generated pattern into an appropriate format, and transmits it to the terminal using a secure communication protocol.

[0659] Input: Generated scent pattern

[0660] Output: Generated pattern sent to the terminal

[0661] Step 6:

[0662] The device receives the scent generation pattern sent from the server and generates the scent based on it. The device is equipped with hardware for generating the scent, such as a scent cartridge or diffuser. The device selects an appropriate scent according to the generation pattern and provides it to the user.

[0663] Input: Generated pattern sent from the server

[0664] Output: Generated scent

[0665] Specific examples of operation

[0666] Example 1: If the user likes the smell of coffee

[0667] 1. The user types "I love the smell of coffee" through the application.

[0668] 2. The device sends this information to the server.

[0669] 3. The server receives the information and generates a prompt saying, "The user likes the smell of coffee."

[0670] 4. The server inputs the prompt sentence into the generative AI model and generates a pattern for generating the coffee aroma.

[0671] 5. The server sends the generated pattern to the terminal.

[0672] 6. The device receives the generated pattern and generates the coffee aroma.

[0673] Example 2: When the user has a cold

[0674] 1. The user enters "I have a cold" through the application.

[0675] 2. The device sends this information to the server.

[0676] 3. The server receives the information and generates a prompt saying, "The user has a cold."

[0677] 4. The server inputs the prompt sentence into the generative AI model and generates a pattern for generating the eucalyptus scent.

[0678] 5. The server sends the generated pattern to the terminal.

[0679] 6. The device receives the generated pattern and generates a eucalyptus scent.

[0680] (Application example 3)

[0681] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0682] Conventional scent generation systems have difficulty generating scents that match the user's preferences and emotions, making it impossible to provide the optimal scent for each individual user. Furthermore, in physical stores, improving the user experience has been a challenge, as scents cannot be adjusted in real time to match the user's emotions and preferences. Furthermore, there are no systems that combine facial recognition and emotion recognition, making it impossible to meet the individual needs of users.

[0683] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0684] In this invention, the server includes a sensor means for performing detailed identification of the scent, a means for inputting scent data identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, a face recognition means for recognizing the user's face, an emotion recognition means for recognizing the user's emotion, and a means for adjusting the scent generation pattern based on the user's preferences and emotions. This makes it possible to provide the optimal scent in real time according to the user's preferences and emotions.

[0685] The "sensor means for detailed identification of scents" is a device for detecting the components and strength of scents and acquiring detailed data on them.

[0686] "Generative AI" is an artificial intelligence that creates scent generation patterns based on acquired data.

[0687] The "fragrance generation means" is a device that actually generates a fragrance based on the fragrance generation pattern created by the generation AI.

[0688] The "face recognition means" is a device for detecting the face of a user and identifying an individual user.

[0689] The "emotion recognition means" is a device for analyzing and recognizing emotions from the user's facial expressions, voice, etc.

[0690] The "means for adjusting the scent generation pattern" is a device for changing the scent generation pattern based on the user's preferences and emotions.

[0691] A system for implementing this invention includes a sensor means for performing detailed scent identification, a generation AI, a scent generation means, a face recognition means, an emotion recognition means, and a means for adjusting the scent generation pattern.

[0692] First, the sensor means detects the components and intensity of the scent and acquires detailed data. The acquired data is input into the generation AI, which then creates a scent generation pattern based on this data. The generation AI also includes a function to adjust the scent generation pattern based on the user's preferences and emotions.

[0693] The facial recognition means detects the user's face and identifies each individual user. This allows information about the user's past preferences and situations to be obtained from a database. The emotion recognition means analyzes and recognizes emotions from the user's facial expressions, voice, etc. This allows the user's current emotional state to be grasped in real time.

[0694] The scent generation means actually generates scents based on the scent generation patterns created by the generation AI, making it possible to provide optimal scents in real time according to the user's preferences and emotions.

[0695] For example, when a user enters a physical store, the facial recognition means identifies the user and recognizes from past data that the user likes the scent of lavender. If the emotion recognition means detects that the user is feeling stressed, the generation AI generates a relaxing lavender scent and releases it into the store through the scent generation means.

[0696] An example of a prompt is as follows:

[0697] "When a user enters the store, a facial recognition system is used to identify them and an emotion engine is used to analyze their emotions. Based on the user's preferences and emotions, the system generates the optimal scent and controls the scent diffuser to release it throughout the store."

[0698] The following hardware and software are used to realize this system: OpenCV for face recognition, EmotionEngine for emotion recognition, ScentGenerator for scent generation, and ScentDiffuser for scent emission. By combining these technologies, it becomes possible to provide scents in real time that match the user's preferences and emotions.

[0699] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0700] Step 1:

[0701] When a user enters a physical store, the device's camera captures the user's face. A facial recognition mechanism takes this image data as input and identifies the user using a facial recognition model (e.g., OpenCV). The output is a user ID.

[0702] Step 2:

[0703] The server retrieves information about the user's past preferences and situations from a database based on the user ID. This information includes the user's preferred scents and past emotional states. The input is the user ID, and the output is data about the user's preferences and situations.

[0704] Step 3:

[0705] The device's camera captures the user's facial expressions in real time, and the emotion recognition means receives this image data as input. It analyzes the user's current emotions using an emotion recognition model (e.g., EmotionEngine). The output is the user's emotional state.

[0706] Step 4:

[0707] The server inputs data about the user's preferences and situation, as well as their current emotional state, into the generation AI. The generation AI then creates a scent generation pattern based on this data. The input is the user's preferences, situation, and emotional state, and the output is the scent generation pattern.

[0708] Step 5:

[0709] The server transmits the generated scent generation pattern to the scent generation means, which generates a scent based on this pattern. The input is the scent generation pattern, and the output is the generated scent.

[0710] Step 6:

[0711] The scent generating means releases the generated scent into the store through a scent diffuser, thereby spreading the optimal scent according to the user's preferences and emotions throughout the store. The input is the generated scent, and the output is the scent released into the store.

[0712] 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.

[0713] 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> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0714] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0715] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0716] [Second embodiment]

[0717] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0718] 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.

[0719] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0720] 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.

[0721] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0722] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0723] 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.

[0724] 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.

[0725] 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 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.

[0726] 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.

[0727] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0728] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0729] "Example 1"

[0730] One embodiment of the present invention provides an IoT device equipped with a sensor means for performing detailed scent identification, a generation AI, and a scent generation means. The sensor means analyzes compounds in the air using, for example, gas chromatography to identify the scent components in detail. The identified scent data is input to the generation AI. The generation AI uses machine learning techniques such as deep learning to learn and generate a scent generation pattern from the scent data. The scent generation means mixes the necessary compounds based on the generation pattern output from the generation AI to generate a scent.

[0731] "Example 2"

[0732] Furthermore, the generation AI generates scent patterns that take into account time and space constraints. For example, if the user uses it in the morning, it will generate a refreshing forest scent. If the user uses it during relaxation time, it will generate a relaxing lavender scent. This makes it possible to provide scents that suit the user's living environment and time of day. "Example 3"

[0733] Furthermore, the generation AI adjusts the scent generation pattern according to the user's preferences and the situation. For example, if there is information that the user likes the scent of coffee, the generation AI will prioritize the coffee scent generation pattern. Also, if there is information that the user has a cold, the generation AI will generate a eucalyptus scent generation pattern, which is said to relieve cold symptoms. This makes it possible to provide a scent optimized for the user's preferences and situation.

[0734] The processing flow of each embodiment will be described below.

[0735] "Example 1"

[0736] Step 1: The sensor analyzes the compounds in the air and identifies the scent components in detail. This identification is performed using, for example, gas chromatography.

[0737] Step 2: The identified scent data is input into the generative AI.

[0738] Step 3: The generation AI uses machine learning techniques such as deep learning to learn and generate scent generation patterns from scent data.

[0739] Step 4: The fragrance generation means mixes the necessary compounds to generate the fragrance based on the generation pattern output from the generation AI.

[0740] "Example 2"

[0741] Step 1: The generative AI generates a scent generation pattern that takes into account time and space constraints.

[0742] Step 2: For example, if the user uses it in the morning, the generation AI will generate a pattern for generating a refreshing forest scent.

[0743] Step 3: If the user uses it during relaxation time, the generation AI will generate a pattern of lavender scent that has a relaxing effect.

[0744] "Example 3"

[0745] Step 1: The generation AI adjusts the scent generation pattern based on the user's preferences and the situation.

[0746] Step 2: For example, if there is information that the user likes the smell of coffee, the generation AI will prioritize the coffee smell generation pattern.

[0747] Step 3: If the user has a cold, the AI ​​generates a pattern for the scent of eucalyptus, which is said to relieve cold symptoms.

[0748] Example 1

[0749] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0750] Conventional scent generation systems have difficulty identifying scent components in detail and generating scents that suit the user's preferences and circumstances. It is also difficult to generate scents that take into account time and space constraints, making it impossible to meet the diverse needs of users.

[0751] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0752] In this invention, the server includes a sensor means for analyzing compounds in the air and identifying the fragrance components in detail, a means for inputting the identified fragrance component data into a generative AI model, which then learns a fragrance generation pattern based on the data, and a fragrance generation means for mixing the necessary compounds based on the generation pattern to generate a fragrance. This makes it possible to identify the fragrance components in detail and generate a fragrance that suits the user's preferences and situation. It is also possible to generate fragrances that take into account time and spatial constraints.

[0753] The "sensor means" is a device that analyzes compounds in the air and identifies the scent components in detail.

[0754] A "generative AI model" is an artificial intelligence model that uses machine learning technology to learn fragrance generation patterns from fragrance component data.

[0755] The "fragrance generation means" is a device that generates a fragrance by mixing the necessary compounds based on the generation pattern obtained from the generative AI model.

[0756] "Fragrance component data" refers to data relating to the types and concentrations of compounds that make up the fragrance identified by the sensor means.

[0757] A "generative pattern" is information about the combination and proportion of compounds required to produce a specific fragrance, learned by the generative AI model based on fragrance component data.

[0758] "Temporal and spatial constraints" refer to the temporal and spatial conditions and limitations that affect the generation and diffusion of scent.

[0759] "User preferences and circumstances" refers to individual requirements and conditions such as the type and strength of the scent desired by the user, and the situation in which it will be used.

[0760] MODE FOR CARRYING OUT THE INVENTION

[0761] The present invention is a system for detailed identification and generation of scents, which includes a sensor means, a generation AI model, and a scent generation means. Specific embodiments of this system will be described below.

[0762] Sensor Means

[0763] The server activates the sensor and analyzes the compounds in the air using a gas chromatography device. The gas chromatography device detects trace amounts of compounds in the air and identifies the concentration and type of each compound. The analysis results are sent to the server as fragrance component data.

[0764] Generative AI Models

[0765] The server inputs the identified scent component data into a generative AI model. The generative AI model is built using machine learning frameworks such as TensorFlow and PyTorch. The generative AI model learns scent generation patterns based on the scent component data using a deep learning algorithm. The learned generation patterns are sent to the scent generation means.

[0766] Fragrance generating means

[0767] The server controls the scent generator based on the generation pattern obtained from the generative AI model. The scent generator mixes the necessary compounds in the correct proportions to create the specified scent. This process uses specialized hardware to control chemical reactions. The generated scent is then provided to the user.

[0768] Specific examples

[0769] For example, suppose a user wants to create a "rose scent." The server uses sensor means to identify in detail the compounds that make up the rose scent and inputs that data into the generative AI model. The generative AI model learns the pattern for generating rose scent based on past data and sends that pattern to the scent generation means. The server controls the scent generation means and mixes the necessary compounds to generate the rose scent. Finally, the server provides the generated rose scent to the user.

[0770] Prompt Sentence Examples

[0771] "Please explain the process by which the generative AI learns the patterns of scent generation based on the detailed compounds that produce the rose scent."

[0772] In this way, a system is realized in which the sensor means, the generative AI model, and the scent generation means work together to perform detailed scent identification and generation.

[0773] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0774] Step 1:

[0775] The server activates the sensor means and analyzes the compounds in the air using a gas chromatography device. It receives the air sample as input and generates scent component data as output. Specifically, the gas chromatography device separates the compounds in the air and identifies the concentration and type of each compound. This data is sent to the server as scent component data.

[0776] Step 2:

[0777] The server inputs the identified scent component data into the generative AI model. It receives the scent component data as input and obtains a generated pattern as output. Specifically, the generative AI model uses machine learning frameworks such as TensorFlow and PyTorch to preprocess the scent component data and learns a scent generation pattern using a deep learning algorithm. The learned generated pattern is stored on the server.

[0778] Step 3:

[0779] The server controls the scent generator based on the generation pattern obtained from the generative AI model. It receives the generation pattern as input and generates the scent as output. Specifically, the scent generator mixes the necessary compounds in the correct proportions to create the specified scent. This process uses dedicated hardware to control the chemical reactions.

[0780] Step 4:

[0781] The server provides the generated scent to the user. It receives the generated scent as input and provides the scent to the user as output. Specifically, it provides the scent generated by the scent generation means through an interface that allows the user to check the scent. The user can check the operation of the system by smelling the generated scent.

[0782] In this way, a system is realized in which the sensor means, the generative AI model, and the scent generation means work together to perform detailed scent identification and generation.

[0783] (Application example 1)

[0784] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0785] Conventional scent generation systems are effective in identifying scents in detail and learning scent generation patterns, but they have limitations in generating scents that meet customer preferences and providing scents effectively within physical stores. Furthermore, it is difficult to generate scents that meet customer preferences and situations, making it difficult to contribute to improving the store atmosphere and customer satisfaction. This has led to a demand for new technologies to improve the quality of the customer experience.

[0786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0787] In this invention, the server includes a sensor means for performing detailed identification of scents, a means for inputting data on the scent identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, an input means for generating a scent according to customer preferences, and a control means for generating a scent based on the data from the input means. This makes it possible to generate scents according to customer preferences, thereby improving the quality of the customer experience in a physical store.

[0788] "Sensor means" refers to a device that analyzes compounds in the air to provide detailed scent identification.

[0789] "Generative AI" is an artificial intelligence that learns and generates scent generation patterns based on identified scent data.

[0790] The "fragrance generation means" is a device that generates a fragrance by mixing the necessary compounds based on the generation pattern output from the generation AI.

[0791] The "input means" is a device or interface for inputting data for generating a fragrance according to the customer's preferences.

[0792] The "control means" is a device or system for controlling the fragrance generating means based on data from the input means and generating a fragrance.

[0793] As an embodiment of the present invention, a smart scent display system installed in a brick-and-mortar store will be described as an example. This system includes a sensor means for detailed scent identification, a generation AI, a scent generation means, an input means for generating scents according to customer preferences, and a control means.

[0794] System configuration

[0795] 1. Sensor means:

[0796] A gas chromatography sensor is used to analyze compounds in the air and identify the fragrance components in detail.

[0797] The sensor data includes detailed information about the components of the scent.

[0798] 2. Generation AI:

[0799] Using deep learning technology, scent generation patterns are learned and generated based on scent data acquired from sensor means.

[0800] The software used is Keras (deep learning library) and Scikit-learn (data preprocessing).

[0801] 3. Scent generating means:

[0802] This device mixes the necessary compounds to generate fragrance based on the generation pattern output by the generation AI.

[0803] The scent generator precisely mixes compounds according to a generation pattern.

[0804] 4. Input method:

[0805] It is an interface that allows customers to input their preferred scents via smartphones or smart glasses.

[0806] The data entered reflects the customer's preferences and circumstances.

[0807] 5. Control measures:

[0808] This is a system for controlling a fragrance generating means based on data from an input means and generating a fragrance.

[0809] The control means operates the fragrance generation means according to instructions from the generation AI.

[0810] System Operation

[0811] 1. Acquiring sensor data:

[0812] The sensor means analyzes compounds in the air and identifies scent components.

[0813] The acquired sensor data is sent to the generation AI.

[0814] 2. Data preprocessing:

[0815] The generative AI standardizes the sensor data using Scikit-learn's StandardScaler.

[0816] The standardized data is then fed into a generative AI model.

[0817] 3. Training the generative AI model:

[0818] The generative AI defines a deep learning model using Keras and learns scent generation patterns based on sensor data.

[0819] The trained model is used to predict scent production patterns.

[0820] 4. Aroma generation:

[0821] Based on the generation pattern output by the generation AI, the fragrance generation means mixes the necessary compounds to generate the fragrance.

[0822] The control means operates the fragrance generating means based on data from the input means.

[0823] Specific examples

[0824] Physical store: In an apparel shop, customers can select a "relaxing scent" using their smartphone, and that scent will spread throughout the store.

[0825] Cafe: Customers select a "fresh scent" through smart glasses, and the scent spreads throughout the store.

[0826] Prompt Sentence Examples

[0827] When a customer uses their smartphone to select a "relaxing scent," that scent will fill the store.

[0828] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0829] Step 1:

[0830] The sensor means analyzes compounds in the air and identifies scent components.

[0831] Input: Compounds in the air

[0832] Data processing: Compounds are analyzed using a gas chromatography sensor to obtain data on fragrance components.

[0833] Output: Fragrance component data

[0834] Step 2:

[0835] The server transmits the scent component data acquired from the sensor means to the generation AI.

[0836] Input: Fragrance ingredient data

[0837] Data processing: Converting data into an appropriate format for input into the generative AI.

[0838] Output: Fragrance component data input into the generation AI

[0839] Step 3:

[0840] The generation AI learns and generates fragrance generation patterns based on fragrance component data.

[0841] Input: Fragrance ingredient data input into the generation AI

[0842] Data computation: Deep learning technology is used to learn and generate scent generation patterns.

[0843] Output: Scent generation pattern

[0844] Step 4:

[0845] The server sends the generated pattern output from the generation AI to the scent generation means.

[0846] Input: scent generation pattern

[0847] Data processing: Convert the generated pattern into a suitable format for input to the scent generation means.

[0848] Output: Generated pattern input to the scent generation means

[0849] Step 5:

[0850] The scent generating means mixes necessary compounds based on the generation pattern to generate the scent.

[0851] Input: A generated pattern input to the scent generation means

[0852] Data calculation: Accurately mix compounds according to the generated patterns.

[0853] Output: Generated scent

[0854] Step 6:

[0855] Users input their preferred scent via their smartphone or smart glasses.

[0856] Input: User's preferred scent data

[0857] Data processing: Converting user input data into an appropriate format for sending to the server.

[0858] Output: User's preferred scent data sent to the server

[0859] Step 7:

[0860] The server controls the fragrance generating means based on the fragrance data of the user's preference.

[0861] Input: User's preferred scent data

[0862] Data calculation: Adjusts the generation pattern from the generation AI based on the user's preferred scent data and controls the scent generation method.

[0863] Output: Scent generated based on the adjusted generation pattern

[0864] Step 8:

[0865] The fragrance generating means generates a fragrance based on the adjusted generation pattern and spreads it throughout the physical store.

[0866] Input: Adjusted generation pattern

[0867] Data calculation: Compounds are mixed according to a coordinated production pattern to generate a scent.

[0868] Output: Generated scent spreads throughout the physical store

[0869] Example 2

[0870] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0871] Conventional scent generation systems have difficulty generating scents that suit the user's living environment and time of day, and also have difficulty adjusting them to suit the user's preferences and circumstances. As a result, it has not been possible to provide the optimal scent desired by the user. Furthermore, there has been a demand for efficient and accurate methods for detailed scent identification and generation of scent generation patterns.

[0872] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0873] In this invention, the server includes a sensor means for performing detailed identification of scents, a means for inputting scent data identified by the sensor means into a generative AI model and for the generative AI model to generate a scent generation pattern based on the data, a means for a user to input information necessary for scent generation into a terminal, a means for the terminal to send the input information to the server, a means for the server to generate a prompt sentence and input the prompt sentence to the generative AI model, a means for the generative AI model to generate a scent pattern, a means for the server to provide the generated scent pattern to the user, and a scent generation means for generating a scent based on the generation pattern. This makes it possible to generate a scent according to the user's living environment and time of day, and also to adjust the scent according to the user's preferences and situation.

[0874] The "sensor means" is a device for performing detailed identification of scents.

[0875] A "generative AI model" is an artificial intelligence algorithm that generates a scent generation pattern based on input data.

[0876] A "terminal" is a device through which a user inputs information necessary for generating a fragrance.

[0877] The "server" is a computer system that receives information sent from the terminal, generates prompt sentences, and inputs them into the generative AI model.

[0878] A "prompt sentence" is an instruction sentence that instructs the generative AI model to generate a scent generation pattern.

[0879] The "fragrance generating means" is a device that actually generates a fragrance based on the generated fragrance pattern.

[0880] A "fragrance pattern" is a combination of ingredients and blending ratios that reproduces a specific fragrance.

[0881] A "user" is an individual or organization that uses the scent generation system.

[0882] This invention is a system that generates fragrances according to the user's living environment and time of day. The system includes a sensor means for detailed identification of fragrances, a generation AI model, a terminal, a server, a prompt sentence, and a fragrance generation means.

[0883] Hardware and software used

[0884] Hardware

[0885] Sensor means: A device for identifying scents in detail. For example, a gas sensor or an electronic nose is included.

[0886] Terminal: A device through which the user inputs the information necessary to generate the scent. This includes smartphones and tablets.

[0887] Server: A computer system that receives information sent from the device, generates prompts, and inputs them into the generative AI model.

[0888] Scent generation means: A device that actually generates a scent based on the generated scent pattern. For example, an aroma diffuser.

[0889] software

[0890] Generative AI model: An artificial intelligence algorithm that generates scent patterns based on input data. Examples include natural language processing models and machine learning algorithms.

[0891] Data processing and calculation

[0892] server

[0893] The server receives the user's input information sent from the device and generates a prompt based on that information. The generated prompt is input into the generative AI model. The generative AI model analyzes the prompt and generates an appropriate scent pattern. The generated scent pattern is provided to the user via the server.

[0894] Terminal

[0895] The terminal provides an interface for the user to input information necessary for generating the fragrance. The information input by the user is transmitted from the terminal to the server.

[0896] User

[0897] The user inputs the information necessary to generate the fragrance through the device. For example, if the user desires a fragrance to use in the morning, they input "fragrance to use in the morning" into the input field on the device. If the user desires a fragrance to use during relaxation time, they input "fragrance to use during relaxation time."

[0898] Specific examples

[0899] 1. If the user wants a scent to use in the morning:

[0900] The user enters "fragrance for morning use" into the terminal.

[0901] The terminal transmits the information to the server.

[0902] The server inputs the prompt statement "Generate a refreshing forest scent to use in the morning" into the generative AI model.

[0903] The generative AI model generates patterns of refreshing forest scents, and the server provides the results to the user.

[0904] 2. If the user wishes to use a fragrance for relaxation:

[0905] The user enters "scent to use during relaxation time" into the terminal.

[0906] The terminal transmits the information to the server.

[0907] The server inputs the prompt sentence "Generate a relaxing lavender scent to use during relaxation time" into the generative AI model.

[0908] The generative AI model generates patterns of relaxing lavender scents, and the server provides the results to the user.

[0909] Prompt Sentence Examples

[0910] 1. "Produce a refreshing forest scent for morning use."

[0911] 2. "Produce a relaxing lavender scent for use during relaxation time."

[0912] This allows users to easily create fragrances that suit their living environment and time of day.

[0913] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0914] Step 1:

[0915] The user enters information into the terminal

[0916] The user inputs the information necessary to generate the fragrance through the device's interface. For example, if the user desires a fragrance to be used in the morning, they input "fragrance to be used in the morning" into the device's input field. The input data includes information such as the type of fragrance and the time of day it will be used. The input information is temporarily stored in the device's internal memory.

[0917] Step 2:

[0918] The device sends the input information to the server

[0919] The device sends the information entered by the user to the server. Specifically, the device converts the user's input data into JSON format data and sends it to the server as an HTTP POST request. This request includes information such as the type of fragrance the user desires and the time of day it will be used. The server analyzes the received data and proceeds to the next processing step.

[0920] Step 3:

[0921] The server generates a prompt

[0922] The server generates a prompt based on the user's input information received from the terminal. For example, if the user requests a "scent to use in the morning," the server generates a prompt such as "Please generate a refreshing forest scent to use in the morning." The generated prompt is stored in the server's internal memory, and the server proceeds to the next processing step.

[0923] Step 4:

[0924] The server inputs a prompt to the generative AI model.

[0925] The server inputs the generated prompt sentence into the generative AI model. Specifically, the server sends a request including the generated prompt sentence to the API endpoint of the generative AI model. The generative AI model analyzes the prompt sentence and starts internal processing to generate an appropriate scent pattern.

[0926] Step 5:

[0927] Generative AI model generates scent patterns

[0928] The generative AI model generates a scent pattern based on a prompt sent from the server. For example, for a prompt such as "A refreshing forest scent for use in the morning," the generative AI model calculates the ingredients and blending ratios required to recreate the refreshing forest scent and returns the results to the server. The generated scent pattern is sent to the server as data including the ingredient list and blending ratios.

[0929] Step 6:

[0930] The server provides the generated scent pattern to the user.

[0931] The server receives the scent pattern returned by the generative AI model and provides it to the user. Specifically, the server sends the scent pattern to the device, which then displays the information to the user. The user can check the displayed scent pattern and input it into the device to generate the scent as needed.

[0932] This allows users to easily create fragrances that suit their living environment and time of day.

[0933] (Application example 2)

[0934] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0935] Conventional aroma generation systems have difficulty generating aromas that suit the user's living environment and time of day, and are unable to provide aromas that match the ordered food, particularly in food delivery services. For this reason, there is a demand for aroma generation technology that can further enrich users' dining experiences.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0937] In this invention, the server includes a sensor means for performing detailed identification of aromas, a means for inputting data on the aromas identified by the sensor means to a generation AI, which then generates an aroma generation pattern based on the data, an aroma generation means for generating an aroma based on the generation pattern, and a means for generating an aroma based on user order information. This makes it possible to provide aromas that match the dishes ordered by the user, further enriching the dining experience.

[0938] The "sensor means for detailed identification of scents" is a sensor device for detecting and identifying the components and characteristics of scents in detail.

[0939] "Generative AI" is an artificial intelligence model that generates scent generation patterns based on input data.

[0940] "Means for generating a scent generation pattern" refers to the function by which the generation AI creates a pattern for generating a specific scent based on scent data.

[0941] The "scent generating means" is a device or system that actually generates a scent based on the generated scent pattern.

[0942] The "means for generating aroma based on user order information" is a function that generates an appropriate aroma based on information such as the dish ordered by the user and the time of day.

[0943] A system for implementing this invention includes a sensor means for performing detailed scent identification, a generative AI model, a scent generation means, and a means for generating a scent based on user order information. Specific embodiments of the system are described below.

[0944] System configuration

[0945] 1. Sensor means for detailed scent identification:

[0946] The sensor means is a device for detecting and identifying the components and characteristics of a scent in detail, such as a gas sensor or an electronic nose.

[0947] 2. Generative AI Model:

[0948] A generative AI model is an artificial intelligence model that generates scent patterns based on input scent data. Specifically, it uses natural language processing models such as GPT-3.

[0949] 3. Scent generating means:

[0950] The scent generating means is a device or system that actually generates a scent based on the generated scent pattern, such as a scent diffuser or aroma diffuser.

[0951] 4. Means for generating scents based on user order information:

[0952] This method has the function of generating an appropriate scent based on information such as the dish ordered by the user and the time of day. For example, it works in conjunction with a food delivery application.

[0953] Program processing explanation

[0954] The server first acquires scent data from a sensor means for detailed scent identification. This data is input into a generative AI model, which generates a scent generation pattern. The generated pattern is then sent to the scent generation means, which generates the actual scent.

[0955] Furthermore, the system obtains the user's order information (e.g., type of food and time of day), and the generative AI model adjusts the aroma generation pattern based on this information, thereby providing aromas that match the food ordered by the user.

[0956] Specific examples

[0957] For example, if a user orders Italian food for breakfast, the server will input the following prompt into the generative AI model:

[0958] "If the user orders Italian food for breakfast, generate the scent of basil."

[0959] Based on this prompt sentence, the generative AI model creates a pattern for generating the scent of basil and sends it to the scent generation means, which then generates the scent of basil based on this pattern and provides it to the user.

[0960] As described above, the present invention can generate an appropriate aroma according to the user's order information and time of day, thereby enriching the dining experience.

[0961] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0962] Step 1:

[0963] The server acquires scent data from a sensor means for detailed scent identification. The sensor means detects the scent components and characteristics and sends the data to the server. The input is scent component data, and the output is the scent data sent to the server.

[0964] Step 2:

[0965] The server inputs the acquired scent data into the generative AI model, which then generates a scent generation pattern based on this data. The input is the scent data, and the output is the generated scent pattern.

[0966] Step 3:

[0967] The server transmits the generated scent pattern to the scent generation means, which generates the actual scent based on this pattern. The input is the scent generation pattern, and the output is the generated scent.

[0968] Step 4:

[0969] A user orders food through a food delivery application. The terminal sends the user's order information (type of food and time slot) to the server. The input is the user's order information, and the output is the order information sent to the server.

[0970] Step 5:

[0971] The server inputs a prompt sentence to the generative AI model based on the user's order information. For example, the prompt sentence "If the user orders Italian food for breakfast, please generate the scent of basil." The input is the prompt sentence, and the output is the generated scent pattern.

[0972] Step 6:

[0973] The server sends the scent pattern obtained from the generative AI model to the scent generation means. The scent generation means generates an appropriate scent based on this pattern and provides it to the user. The input is the scent generation pattern, and the output is the scent provided to the user.

[0974] Example 3

[0975] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0976] Conventional scent generation systems have difficulty generating scents that suit the user's preferences and circumstances, making it impossible to provide scents optimized for each individual user. It is also difficult to generate scents that take into account time and space constraints. This has limited the utility of scent generation systems, making it difficult to increase user satisfaction.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0978] In this invention, the server includes means for collecting information on the user's preferences and circumstances, means for generating prompt sentences to be input to the generative AI model based on the collected information, and means for inputting the generated prompt sentences to the generative AI model and calculating a scent generation pattern, thereby enabling the generation of scents according to the user's preferences and circumstances.

[0979] "Information about the user's preferences and situation" refers to individual information such as the user's preferred scent, current physical condition, and environment.

[0980] A "generative AI model" refers to an artificial intelligence model that calculates scent generation patterns based on input data.

[0981] A "prompt" is an instruction to be input into a generative AI model, and refers to text generated based on the user's preferences and situation.

[0982] "Scent production pattern" refers to the specific steps and combination of ingredients used to produce a particular scent.

[0983] The term "fragrance generating means" refers to a device or mechanism for actually generating a fragrance based on the generated fragrance generation pattern.

[0984] The present invention relates to a system for generating a fragrance according to a user's preferences and circumstances. Specific embodiments of this system will be described below.

[0985] Server Roles

[0986] The server collects information about the user's preferences and circumstances. Specifically, it uses information the user enters into the application and health data obtained from the wearable device. For example, if the user enters "I like the smell of coffee" into the application, that information is stored in a database. It may also obtain information from the wearable device that "the user has a cold."

[0987] Next, the server generates a prompt sentence to be input into the generative AI model based on the collected information. For example, if the information indicates that the user likes the smell of coffee, the server generates a prompt sentence such as, "The user likes the smell of coffee, so please generate a coffee smell." If the user has a cold, the server generates a prompt sentence such as, "The user has a cold, so please generate a eucalyptus scent that will relieve cold symptoms."

[0988] The generated prompt text is sent to the device using an HTTP request, for example, by packaging the generated prompt text in JSON format and sending it to the device's API endpoint.

[0989] Device Role

[0990] The terminal receives the prompt sentence sent from the server. Specifically, the terminal receives the HTTP request and analyzes the prompt sentence. For example, it analyzes the received JSON data and extracts the prompt sentence.

[0991] Next, the device inputs the received prompt into a generative AI model to calculate the aroma generation pattern. Specifically, the prompt is input into a generative AI model (e.g., OpenAI's GPT-3), and the generated text is analyzed to extract the aroma generation pattern. For example, in response to the prompt "Please generate the aroma of coffee," the generative AI model generates the text "The components of coffee aroma are caffeine, chlorogenic acid, and pyrrolidine."

[0992] The calculated scent generation pattern is again packaged in JSON format and sent to the API endpoint of the scent generation device. For example, the generation pattern is sent in the format "{ 'ingredients': ['caffeine', 'chlorogenic acid', 'pyrrolidine']}".

[0993] User Roles

[0994] The user receives the scent provided by the scent generating device. Specifically, the scent generating device generates a scent based on the received scent generation pattern and provides it to the user. For example, a smart diffuser mixes the ingredients "caffeine, chlorogenic acid, and pyrrolidine" to generate a coffee scent and release it to the user.

[0995] Specific examples

[0996] Example 1: If the user likes the smell of coffee

[0997] 1. The server collects information that "the user likes the smell of coffee."

[0998] 2. The server sends the prompt "Generate the aroma of coffee" to the generative AI model.

[0999] 3. The device uses the generative AI model to calculate the coffee aroma generation pattern.

[1000] 4. The device sends a pattern to the smart diffuser to create the coffee aroma.

[1001] 5. The user receives the aroma of coffee from the smart diffuser.

[1002] Example 2: If the user has a cold

[1003] 1. The server collects information that "the user has a cold."

[1004] 2. The server sends the prompt to the generative AI model: "Generate a eucalyptus scent that will relieve cold symptoms."

[1005] 3. The device uses a generative AI model to calculate the eucalyptus scent generation pattern.

[1006] 4. The device sends a pattern to the smart diffuser to generate a eucalyptus scent.

[1007] 5. The user receives the eucalyptus scent from the smart diffuser.

[1008] Prompt Sentence Examples

[1009] "Users like the smell of coffee, so generate a coffee smell."

[1010] "The user has a cold, so generate a eucalyptus scent to soothe cold symptoms."

[1011] In this way, a system can be realized that can generate a fragrance according to the user's preferences and circumstances. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1012] Step 1:

[1013] The server collects information about the user's preferences and circumstances. Specifically, it uses information the user enters into the application and health data obtained from the wearable device. For example, if the user enters "I like the smell of coffee," that information is stored in a database. It may also obtain information from the wearable device that "the user has a cold."

[1014] Input: User application input data, health data from wearable devices

[1015] Output: Information data about the user's preferences and situation

[1016] Step 2:

[1017] The server generates prompts to be input into the generative AI model based on the collected information. Specifically, it uses a natural language processing algorithm to create prompts that match the user's preferences and the situation. For example, it generates a prompt such as, "The user likes the smell of coffee, so please generate the smell of coffee."

[1018] Input: User preferences and contextual information data

[1019] Output: Prompt sentence to be input to the generative AI model

[1020] Step 3:

[1021] The server sends the generated prompt text to the device. Specifically, it sends the prompt text to the device using an HTTP POST request. For example, it packages the generated prompt text in JSON format and sends it to the device's API endpoint.

[1022] Input: A prompt to be input to the generative AI model

[1023] Output: Prompt sent to the terminal

[1024] Step 4:

[1025] The terminal receives the prompt sentence sent from the server. Specifically, the terminal receives the HTTP request and analyzes the prompt sentence. For example, it analyzes the received JSON data and extracts the prompt sentence.

[1026] Input: Prompt sent from the server

[1027] Output: Parsed prompt statement

[1028] Step 5:

[1029] The device inputs the received prompt into a generative AI model and calculates the aroma generation pattern. Specifically, the prompt is input into a generative AI model (for example, OpenAI's GPT-3), and the generated text is analyzed to extract the aroma generation pattern. For example, in response to the prompt "Please generate the aroma of coffee," the generative AI model generates the text "The components of coffee aroma are caffeine, chlorogenic acid, and pyrrolidine."

[1030] Input: Parsed prompt statement

[1031] Output: Scent generation pattern

[1032] Step 6:

[1033] The terminal sends the calculated scent generation pattern to the scent generation device. Specifically, the generation pattern is packaged in JSON format and sent to the scent generation device's API endpoint. For example, the generation pattern is sent in the format "{ 'ingredients': ['caffeine', 'chlorogenic acid', 'pyrrolidine']}".

[1034] Input: scent generation pattern

[1035] Output: Generated pattern sent to the scent generating device

[1036] Step 7:

[1037] The user receives the scent provided by the scent generating device. Specifically, the scent generating device generates a scent based on the received scent generation pattern and provides it to the user. For example, a smart diffuser mixes the ingredients "caffeine, chlorogenic acid, and pyrrolidine" to generate a coffee scent and release it to the user.

[1038] Input: Generated pattern sent to the scent generating device

[1039] Output: The scent provided to the user

[1040] (Application example 3)

[1041] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1042] Conventional scent generation systems have difficulty generating scents that suit user preferences and situations, and have not been able to provide optimal scents to improve customer experience in physical stores. Therefore, there is a need for an effective scent generation system that can increase customer satisfaction.

[1043] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1044] In this invention, the server includes a sensor means for performing detailed identification of the scent, a means for inputting scent data identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, a means for adjusting the scent generation pattern according to the user's preferences and circumstances, and a means for generating and providing an optimal scent in a physical store based on the customer's preferences and circumstances. This makes it possible to provide an optimal scent in a physical store according to the customer's preferences and circumstances.

[1045] The "sensor means for detailed identification of scents" is a sensor device for detecting and identifying the components and characteristics of scents in detail.

[1046] "Generative AI" is an artificial intelligence system that generates scent generation patterns based on input data.

[1047] A "scent generation pattern" is a data set that shows instructions and steps for generating a specific scent.

[1048] The "fragrance generation means" is a device that actually generates a fragrance based on the fragrance generation pattern generated by the generation AI.

[1049] A "means for adjusting the scent generation pattern according to the user's preferences and circumstances" is a device or system that has the function of dynamically changing the scent generation pattern based on the user's individual preferences and current circumstances.

[1050] "Means for generating and providing optimal fragrances based on customer preferences and circumstances within a physical store" refers to a device or system that detects customer preferences and circumstances within a physical store and generates and provides optimal fragrances based on those preferences and circumstances.

[1051] A "smartphone" is a multi-functional mobile device that can connect to the Internet and run applications in addition to the functions of a mobile phone.

[1052] "Smart glasses" are glasses-type devices that have the ability to display visual information, connect to the Internet, and run applications.

[1053] A "head-mounted display" is a display device worn on the head to display visual information.

[1054] A system for implementing this invention includes a sensor means for performing detailed scent identification, a generation AI, a scent generation means, a means for adjusting the scent generation pattern according to the user's preferences and circumstances, and a means for generating and providing the optimal scent based on the customer's preferences and circumstances within a physical store.

[1055] System program generation

[1056] The server receives data from the sensor means for detailed scent identification and inputs that data into the generation AI. The generation AI generates a scent generation pattern based on the input data. The generated pattern is sent to the scent generation means, which actually generates the scent.

[1057] Program processing explanation

[1058] The server uses sensor means (e.g., gas sensors or electronic noses) to identify the scent in detail and detect the scent's components and characteristics in detail. This data is input into the generation AI. The generation AI uses software such as Python or TensorFlow to generate a scent generation pattern based on the input data. This generation pattern is sent to the scent generation means (e.g., an aroma diffuser), which actually generates the scent.

[1059] The means to adjust the scent generation pattern according to the user's preferences and circumstances is realized through applications installed on devices such as smartphones, smart glasses, and head-mounted displays. These devices detect the user's preferences and current circumstances and send that information to a server. The server then provides prompts to the generation AI based on this information and generates the optimal scent generation pattern.

[1060] Specific examples

[1061] For example, when a user enters a coffee shop, the smartphone app automatically launches and detects the user's preferences and current situation. If the user likes the smell of coffee, the AI ​​will prioritize generating a coffee aroma. If the user has a cold, it will generate a eucalyptus scent.

[1062] Prompt Sentence Examples

[1063] "The user likes coffee and vanilla and is currently healthy. Use this information to generate the optimal scent."

[1064] In this way, it becomes possible to provide optimal scents in physical stores according to customer preferences and circumstances.

[1065] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1066] Step 1:

[1067] The server receives data from a sensor means for detailed scent identification. The sensor means uses a gas sensor, electronic nose, or the like to detect the components and characteristics of the surrounding scent in detail. The input data is scent component information and concentration information. The server receives this data and passes it on to the next processing step.

[1068] Step 2:

[1069] The server inputs the received scent data into the generation AI. The generation AI uses software such as Python and TensorFlow to generate a scent generation pattern based on the input data. Specifically, the generation AI analyzes the scent component information and determines the optimal scent combination and concentration. The output is a scent generation pattern.

[1070] Step 3:

[1071] The server sends the scent generation pattern generated by the generation AI to the scent generation means. The scent generation means is a device such as an aroma diffuser, which actually generates the scent based on the received generation pattern. Specifically, the scent generation means mixes the specified ingredients at the appropriate concentration and releases the scent. The output is the generated scent.

[1072] Step 4:

[1073] The device (smartphone, smart glasses, head-mounted display) detects the user's preferences and situation. The device collects user input and sensor data (e.g., body temperature and heart rate) to determine the user's current state. The input data is the user's preference information and health status information. The device transmits this information to a server.

[1074] Step 5:

[1075] The server provides prompts to the generation AI based on information about the user's preferences and situation received from the device. The generation AI adjusts the scent generation pattern based on these prompts. Specifically, the generation AI takes into account the user's preferences and health condition and recalculates the optimal scent combination and concentration. The output is an adjusted scent generation pattern.

[1076] Step 6:

[1077] The server transmits the adjusted scent generation pattern to the scent generation means again. The scent generation means regenerates the scent based on the received generation pattern. Specifically, the scent generation means mixes the recalculated ingredients at appropriate concentrations and releases the scent. The output is the adjusted scent.

[1078] Step 7:

[1079] Users can experience the scents generated in the physical store. By smelling the scents generated, users can experience effects such as relaxation and improved concentration. Specifically, users can enjoy the changing scents as they walk around the store.

[1080] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1081] "Example 1"

[1082] In one embodiment of the present invention, an emotion engine recognizes a user's emotion and provides that information to a generation AI. The generation AI generates a scent generation pattern that takes this emotion information into account. For example, if the emotion engine recognizes that the user is feeling joy, the generation AI generates a scent generation pattern that amplifies that joy, such as a citrus scent.

[1083] "Example 2"

[1084] The generation AI also generates a scent generation pattern that takes into account the user's emotions recognized by the emotion engine as well as time and space constraints. For example, if the emotion engine recognizes that the user wants to relax at night, the generation AI will generate a scent generation pattern for lavender, which has a relaxing effect, taking into account the user's desire to relax and the time constraints of nighttime.

[1085] "Example 3"

[1086] Furthermore, the generation AI adjusts the scent generation pattern taking into account the user's emotions recognized by the emotion engine, as well as the user's preferences and situation. For example, if the emotion engine recognizes that the user is feeling sad and there is information that the user likes the scent of roses, the generation AI will generate a rose scent generation pattern that alleviates the sadness and at the same time satisfies the user's preferences.

[1087] The processing flow of each embodiment will be described below.

[1088] "Example 1"

[1089] Step 1: The emotion engine recognizes the user's emotion.

[1090] Step 2: The emotion engine provides the recognized emotion information to the generation AI.

[1091] Step 3: The generation AI generates a scent generation pattern taking into account the provided emotional information.

[1092] Step 4: Generate a scent based on the scent generation pattern generated by the generation AI.

[1093] "Example 2"

[1094] Step 1: The emotion engine recognizes the user's emotions and time and space constraints.

[1095] Step 2: The emotion engine provides the recognized emotions and time and space constraint information to the generation AI.

[1096] Step 3: The generation AI generates a scent generation pattern taking into account the provided information. Step 4: The generation AI generates a scent based on the scent generation pattern generated.

[1097] "Example 3"

[1098] Step 1: The emotion engine recognizes the user's emotions, preferences, and situation.

[1099] Step 2: The emotion engine provides the recognized information to the generation AI.

[1100] Step 3: The generation AI adjusts the scent generation pattern taking into account the provided information. Step 4: The generation AI generates a scent based on the adjusted scent generation pattern.

[1101] Example 1

[1102] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1103] Conventional scent generation systems have difficulty identifying scent components in detail and generating scents that match the user's emotions and preferences. Furthermore, they have not been able to generate scents that reflect the user's emotional state in real time. This makes it impossible to optimize the user experience and maximize the effects of the scent.

[1104] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1105] In this invention, the server includes a sensor means for performing detailed scent identification, a means for inputting scent data identified by the sensor means into a generation AI and having the generation AI generate a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, an emotion engine for recognizing a user's emotion, and a means for providing emotion information recognized by the emotion engine to the generation AI and having the generation AI generate a scent generation pattern taking the emotion information into consideration. This makes it possible to generate a scent that reflects the user's emotional state in real time, optimizing the user experience and maximizing the effect of the scent.

[1106] The "sensor means" is a device for identifying the scent in detail, and analyzes compounds in the air to identify the scent components.

[1107] "Generative AI" is an artificial intelligence that uses machine learning technology to learn and generate scent generation patterns from scent data.

[1108] The "fragrance generation means" is a device that mixes the necessary compounds to generate a fragrance based on the generation pattern output from the generation AI.

[1109] An "emotion engine" is a device or software that recognizes a user's emotions, analyzing the user's facial expressions and tone of voice through sensor devices such as cameras and microphones.

[1110] "Scent generation patterns" refer to the combinations and blending ratios of compounds that create a specific scent, generated by the generation AI based on scent data and emotional information.

[1111] "Emotion information" is data that indicates the user's emotional state, obtained by the emotion engine by analyzing the user's facial expression, tone of voice, and the like.

[1112] MODE FOR CARRYING OUT THE INVENTION

[1113] This invention provides an IoT device equipped with a sensor means for detailed scent identification, a scent generation AI, and a scent generation means. Specific embodiments of this system are described below.

[1114] Scent discrimination by sensor means

[1115] The server uses gas chromatography to analyze compounds in the air and identify the scent components in detail. The gas chromatography device separates and detects each compound by passing the air sample through the device. The data on the detected compounds is sent to the server.

[1116] Inputting scent data into AI

[1117] The server inputs the identified fragrance component data into the generative AI. Specifically, the server retrieves the fragrance component data stored in the database and converts it into the input format of the generative AI. This data is then fed into the generative AI model.

[1118] Learning and generating scent generation patterns

[1119] The server uses generative AI to learn and generate scent generation patterns. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to train a model based on scent component data. The trained model then outputs new scent generation patterns.

[1120] Generation of fragrance by fragrance generating means

[1121] The server controls the scent generation means based on the generation pattern output by the generation AI. Specifically, the server operates a precision chemical mixing device to mix the required compounds in the correct proportions, thereby generating the specified scent.

[1122] User Emotion Recognition

[1123] The device uses an emotion engine to recognize the user's emotions. Specifically, the device captures the user's facial expressions and tone of voice through a camera and microphone, and the emotion engine analyzes this data. As a result of the analysis, the user's emotional state is identified.

[1124] Providing emotional information to AI

[1125] The device provides the recognized emotion information to the generative AI. Specifically, the device sends the emotion data obtained from the emotion engine to the server and uses it as input data for the generative AI. This data is fed into the generative AI model.

[1126] Emotion-based scent generation pattern generation

[1127] The server generates a scent generation pattern that takes emotional information into account. Specifically, the generation AI receives emotional data as input and outputs a scent generation pattern that is appropriate for the user's emotional state. For example, if the user feels like relaxing, the generation AI will generate a scent generation pattern that has a relaxing effect, such as lavender or chamomile.

[1128] Examples and prompts

[1129] Specific examples

[1130] If the user feels like relaxing, the device uses a camera and microphone to capture the user's facial expressions and tone of voice. The emotion engine analyzes this data and recognizes that the user wants to relax. The device then sends this emotional information to the server, which inputs it into the generation AI. The generation AI outputs a pattern for generating a scent that has a relaxing effect, and the server controls the scent generation means based on this pattern. Ultimately, a lavender or chamomile scent is generated and provided to the user.

[1131] Prompt Sentence Examples

[1132] "If the user feels like they want to relax, what kind of scent generation pattern will the generative AI generate?"

[1133] By inputting this prompt into the generative AI model, a specific answer can be obtained regarding the pattern of generating a relaxing scent.

[1134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1135] Step 1:

[1136] The server uses gas chromatography to analyze compounds in the air and identify the scent components in detail. Specifically, the server controls the gas chromatography device, which separates and detects each compound by passing the air sample through the device. It receives the air sample as input and obtains data on each compound as output. This data is sent to the server.

[1137] Step 2:

[1138] The server inputs the identified fragrance component data into the generative AI. Specifically, the server retrieves the fragrance component data stored in the database and converts it into the input format of the generative AI. It receives the fragrance component data as input and generates data in a format suitable for the generative AI as output. This data is fed into the generative AI model.

[1139] Step 3:

[1140] The server uses generative AI to learn and generate scent generation patterns. Specifically, the server uses machine learning frameworks such as TensorFlow and PyTorch to train a model based on scent component data. It receives scent component data as input and obtains scent generation patterns as output. The trained model then outputs new scent generation patterns.

[1141] Step 4:

[1142] The server controls the scent generation means based on the generation pattern output by the generation AI. Specifically, the server operates a precision chemical mixing device to mix the required compounds in the correct proportions. It receives the generation pattern as input and generates the specified scent as output.

[1143] Step 5:

[1144] The device uses an emotion engine to recognize the user's emotions. Specifically, the device captures the user's facial expressions and tone of voice through a camera and microphone, and the emotion engine analyzes this data. It receives the user's facial and voice data as input and identifies the user's emotional state as output.

[1145] Step 6:

[1146] The device provides the recognized emotion information to the generative AI. Specifically, the device sends the emotion data obtained from the emotion engine to the server, which uses it as input data for the generative AI. It receives emotion data as input and generates data in a format suitable for the generative AI as output. This data is fed into the generative AI model.

[1147] Step 7:

[1148] The server generates a scent generation pattern taking emotional information into consideration. Specifically, the generation AI receives emotional data as input and outputs a scent generation pattern appropriate for the user's emotional state. It receives emotional data as input and obtains an emotion-based scent generation pattern as output. For example, if the user feels like relaxing, the generation AI generates a scent generation pattern that has a relaxing effect, such as lavender or chamomile.

[1149] (Application example 1)

[1150] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1151] Conventional scent generation systems have difficulty generating scents that correspond to the user's emotions and circumstances, and have not provided enough scents to optimize the user experience, especially in physical stores. In addition, there is a lack of technology to generate scents based on the user's emotions, making it difficult to provide personalized scents to individual users.

[1152] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes sensor means for performing detailed identification of scents, means for inputting scent data identified by the sensor means to the generation AI and causing the generation AI to generate a scent generation pattern based on the data, scent generation means for generating a scent based on the generation pattern, emotion recognition means for recognizing the user's emotion, and means for providing emotion information recognized by the emotion recognition means to the generation AI and causing the generation AI to generate a scent generation pattern taking the emotion information into consideration. This makes it possible to generate a personalized scent according to the user's emotion and situation.

[1153] "Detailed scent identification" involves analyzing compounds in the air using techniques such as gas chromatography to identify the scent components in detail.

[1154] The "sensor means" refers to a device or equipment for performing detailed identification of scents.

[1155] "Generative AI" is an artificial intelligence that uses machine learning technology to learn and generate scent generation patterns from scent data.

[1156] "Fragrance generation means" refers to a device or equipment that generates a fragrance by mixing the necessary compounds based on the generation pattern output from the generation AI.

[1157] An "emotion recognition means" is a device or equipment that recognizes the user's emotions and provides that information to the generating AI.

[1158] A "generated pattern" is a pattern of fragrance composition and blending that the generative AI generates based on fragrance data and emotional information.

[1159] "User emotion" refers to the emotional state that the user is feeling, such as happiness, sadness, or relaxation.

[1160] The system for implementing this invention includes a sensor means for detailed scent identification, a generation AI, a scent generation means, and an emotion recognition means. The specific configuration and operation of each means will be described below.

[1161] Sensor Means

[1162] The sensor means is a device that uses techniques such as gas chromatography to analyze compounds in the air and identify the fragrance components in detail. This sensor means is installed in a physical store and samples the surrounding air to obtain fragrance component data.

[1163] Generation AI

[1164] The generation AI is an artificial intelligence that uses machine learning technology to learn and generate scent generation patterns from scent data. The scent component data acquired from the sensor means is input into the generation AI. The generation AI generates a scent generation pattern based on this data.

[1165] Fragrance generating means

[1166] The scent generation means is a device that generates a scent by mixing the necessary compounds based on the generation pattern output by the generation AI. This device is installed in a physical store and diffuses the generated scent throughout the store.

[1167] emotion recognition means

[1168] The emotion recognition means is a device that recognizes the user's emotions and provides that information to the generation AI. When the user inputs their emotions via their smartphone, the information is analyzed by the emotion recognition means and sent to the generation AI.

[1169] System Operation

[1170] 1. Acquiring data on fragrance components: Sensors installed in physical stores analyze compounds in the air and acquire data on fragrance components.

[1171] 2. Generation of the generation pattern: The acquired fragrance component data is input into the generation AI, which then generates a fragrance generation pattern.

[1172] 3. Acquiring emotional information: The user inputs their emotions through their smartphone, and the information is analyzed by the emotion recognition means.

[1173] 4. Scent generation: The generation AI adjusts the scent generation pattern taking into account emotional information, and the scent generation means generates the scent based on that pattern.

[1174] 5. Scent diffusion: The generated scent is diffused within the physical store to optimize the user experience.

[1175] Specific examples

[1176] For example, if a user feels like relaxing in a store, the emotion recognition means recognizes that emotion, and the generation AI creates a pattern for generating a lavender scent. The scent generation means generates a lavender scent based on that pattern and diffuses it throughout the store.

[1177] Prompt Sentence Examples

[1178] User ID: 12345

[1179] Emotion: Relaxed

[1180] Generative AI: Creating a lavender scent generation pattern

[1181] Fragrance generation method: Generates lavender scent and spreads it throughout the store

[1182] In this way, a scent can be generated in a physical store that corresponds to the user's emotions, providing an optimal atmosphere.

[1183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1184] Step 1:

[1185] The sensor means analyzes compounds in the air and obtains data on the components of the scent. The sensor means uses gas chromatography to identify the compounds in the air in detail and sends that data to the generation AI. The input is the compounds in the air, and the output is data on the components of the scent.

[1186] Step 2:

[1187] The generation AI generates a scent generation pattern based on the scent component data received from the sensor means. The generation AI uses machine learning technology to analyze the input scent component data and output the optimal scent generation pattern. The input is scent component data, and the output is a scent generation pattern.

[1188] Step 3:

[1189] The user inputs their emotions through a smartphone. The user inputs their emotional state using a dedicated application. The input is the user's emotional information, and the output is emotional data sent to the emotion recognition means.

[1190] Step 4:

[1191] The emotion recognition means analyzes the user's emotional information and provides that information to the generation AI. The emotion recognition means analyzes the input emotional information and sends it to the generation AI. The input is the user's emotional information, and the output is analyzed emotional data.

[1192] Step 5:

[1193] The generation AI adjusts the scent generation pattern taking into account emotional information. The generation AI integrates scent component data from the sensor means and emotional data from the emotion recognition means to regenerate the optimal scent generation pattern. The input is scent component data and emotional data, and the output is the adjusted scent generation pattern.

[1194] Step 6:

[1195] The scent generation means generates a scent based on the generation pattern received from the generation AI. The scent generation means mixes the necessary compounds and diffuses the generated scent throughout the physical store. The input is the adjusted scent generation pattern, and the output is the generated scent.

[1196] Step 7:

[1197] The generated scent is diffused throughout the physical store to optimize the user experience. The scent generation means diffuses the generated scent throughout the store, and the user experiences the scent. The input is the generated scent, and the output is the scent diffused throughout the store.

[1198] In this way, a scent can be generated that matches the user's emotions, providing an optimal atmosphere within the physical store.

[1199] Example 2

[1200] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1201] Conventional scent generation systems generate scents without considering the user's emotional state or time and space constraints, and therefore are unable to fully meet the user's needs. Furthermore, because scents cannot be generated based on the user's emotional state, it is difficult to achieve specific effects such as relaxation or refreshment. This has led to the issue of reduced user satisfaction.

[1202] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1203] In this invention, the server includes sensor means for performing detailed identification of scents, means for inputting scent data identified by the sensor means to a generation AI and having the generation AI generate a scent generation pattern based on the data, scent generation means for generating a scent based on the generation pattern, emotion engine means for analyzing a user's emotional state, and means for inputting the emotional state analyzed by the emotion engine means to the generation AI and having the generation AI generate a scent generation pattern taking the emotional state and time and space constraints into consideration. This makes it possible to generate a scent that takes the user's emotional state and time and space constraints into consideration.

[1204] The "sensor means for detailed scent identification" is a device for detecting scent components and intensity and outputting the results as data.

[1205] "Generative AI" is an artificial intelligence model that creates scent generation patterns based on input data.

[1206] An "aroma generation pattern" is data that includes instructions and settings for generating a specific aroma.

[1207] The "fragrance generating means" is a device that actually emits a fragrance based on the generated fragrance pattern.

[1208] The "emotion engine means" is a device or software for analyzing the user's emotional state and outputting the results as data.

[1209] "Time and space constraints" refer to the time and location conditions that must be taken into consideration when creating a scent.

[1210] The "user's emotional state" refers to the emotional and mood state that the user is currently experiencing.

[1211] MODE FOR CARRYING OUT THE INVENTION

[1212] The present invention relates to a system for generating a scent taking into consideration the emotional state of a user and time and space constraints. Specific embodiments of this system will be described below.

[1213] System configuration

[1214] The system consists of the following main components:

[1215] 1. Sensor means: A device for detailed identification of scents. This sensor means detects the components and strength of the scent and outputs it as data.

[1216] 2. Generative AI: An artificial intelligence model that creates a scent generation pattern based on input data. Generative AI generates a scent generation pattern taking into account the user's emotional state and time and space constraints.

[1217] 3. Scent generating means: A device that actually emits the scent based on the generated scent pattern.

[1218] 4. Emotion engine means: A device or software that analyzes the user's emotional state and outputs the results as data.

[1219] Program processing

[1220] The server inputs scent data acquired from the sensor means into the generation AI. The generation AI creates a scent generation pattern based on the data. Furthermore, the server uses the emotion engine means to analyze the user's emotional state and inputs the results into the generation AI. The generation AI generates a scent generation pattern taking into account the user's emotional state and time and space constraints.

[1221] The generated scent pattern is sent from the server to the scent generator, which then emits the scent, providing the optimal scent according to the user's emotional state and time and space constraints.

[1222] Specific examples

[1223] For example, consider a case where a user feels like they want to refresh themselves in the morning. The user opens a dedicated app and enters "I want to refresh myself in the morning." The device sends this request to the server. The server uses the emotion engine means to analyze the user's desire to refresh themselves. Next, the server uses the generation AI to create a generation pattern for a refreshing forest scent, taking into account the time of day (morning) and the feeling of wanting to refresh themselves. The server sends this generation pattern to the scent generation means, which then uses a built-in diffuser to emit a refreshing forest scent.

[1224] Prompt Sentence Examples

[1225] If the user is feeling refreshed in the morning, create a fresh forest scent.

[1226] In this way, the server and the terminal can work together to provide the optimal scent according to the user's living environment, time of day, and emotional state.

[1227] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1228] Step 1:

[1229] The user operates the device to request the generation of a scent.

[1230] The user opens the app on a smartphone, tablet, or other device and enters, "I want to refresh myself in the morning." This input is sent to the device.

[1231] Step 2:

[1232] The terminal sends the user's request to the server.

[1233] The device sends the user's request to the server. This request includes information such as the user's emotional state, time of day, and location. The input is the user's request data, and the output is the request sent to the server.

[1234] Step 3:

[1235] The server uses an emotion engine to analyze the user's emotions.

[1236] The server uses an emotion engine to analyze the user's emotional state. The emotion engine determines the user's current emotional state based on the user's input and past data. The input is the user's request data, and the output is the analyzed emotional state data.

[1237] Step 4:

[1238] The server uses a generative AI model to create a scent generation pattern.

[1239] The server uses the generative AI model to create a scent generation pattern that takes into account the user's emotional state and time and space constraints. For example, if the user feels like they want to refresh themselves in the morning, the generative AI model will create a pattern for generating a refreshing forest scent. The input is the analyzed emotional state data and time and space constraint information, and the output is a scent generation pattern.

[1240] Step 5:

[1241] The server sends the generated scent pattern to the terminal.

[1242] The server sends the generated scent pattern to the device. This pattern includes information such as the type of scent, its intensity, and its duration. The input is the scent generation pattern, and the output is the pattern sent to the device.

[1243] Step 6:

[1244] The device receives the scent pattern and generates the scent.

[1245] The device actually generates the scent based on the scent pattern received from the server. For example, a diffuser built into the device emits a refreshing forest scent. The input is the scent generation pattern, and the output is the generated scent.

[1246] (Application example 2)

[1247] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1248] Conventional scent generation systems have difficulty adjusting scents according to the time of day and the user's emotions, making it impossible to provide the optimal scent for a specific situation. Furthermore, scents have not been effectively generated to increase customer purchasing motivation in physical stores. This has resulted in insufficient improvements to customer experience and sales growth.

[1249] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1250] In this invention, the server includes a sensor means for performing detailed identification of scents, a means for inputting scent data identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, an emotion recognition means for recognizing customer emotions, and a time recognition means for adjusting the scent generation pattern according to the time of day. This allows for the generation of an optimal scent according to the time of day and the customer's emotions, improving the customer experience in physical stores and increasing purchasing motivation.

[1251] The "sensor means for detailed identification of scents" is a device for detecting the components and intensity of scents and identifying them as detailed data.

[1252] "Generative AI" is an artificial intelligence system that creates scent generation patterns based on input data.

[1253] The "fragrance generation means" is a device that actually generates a fragrance based on the fragrance generation pattern created by the generation AI.

[1254] An "emotion recognition means" is a device or system for analyzing a customer's facial expressions and behavior and identifying their emotional state.

[1255] The "time recognition means" is a device or system that recognizes the current time and adjusts the scent generation pattern based on that information.

[1256] The system for implementing this invention includes a sensor means for detailed scent identification, a generation AI, a scent generation means, an emotion recognition means, and a time recognition means. The specific operation of each means and their cooperation will be explained below.

[1257] First, sensors are installed in the store to identify the fragrance components and intensity in detail. This generates fragrance data, which is then input into the generation AI.

[1258] The generation AI creates a scent generation pattern based on the input scent data. In doing so, the generation AI takes into account time information from the time recognition means and emotional information from the emotion recognition means. For example, if it is morning and the customer feels like relaxing, the generation AI will create a scent generation pattern that has a relaxing effect.

[1259] The scent generator actually generates the scent based on the scent generation pattern created by the generation AI. The generated scent is then diffused throughout the store to improve the customer experience.

[1260] The emotion recognition system uses in-store camera sensors to analyze customers' facial expressions and behaviors to identify their emotional state. For example, if a customer is feeling stressed, that information is sent to the generation AI.

[1261] The time recognition means recognizes the current time and provides that information to the generation AI, which then can adjust the scent generation pattern according to the time of day.

[1262] As a concrete example, the following prompt sentence is input to the generative AI model:

[1263] "The current time is 10:00 AM. What scent would you produce if your customer is stressed?"

[1264] "It's currently 6pm. What scent would you produce if your customer is feeling relaxed?"

[1265] This allows the generative AI model to provide an appropriate scent generation pattern.

[1266] This system generates the optimal scent according to the time of day and the customer's emotions, improving the customer experience in physical stores and increasing purchasing motivation.

[1267] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1268] Step 1:

[1269] The sensor means identifies the scent in the store in detail and generates data on its components and intensity.

[1270] Input: Scent inside the store

[1271] Output: Fragrance component data

[1272] Step 2:

[1273] The generation AI receives scent component data from the sensor means and creates a scent generation pattern taking into account time information from the time recognition means and emotion information from the emotion recognition means.

[1274] Input: Fragrance component data, time information, emotional information

[1275] Output: Scent generation pattern

[1276] Step 3:

[1277] The emotion recognition means uses camera sensors in the store to analyze the customer's facial expressions and behavior to identify their emotional state.

[1278] Input: Customer's facial expressions and behavior

[1279] Output: Emotional information

[1280] Step 4:

[1281] The time recognition means recognizes the current time and provides that information to the generation AI.

[1282] Input: current time

[1283] Output:Time information

[1284] Step 5:

[1285] The scent generation means actually generates the scent based on the scent generation pattern from the generation AI and spreads it throughout the store.

[1286] Input: scent generation pattern

[1287] Output: Generated scent

[1288] Step 6:

[1289] The generation AI uses prompt text to adjust the scent generation pattern. For example, by entering a prompt text such as "It is now 10:00 AM. What kind of scent would you generate if the customer is feeling stressed?", it will provide an appropriate scent generation pattern.

[1290] Input: prompt statement

[1291] Output: Adjusted scent generation pattern

[1292] In this way, the system generates the optimal scent according to the time of day and the customer's emotions, improving the customer experience in physical stores and increasing purchasing motivation.

[1293] Example 3

[1294] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1295] Conventional scent generation systems have difficulty generating scents that match the user's preferences and circumstances, making it impossible to provide the optimal scent for each individual user. Furthermore, the collection and analysis of information needed to adjust scent generation patterns was insufficient, making it difficult to provide scents that match the user's emotions and health status. This resulted in an inability to increase user satisfaction.

[1296] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1297] In this invention, the server includes means for inputting information about a user's preferences and situation, means for transmitting the information to the server, means for the server to analyze the information and generate a prompt sentence, means for inputting the prompt sentence into a generative AI model and generating a scent generation pattern, and means for transmitting the generation pattern to the terminal. This makes it possible to generate a scent according to the user's preferences and situation, and to provide the optimal scent for each individual user.

[1298] The "sensor means" is a device for performing detailed identification of scents.

[1299] A "generative AI model" is an artificial intelligence algorithm that generates a scent generation pattern based on input data.

[1300] The "fragrance generating means" is a device that actually generates a fragrance based on the generated fragrance generation pattern.

[1301] The "means for inputting information relating to the user's preferences and circumstances" is an interface that allows the user to input information relating to his or her preferences and circumstances into the system.

[1302] The "means for transmitting the information to the server" refers to a communication means for transmitting the information input by the user to the server.

[1303] "Means for the server to analyze the information and generate a prompt sentence" refers to the function by which the server analyzes the user's input information and generates a prompt sentence to be input into the generative AI model based on that information.

[1304] "Means for inputting a prompt sentence into a generative AI model and generating a scent generation pattern" refers to a function for inputting a prompt sentence into a generative AI model and generating a scent generation pattern based on that sentence.

[1305] The "means for transmitting the generation pattern to the terminal" refers to a communication means for transmitting the generated scent generation pattern to the terminal.

[1306] The present invention relates to a system for generating a fragrance according to a user's preferences and circumstances. Specific embodiments of this system will be described below.

[1307] System configuration

[1308] The system mainly consists of the following elements:

[1309] 1. Sensor means: A device for identifying scents in detail. For example, a gas sensor or an electronic nose is one example.

[1310] 2. Generative AI model: An artificial intelligence algorithm that generates a scent generation pattern based on input data. For example, a machine learning model or neural network is used.

[1311] 3. Scent generation means: A device that actually generates a scent based on the generated scent generation pattern. Examples include scent cartridges and diffusers.

[1312] 4. A means for inputting user preferences and context information: An interface through which users can input information about their preferences and context into the system. For example, a smartphone app or a web interface.

[1313] 5. Means for transmitting the information to the server: This is a communication means for transmitting the information entered by the user to the server. For example, Wi-Fi or Bluetooth is used.

[1314] 6. Means for the server to analyze the information and generate a prompt sentence: This is a function that allows the server to analyze the user's input information and generate a prompt sentence to be input into the generative AI model based on that information.

[1315] 7. A means for inputting a prompt sentence into the generative AI model and generating a scent generation pattern: This is a function for inputting a prompt sentence into the generative AI model and generating a scent generation pattern based on it.

[1316] 8. Means for transmitting the generated pattern to a terminal: A communication means for transmitting the generated scent generation pattern to a terminal.

[1317] System Operation

[1318] User operations

[1319] Users input information about their preferences and circumstances through a smartphone app or web interface, such as "I like the smell of coffee" or "I have a cold."

[1320] Sending information

[1321] The terminal transmits the information input by the user to the server, converts the input information into an appropriate format, and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[1322] Server Processing

[1323] The server receives and analyzes the user information sent from the device. Based on the analyzed information, the server generates a prompt sentence to input into the generative AI model. For example, it generates a prompt sentence such as "The user likes the smell of coffee."

[1324] How generative AI models work

[1325] The server inputs a prompt statement into the generative AI model and generates a scent generation pattern. The generative AI model outputs the optimal scent generation pattern based on the prompt statement. For example, in response to the prompt statement "The user likes the scent of coffee," the generative AI model outputs a coffee scent generation pattern.

[1326] Sending generated patterns and generating scents

[1327] The server transmits the generated scent generation pattern to the terminal. The terminal receives the generation pattern and generates the scent based on it. The terminal is equipped with hardware for generating the scent, such as a scent cartridge or diffuser. The terminal selects an appropriate scent according to the generation pattern and provides it to the user.

[1328] Specific examples

[1329] Example 1: If the user likes the smell of coffee

[1330] 1. The user types "I love the smell of coffee" through the application.

[1331] 2. The device sends this information to the server.

[1332] 3. The server receives the information and generates a prompt saying, "The user likes the smell of coffee."

[1333] 4. The server inputs the prompt sentence into the generative AI model and generates a pattern for generating the coffee aroma.

[1334] 5. The server sends the generated pattern to the terminal.

[1335] 6. The device receives the generated pattern and generates the coffee aroma.

[1336] Example 2: When the user has a cold

[1337] 1. The user enters "I have a cold" through the application.

[1338] 2. The device sends this information to the server.

[1339] 3. The server receives the information and generates a prompt saying, "The user has a cold."

[1340] 4. The server inputs the prompt sentence into the generative AI model and generates a pattern for generating the eucalyptus scent.

[1341] 5. The server sends the generated pattern to the terminal.

[1342] 6. The device receives the generated pattern and generates a eucalyptus scent.

[1343] This makes it possible to provide a scent that is optimized for the user's preferences and circumstances. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1344] Step 1:

[1345] Users input information about their preferences and circumstances through a smartphone app or web interface, such as "I like the smell of coffee" or "I have a cold."

[1346] Input: User preferences and context information

[1347] Output: Input information is saved on the device

[1348] Step 2:

[1349] The terminal transmits the information input by the user to the server, converts the input information into an appropriate format, and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[1350] Input: User input information

[1351] Output: User information sent to the server

[1352] Step 3:

[1353] The server receives and analyzes the user information sent from the device. Based on the analyzed information, the server generates a prompt sentence to input into the generative AI model. For example, it generates a prompt sentence such as "The user likes the smell of coffee."

[1354] Input: User information sent to the server

[1355] Output: Generated prompt statement

[1356] Step 4:

[1357] The server inputs a prompt statement into the generative AI model and generates a scent generation pattern. The generative AI model outputs the optimal scent generation pattern based on the prompt statement. For example, in response to the prompt statement "The user likes the scent of coffee," the generative AI model outputs a coffee scent generation pattern.

[1358] Input: Generated prompt text

[1359] Output: Generated scent pattern

[1360] Step 5:

[1361] The server transmits the generated scent generation pattern to the terminal, converts the generated pattern into an appropriate format, and transmits it to the terminal using a secure communication protocol.

[1362] Input: Generated scent pattern

[1363] Output: Generated pattern sent to the terminal

[1364] Step 6:

[1365] The device receives the scent generation pattern sent from the server and generates the scent based on it. The device is equipped with hardware for generating the scent, such as a scent cartridge or diffuser. The device selects an appropriate scent according to the generation pattern and provides it to the user.

[1366] Input: Generated pattern sent from the server

[1367] Output: Generated scent

[1368] Specific examples of operation

[1369] Example 1: If the user likes the smell of coffee

[1370] 1. The user types "I love the smell of coffee" through the application.

[1371] 2. The device sends this information to the server.

[1372] 3. The server receives the information and generates a prompt saying, "The user likes the smell of coffee."

[1373] 4. The server inputs the prompt sentence into the generative AI model and generates a pattern for generating the coffee aroma.

[1374] 5. The server sends the generated pattern to the terminal.

[1375] 6. The device receives the generated pattern and generates the coffee aroma.

[1376] Example 2: When the user has a cold

[1377] 1. The user enters "I have a cold" through the application.

[1378] 2. The device sends this information to the server.

[1379] 3. The server receives the information and generates a prompt saying, "The user has a cold."

[1380] 4. The server inputs the prompt sentence into the generative AI model and generates a pattern for generating the eucalyptus scent.

[1381] 5. The server sends the generated pattern to the terminal.

[1382] 6. The device receives the generated pattern and generates a eucalyptus scent.

[1383] (Application example 3)

[1384] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1385] Conventional scent generation systems have difficulty generating scents that match the user's preferences and emotions, making it impossible to provide the optimal scent for each individual user. Furthermore, in physical stores, improving the user experience has been a challenge, as scents cannot be adjusted in real time to match the user's emotions and preferences. Furthermore, there are no systems that combine facial recognition and emotion recognition, making it impossible to meet the individual needs of users.

[1386] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1387] In this invention, the server includes a sensor means for performing detailed identification of the scent, a means for inputting scent data identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, a face recognition means for recognizing the user's face, an emotion recognition means for recognizing the user's emotion, and a means for adjusting the scent generation pattern based on the user's preferences and emotions. This makes it possible to provide the optimal scent in real time according to the user's preferences and emotions.

[1388] The "sensor means for detailed identification of scents" is a device for detecting the components and strength of scents and acquiring detailed data on them.

[1389] "Generative AI" is an artificial intelligence that creates scent generation patterns based on acquired data.

[1390] The "fragrance generation means" is a device that actually generates a fragrance based on the fragrance generation pattern created by the generation AI.

[1391] The "face recognition means" is a device for detecting the face of a user and identifying an individual user.

[1392] The "emotion recognition means" is a device for analyzing and recognizing emotions from the user's facial expressions, voice, etc.

[1393] The "means for adjusting the scent generation pattern" is a device for changing the scent generation pattern based on the user's preferences and emotions.

[1394] A system for implementing this invention includes a sensor means for performing detailed scent identification, a generation AI, a scent generation means, a face recognition means, an emotion recognition means, and a means for adjusting the scent generation pattern.

[1395] First, the sensor means detects the components and intensity of the scent and acquires detailed data. The acquired data is input into the generation AI, which then creates a scent generation pattern based on this data. The generation AI also includes a function to adjust the scent generation pattern based on the user's preferences and emotions.

[1396] The facial recognition means detects the user's face and identifies each individual user. This allows information about the user's past preferences and situations to be obtained from a database. The emotion recognition means analyzes and recognizes emotions from the user's facial expressions, voice, etc. This allows the user's current emotional state to be grasped in real time.

[1397] The scent generation means actually generates scents based on the scent generation patterns created by the generation AI, making it possible to provide optimal scents in real time according to the user's preferences and emotions.

[1398] For example, when a user enters a physical store, the facial recognition means identifies the user and recognizes from past data that the user likes the scent of lavender. If the emotion recognition means detects that the user is feeling stressed, the generation AI generates a relaxing lavender scent and releases it into the store through the scent generation means.

[1399] An example of a prompt is as follows:

[1400] "When a user enters the store, a facial recognition system is used to identify them and an emotion engine is used to analyze their emotions. Based on the user's preferences and emotions, the system generates the optimal scent and controls the scent diffuser to release it throughout the store."

[1401] The following hardware and software are used to realize this system: OpenCV for face recognition, EmotionEngine for emotion recognition, ScentGenerator for scent generation, and ScentDiffuser for scent emission. By combining these technologies, it becomes possible to provide scents in real time that match the user's preferences and emotions.

[1402] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1403] Step 1:

[1404] When a user enters a physical store, the device's camera captures the user's face. A facial recognition mechanism takes this image data as input and identifies the user using a facial recognition model (e.g., OpenCV). The output is a user ID.

[1405] Step 2:

[1406] The server retrieves information about the user's past preferences and situations from a database based on the user ID. This information includes the user's preferred scents and past emotional states. The input is the user ID, and the output is data about the user's preferences and situations.

[1407] Step 3:

[1408] The device's camera captures the user's facial expressions in real time, and the emotion recognition means receives this image data as input. It analyzes the user's current emotions using an emotion recognition model (e.g., EmotionEngine). The output is the user's emotional state.

[1409] Step 4:

[1410] The server inputs data about the user's preferences and situation, as well as their current emotional state, into the generation AI. The generation AI then creates a scent generation pattern based on this data. The input is the user's preferences, situation, and emotional state, and the output is the scent generation pattern.

[1411] Step 5:

[1412] The server transmits the generated scent generation pattern to the scent generation means, which generates a scent based on this pattern. The input is the scent generation pattern, and the output is the generated scent.

[1413] Step 6:

[1414] The scent generating means releases the generated scent into the store through a scent diffuser, thereby spreading the optimal scent according to the user's preferences and emotions throughout the store. The input is the generated scent, and the output is the scent released into the store.

[1415] 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.

[1416] 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> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1417] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1418] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1419] [Third embodiment]

[1420] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1421] 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.

[1422] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1423] 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.

[1424] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1425] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1426] 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.

[1427] 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.

[1428] 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 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.

[1429] 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.

[1430] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1431] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1432] "Example 1"

[1433] One embodiment of the present invention provides an IoT device equipped with a sensor means for performing detailed scent identification, a generation AI, and a scent generation means. The sensor means analyzes compounds in the air using, for example, gas chromatography to identify the scent components in detail. The identified scent data is input to the generation AI. The generation AI uses machine learning techniques such as deep learning to learn and generate a scent generation pattern from the scent data. The scent generation means mixes the necessary compounds based on the generation pattern output from the generation AI to generate a scent.

[1434] "Example 2"

[1435] Furthermore, the generation AI generates scent patterns that take into account time and space constraints. For example, if the user uses it in the morning, it will generate a refreshing forest scent. If the user uses it during relaxation time, it will generate a relaxing lavender scent. This makes it possible to provide scents that suit the user's living environment and time of day. "Example 3"

[1436] Furthermore, the generation AI adjusts the scent generation pattern according to the user's preferences and the situation. For example, if there is information that the user likes the scent of coffee, the generation AI will prioritize the coffee scent generation pattern. Also, if there is information that the user has a cold, the generation AI will generate a eucalyptus scent generation pattern, which is said to relieve cold symptoms. This makes it possible to provide a scent optimized for the user's preferences and situation.

[1437] The processing flow of each embodiment will be described below.

[1438] "Example 1"

[1439] Step 1: The sensor analyzes the compounds in the air and identifies the scent components in detail. This identification is performed using, for example, gas chromatography.

[1440] Step 2: The identified scent data is input into the generative AI.

[1441] Step 3: The generation AI uses machine learning techniques such as deep learning to learn and generate scent generation patterns from scent data.

[1442] Step 4: The fragrance generation means mixes the necessary compounds to generate the fragrance based on the generation pattern output from the generation AI.

[1443] "Example 2"

[1444] Step 1: The generative AI generates a scent generation pattern that takes into account time and space constraints.

[1445] Step 2: For example, if the user uses it in the morning, the generation AI will generate a pattern for generating a refreshing forest scent.

[1446] Step 3: If the user uses it during relaxation time, the generation AI will generate a pattern of lavender scent that has a relaxing effect.

[1447] "Example 3"

[1448] Step 1: The AI ​​adjusts the aroma generation pattern based on the user's preferences and the situation. Step 2: For example, if the user has information that they like the aroma of coffee, the AI ​​will prioritize the coffee aroma generation pattern.

[1449] Step 3: If the user has a cold, the AI ​​generates a pattern for the scent of eucalyptus, which is said to relieve cold symptoms.

[1450] Example 1

[1451] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1452] Conventional scent generation systems have difficulty identifying scent components in detail and generating scents that suit the user's preferences and circumstances. It is also difficult to generate scents that take into account time and space constraints, making it impossible to meet the diverse needs of users.

[1453] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1454] In this invention, the server includes a sensor means for analyzing compounds in the air and identifying the fragrance components in detail, a means for inputting the identified fragrance component data into a generative AI model, which then learns a fragrance generation pattern based on the data, and a fragrance generation means for mixing the necessary compounds based on the generation pattern to generate a fragrance. This makes it possible to identify the fragrance components in detail and generate a fragrance that suits the user's preferences and situation. It is also possible to generate fragrances that take into account time and spatial constraints.

[1455] The "sensor means" is a device that analyzes compounds in the air and identifies the scent components in detail.

[1456] A "generative AI model" is an artificial intelligence model that uses machine learning technology to learn fragrance generation patterns from fragrance component data.

[1457] The "fragrance generation means" is a device that generates a fragrance by mixing the necessary compounds based on the generation pattern obtained from the generative AI model.

[1458] "Fragrance component data" refers to data relating to the types and concentrations of compounds that make up the fragrance identified by the sensor means.

[1459] A "generative pattern" is information about the combination and proportion of compounds required to produce a specific fragrance, learned by the generative AI model based on fragrance component data.

[1460] "Temporal and spatial constraints" refer to the temporal and spatial conditions and limitations that affect the generation and diffusion of scent.

[1461] "User preferences and circumstances" refers to individual requirements and conditions such as the type and strength of the scent desired by the user, and the situation in which it will be used.

[1462] MODE FOR CARRYING OUT THE INVENTION

[1463] The present invention is a system for detailed identification and generation of scents, which includes a sensor means, a generation AI model, and a scent generation means. Specific embodiments of this system will be described below.

[1464] Sensor Means

[1465] The server activates the sensor and analyzes the compounds in the air using a gas chromatography device. The gas chromatography device detects trace amounts of compounds in the air and identifies the concentration and type of each compound. The analysis results are sent to the server as fragrance component data.

[1466] Generative AI Models

[1467] The server inputs the identified scent component data into a generative AI model. The generative AI model is built using machine learning frameworks such as TensorFlow and PyTorch. The generative AI model learns scent generation patterns based on the scent component data using a deep learning algorithm. The learned generation patterns are sent to the scent generation means.

[1468] Fragrance generating means

[1469] The server controls the scent generator based on the generation pattern obtained from the generative AI model. The scent generator mixes the necessary compounds in the correct proportions to create the specified scent. This process uses specialized hardware to control chemical reactions. The generated scent is then provided to the user.

[1470] Specific examples

[1471] For example, suppose a user wants to create a "rose scent." The server uses sensor means to identify in detail the compounds that make up the rose scent and inputs that data into the generative AI model. The generative AI model learns the pattern for generating rose scent based on past data and sends that pattern to the scent generation means. The server controls the scent generation means and mixes the necessary compounds to generate the rose scent. Finally, the server provides the generated rose scent to the user.

[1472] Prompt Sentence Examples

[1473] "Please explain the process by which the generative AI learns the patterns of scent generation based on the detailed compounds that produce the rose scent."

[1474] In this way, a system is realized in which the sensor means, the generative AI model, and the scent generation means work together to perform detailed scent identification and generation.

[1475] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1476] Step 1:

[1477] The server activates the sensor means and analyzes the compounds in the air using a gas chromatography device. It receives the air sample as input and generates scent component data as output. Specifically, the gas chromatography device separates the compounds in the air and identifies the concentration and type of each compound. This data is sent to the server as scent component data.

[1478] Step 2:

[1479] The server inputs the identified scent component data into the generative AI model. It receives the scent component data as input and obtains a generated pattern as output. Specifically, the generative AI model uses machine learning frameworks such as TensorFlow and PyTorch to preprocess the scent component data and learns a scent generation pattern using a deep learning algorithm. The learned generated pattern is stored on the server.

[1480] Step 3:

[1481] The server controls the scent generator based on the generation pattern obtained from the generative AI model. It receives the generation pattern as input and generates the scent as output. Specifically, the scent generator mixes the necessary compounds in the correct proportions to create the specified scent. This process uses dedicated hardware to control the chemical reactions.

[1482] Step 4:

[1483] The server provides the generated scent to the user. It receives the generated scent as input and provides the scent to the user as output. Specifically, it provides the scent generated by the scent generation means through an interface that allows the user to check the scent. The user can check the operation of the system by smelling the generated scent.

[1484] In this way, a system is realized in which the sensor means, the generative AI model, and the scent generation means work together to perform detailed scent identification and generation.

[1485] (Application example 1)

[1486] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1487] Conventional scent generation systems are effective in identifying scents in detail and learning scent generation patterns, but they have limitations in generating scents that meet customer preferences and providing scents effectively within physical stores. Furthermore, it is difficult to generate scents that meet customer preferences and situations, making it difficult to contribute to improving the store atmosphere and customer satisfaction. This has led to a demand for new technologies to improve the quality of the customer experience.

[1488] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1489] In this invention, the server includes a sensor means for performing detailed identification of scents, a means for inputting data on the scent identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, an input means for generating a scent according to customer preferences, and a control means for generating a scent based on the data from the input means. This makes it possible to generate scents according to customer preferences, thereby improving the quality of the customer experience in a physical store.

[1490] "Sensor means" refers to a device that analyzes compounds in the air to provide detailed scent identification.

[1491] "Generative AI" is an artificial intelligence that learns and generates scent generation patterns based on identified scent data.

[1492] The "fragrance generation means" is a device that generates a fragrance by mixing the necessary compounds based on the generation pattern output from the generation AI.

[1493] The "input means" is a device or interface for inputting data for generating a fragrance according to the customer's preferences.

[1494] The "control means" is a device or system for controlling the fragrance generating means based on data from the input means and generating a fragrance.

[1495] As an embodiment of the present invention, a smart scent display system installed in a brick-and-mortar store will be described as an example. This system includes a sensor means for detailed scent identification, a generation AI, a scent generation means, an input means for generating scents according to customer preferences, and a control means.

[1496] System configuration

[1497] 1. Sensor means:

[1498] A gas chromatography sensor is used to analyze compounds in the air and identify the fragrance components in detail.

[1499] The sensor data includes detailed information about the components of the scent.

[1500] 2. Generation AI:

[1501] Using deep learning technology, scent generation patterns are learned and generated based on scent data acquired from sensor means.

[1502] The software used is Keras (deep learning library) and Scikit-learn (data preprocessing).

[1503] 3. Scent generating means:

[1504] This device mixes the necessary compounds to generate fragrance based on the generation pattern output by the generation AI.

[1505] The scent generator precisely mixes compounds according to a generation pattern.

[1506] 4. Input method:

[1507] It is an interface that allows customers to input their preferred scents via smartphones or smart glasses.

[1508] The data entered reflects the customer's preferences and circumstances.

[1509] 5. Control measures:

[1510] This is a system for controlling a fragrance generating means based on data from an input means and generating a fragrance.

[1511] The control means operates the fragrance generation means according to instructions from the generation AI.

[1512] System Operation

[1513] 1. Acquiring sensor data:

[1514] The sensor means analyzes compounds in the air and identifies scent components.

[1515] The acquired sensor data is sent to the generation AI.

[1516] 2. Data preprocessing:

[1517] The generative AI standardizes the sensor data using Scikit-learn's StandardScaler.

[1518] The standardized data is then fed into a generative AI model.

[1519] 3. Training the generative AI model:

[1520] The generative AI defines a deep learning model using Keras and learns scent generation patterns based on sensor data.

[1521] The trained model is used to predict scent production patterns.

[1522] 4. Aroma generation:

[1523] Based on the generation pattern output by the generation AI, the fragrance generation means mixes the necessary compounds to generate the fragrance.

[1524] The control means operates the fragrance generating means based on data from the input means.

[1525] Specific examples

[1526] Physical store: In an apparel shop, customers can select a "relaxing scent" using their smartphone, and that scent will spread throughout the store.

[1527] Cafe: Customers select a "fresh scent" through smart glasses, and the scent spreads throughout the store.

[1528] Prompt Sentence Examples

[1529] When a customer uses their smartphone to select a "relaxing scent," that scent will fill the store.

[1530] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1531] Step 1:

[1532] The sensor means analyzes compounds in the air and identifies scent components.

[1533] Input: Compounds in the air

[1534] Data processing: Compounds are analyzed using a gas chromatography sensor to obtain data on fragrance components.

[1535] Output: Fragrance component data

[1536] Step 2:

[1537] The server transmits the scent component data acquired from the sensor means to the generation AI.

[1538] Input: Fragrance ingredient data

[1539] Data processing: Converting data into an appropriate format for input into the generative AI.

[1540] Output: Fragrance component data input into the generation AI

[1541] Step 3:

[1542] The generation AI learns and generates fragrance generation patterns based on fragrance component data.

[1543] Input: Fragrance ingredient data input into the generation AI

[1544] Data computation: Deep learning technology is used to learn and generate scent generation patterns.

[1545] Output: Scent generation pattern

[1546] Step 4:

[1547] The server sends the generated pattern output from the generation AI to the scent generation means.

[1548] Input: scent generation pattern

[1549] Data processing: Convert the generated pattern into a suitable format for input to the scent generation means.

[1550] Output: Generated pattern input to the scent generation means

[1551] Step 5:

[1552] The scent generating means mixes necessary compounds based on the generation pattern to generate the scent.

[1553] Input: A generated pattern input to the scent generation means

[1554] Data calculation: Accurately mix compounds according to the generated patterns.

[1555] Output: Generated scent

[1556] Step 6:

[1557] Users input their preferred scent via their smartphone or smart glasses.

[1558] Input: User's preferred scent data

[1559] Data processing: Converting user input data into an appropriate format for sending to the server.

[1560] Output: User's preferred scent data sent to the server

[1561] Step 7:

[1562] The server controls the fragrance generating means based on the fragrance data of the user's preference.

[1563] Input: User's preferred scent data

[1564] Data calculation: Adjusts the generation pattern from the generation AI based on the user's preferred scent data and controls the scent generation method.

[1565] Output: Scent generated based on the adjusted generation pattern

[1566] Step 8:

[1567] The fragrance generating means generates a fragrance based on the adjusted generation pattern and spreads it throughout the physical store.

[1568] Input: Adjusted generation pattern

[1569] Data calculation: Compounds are mixed according to a coordinated production pattern to generate a scent.

[1570] Output: Generated scent spreads throughout the physical store

[1571] Example 2

[1572] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1573] Conventional scent generation systems have difficulty generating scents that suit the user's living environment and time of day, and also have difficulty adjusting them to suit the user's preferences and circumstances. As a result, it has not been possible to provide the optimal scent desired by the user. Furthermore, there has been a demand for efficient and accurate methods for detailed scent identification and generation of scent generation patterns.

[1574] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1575] In this invention, the server includes a sensor means for performing detailed identification of scents, a means for inputting scent data identified by the sensor means into a generative AI model and for the generative AI model to generate a scent generation pattern based on the data, a means for a user to input information necessary for scent generation into a terminal, a means for the terminal to send the input information to the server, a means for the server to generate a prompt sentence and input the prompt sentence to the generative AI model, a means for the generative AI model to generate a scent pattern, a means for the server to provide the generated scent pattern to the user, and a scent generation means for generating a scent based on the generation pattern. This makes it possible to generate a scent according to the user's living environment and time of day, and also to adjust the scent according to the user's preferences and situation.

[1576] The "sensor means" is a device for performing detailed identification of scents.

[1577] A "generative AI model" is an artificial intelligence algorithm that generates a scent generation pattern based on input data.

[1578] A "terminal" is a device through which a user inputs information necessary for generating a fragrance.

[1579] The "server" is a computer system that receives information sent from the terminal, generates prompt sentences, and inputs them into the generative AI model.

[1580] A "prompt sentence" is an instruction sentence that instructs the generative AI model to generate a scent generation pattern.

[1581] The "fragrance generating means" is a device that actually generates a fragrance based on the generated fragrance pattern.

[1582] A "fragrance pattern" is a combination of ingredients and blending ratios that reproduces a specific fragrance.

[1583] A "user" is an individual or organization that uses the scent generation system.

[1584] This invention is a system that generates fragrances according to the user's living environment and time of day. The system includes a sensor means for detailed identification of fragrances, a generation AI model, a terminal, a server, a prompt sentence, and a fragrance generation means.

[1585] Hardware and software used

[1586] Hardware

[1587] Sensor means: A device for identifying scents in detail. For example, a gas sensor or an electronic nose is included.

[1588] Terminal: A device through which the user inputs the information necessary to generate the scent. This includes smartphones and tablets.

[1589] Server: A computer system that receives information sent from the device, generates prompts, and inputs them into the generative AI model.

[1590] Scent generation means: A device that actually generates a scent based on the generated scent pattern. For example, an aroma diffuser.

[1591] software

[1592] Generative AI model: An artificial intelligence algorithm that generates scent patterns based on input data. Examples include natural language processing models and machine learning algorithms.

[1593] Data processing and calculation

[1594] server

[1595] The server receives the user's input information sent from the device and generates a prompt based on that information. The generated prompt is input into the generative AI model. The generative AI model analyzes the prompt and generates an appropriate scent pattern. The generated scent pattern is provided to the user via the server.

[1596] Terminal

[1597] The terminal provides an interface for the user to input information necessary for generating the fragrance. The information input by the user is transmitted from the terminal to the server.

[1598] User

[1599] The user inputs the information necessary to generate the fragrance through the device. For example, if the user desires a fragrance to use in the morning, they input "fragrance to use in the morning" into the input field on the device. If the user desires a fragrance to use during relaxation time, they input "fragrance to use during relaxation time."

[1600] Specific examples

[1601] 1. If the user wants a scent to use in the morning:

[1602] The user enters "fragrance for morning use" into the terminal.

[1603] The terminal transmits the information to the server.

[1604] The server inputs the prompt statement "Generate a refreshing forest scent to use in the morning" into the generative AI model.

[1605] The generative AI model generates patterns of refreshing forest scents, and the server provides the results to the user.

[1606] 2. If the user wishes to use a fragrance for relaxation:

[1607] The user enters "scent to use during relaxation time" into the terminal.

[1608] The terminal transmits the information to the server.

[1609] The server inputs the prompt sentence "Generate a relaxing lavender scent to use during relaxation time" into the generative AI model.

[1610] The generative AI model generates patterns of relaxing lavender scents, and the server provides the results to the user.

[1611] Prompt Sentence Examples

[1612] 1. "Produce a refreshing forest scent for morning use."

[1613] 2. "Produce a relaxing lavender scent for use during relaxation time."

[1614] This allows users to easily create fragrances that suit their living environment and time of day.

[1615] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1616] Step 1:

[1617] The user enters information into the terminal

[1618] The user inputs the information necessary to generate the fragrance through the device's interface. For example, if the user desires a fragrance to be used in the morning, they input "fragrance to be used in the morning" into the device's input field. The input data includes information such as the type of fragrance and the time of day it will be used. The input information is temporarily stored in the device's internal memory.

[1619] Step 2:

[1620] The device sends the input information to the server

[1621] The device sends the information entered by the user to the server. Specifically, the device converts the user's input data into JSON format data and sends it to the server as an HTTP POST request. This request includes information such as the type of fragrance the user desires and the time of day it will be used. The server analyzes the received data and proceeds to the next processing step.

[1622] Step 3:

[1623] The server generates a prompt

[1624] The server generates a prompt based on the user's input information received from the terminal. For example, if the user requests a "scent to use in the morning," the server generates a prompt such as "Please generate a refreshing forest scent to use in the morning." The generated prompt is stored in the server's internal memory, and the server proceeds to the next processing step.

[1625] Step 4:

[1626] The server inputs a prompt to the generative AI model.

[1627] The server inputs the generated prompt sentence into the generative AI model. Specifically, the server sends a request including the generated prompt sentence to the API endpoint of the generative AI model. The generative AI model analyzes the prompt sentence and starts internal processing to generate an appropriate scent pattern.

[1628] Step 5:

[1629] Generative AI model generates scent patterns

[1630] The generative AI model generates a scent pattern based on a prompt sent from the server. For example, for a prompt such as "A refreshing forest scent for use in the morning," the generative AI model calculates the ingredients and blending ratios required to recreate the refreshing forest scent and returns the results to the server. The generated scent pattern is sent to the server as data including the ingredient list and blending ratios.

[1631] Step 6:

[1632] The server provides the generated scent pattern to the user.

[1633] The server receives the scent pattern returned by the generative AI model and provides it to the user. Specifically, the server sends the scent pattern to the device, which then displays the information to the user. The user can check the displayed scent pattern and input it into the device to generate the scent as needed.

[1634] This allows users to easily create fragrances that suit their living environment and time of day.

[1635] (Application example 2)

[1636] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1637] Conventional aroma generation systems have difficulty generating aromas that suit the user's living environment and time of day, and are unable to provide aromas that match the ordered food, particularly in food delivery services. For this reason, there is a demand for aroma generation technology that can further enrich users' dining experiences.

[1638] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1639] In this invention, the server includes a sensor means for performing detailed identification of aromas, a means for inputting data on the aromas identified by the sensor means to a generation AI, which then generates an aroma generation pattern based on the data, an aroma generation means for generating an aroma based on the generation pattern, and a means for generating an aroma based on user order information. This makes it possible to provide aromas that match the dishes ordered by the user, further enriching the dining experience.

[1640] The "sensor means for detailed identification of scents" is a sensor device for detecting and identifying the components and characteristics of scents in detail.

[1641] "Generative AI" is an artificial intelligence model that generates scent generation patterns based on input data.

[1642] "Means for generating a scent generation pattern" refers to the function by which the generation AI creates a pattern for generating a specific scent based on scent data.

[1643] The "scent generating means" is a device or system that actually generates a scent based on the generated scent pattern.

[1644] The "means for generating aroma based on user order information" is a function that generates an appropriate aroma based on information such as the dish ordered by the user and the time of day.

[1645] A system for implementing this invention includes a sensor means for performing detailed scent identification, a generative AI model, a scent generation means, and a means for generating a scent based on user order information. Specific embodiments of the system are described below.

[1646] System configuration

[1647] 1. Sensor means for detailed scent identification:

[1648] The sensor means is a device for detecting and identifying the components and characteristics of a scent in detail, such as a gas sensor or an electronic nose.

[1649] 2. Generative AI Model:

[1650] A generative AI model is an artificial intelligence model that generates scent patterns based on input scent data. Specifically, it uses natural language processing models such as GPT-3.

[1651] 3. Scent generating means:

[1652] The scent generating means is a device or system that actually generates a scent based on the generated scent pattern, such as a scent diffuser or aroma diffuser.

[1653] 4. Means for generating scents based on user order information:

[1654] This method has the function of generating an appropriate scent based on information such as the dish ordered by the user and the time of day. For example, it works in conjunction with a food delivery application.

[1655] Program processing explanation

[1656] The server first acquires scent data from a sensor means for detailed scent identification. This data is input into a generative AI model, which generates a scent generation pattern. The generated pattern is then sent to the scent generation means, which generates the actual scent.

[1657] Furthermore, the system obtains the user's order information (e.g., type of food and time of day), and the generative AI model adjusts the aroma generation pattern based on this information, thereby providing aromas that match the food ordered by the user.

[1658] Specific examples

[1659] For example, if a user orders Italian food for breakfast, the server will input the following prompt into the generative AI model:

[1660] "If the user orders Italian food for breakfast, generate the scent of basil."

[1661] Based on this prompt sentence, the generative AI model creates a pattern for generating the scent of basil and sends it to the scent generation means, which then generates the scent of basil based on this pattern and provides it to the user.

[1662] As described above, the present invention can generate an appropriate aroma according to the user's order information and time of day, thereby enriching the dining experience.

[1663] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1664] Step 1:

[1665] The server acquires scent data from a sensor means for detailed scent identification. The sensor means detects the scent components and characteristics and sends the data to the server. The input is scent component data, and the output is the scent data sent to the server.

[1666] Step 2:

[1667] The server inputs the acquired scent data into the generative AI model, which then generates a scent generation pattern based on this data. The input is the scent data, and the output is the generated scent pattern.

[1668] Step 3:

[1669] The server transmits the generated scent pattern to the scent generation means, which generates the actual scent based on this pattern. The input is the scent generation pattern, and the output is the generated scent.

[1670] Step 4:

[1671] A user orders food through a food delivery application. The terminal sends the user's order information (type of food and time slot) to the server. The input is the user's order information, and the output is the order information sent to the server.

[1672] Step 5:

[1673] The server inputs a prompt sentence to the generative AI model based on the user's order information. For example, the prompt sentence "If the user orders Italian food for breakfast, please generate the scent of basil." The input is the prompt sentence, and the output is the generated scent pattern.

[1674] Step 6:

[1675] The server sends the scent pattern obtained from the generative AI model to the scent generation means. The scent generation means generates an appropriate scent based on this pattern and provides it to the user. The input is the scent generation pattern, and the output is the scent provided to the user.

[1676] Example 3

[1677] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1678] Conventional scent generation systems have difficulty generating scents that suit the user's preferences and circumstances, making it impossible to provide scents optimized for each individual user. It is also difficult to generate scents that take into account time and space constraints. This has limited the utility of scent generation systems, making it difficult to increase user satisfaction.

[1679] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1680] In this invention, the server includes means for collecting information on the user's preferences and circumstances, means for generating prompt sentences to be input to the generative AI model based on the collected information, and means for inputting the generated prompt sentences to the generative AI model and calculating a scent generation pattern, thereby enabling the generation of scents according to the user's preferences and circumstances.

[1681] "Information about the user's preferences and situation" refers to individual information such as the user's preferred scent, current physical condition, and environment.

[1682] A "generative AI model" refers to an artificial intelligence model that calculates scent generation patterns based on input data.

[1683] A "prompt" is an instruction to be input into a generative AI model, and refers to text generated based on the user's preferences and situation.

[1684] "Scent production pattern" refers to the specific steps and combination of ingredients used to produce a particular scent.

[1685] The term "fragrance generating means" refers to a device or mechanism for actually generating a fragrance based on the generated fragrance generation pattern.

[1686] The present invention relates to a system for generating a fragrance according to a user's preferences and circumstances. Specific embodiments of this system will be described below.

[1687] Server Roles

[1688] The server collects information about the user's preferences and circumstances. Specifically, it uses information the user enters into the application and health data obtained from the wearable device. For example, if the user enters "I like the smell of coffee" into the application, that information is stored in a database. It may also obtain information from the wearable device that "the user has a cold."

[1689] Next, the server generates a prompt sentence to be input into the generative AI model based on the collected information. For example, if the information indicates that the user likes the smell of coffee, the server generates a prompt sentence such as, "The user likes the smell of coffee, so please generate a coffee smell." If the user has a cold, the server generates a prompt sentence such as, "The user has a cold, so please generate a eucalyptus scent that will relieve cold symptoms."

[1690] The generated prompt text is sent to the device using an HTTP request, for example, by packaging the generated prompt text in JSON format and sending it to the device's API endpoint.

[1691] Device Role

[1692] The terminal receives the prompt sentence sent from the server. Specifically, the terminal receives the HTTP request and analyzes the prompt sentence. For example, it analyzes the received JSON data and extracts the prompt sentence.

[1693] Next, the device inputs the received prompt into a generative AI model to calculate the aroma generation pattern. Specifically, the prompt is input into a generative AI model (e.g., OpenAI's GPT-3), and the generated text is analyzed to extract the aroma generation pattern. For example, in response to the prompt "Please generate the aroma of coffee," the generative AI model generates the text "The components of coffee aroma are caffeine, chlorogenic acid, and pyrrolidine."

[1694] The calculated scent generation pattern is again packaged in JSON format and sent to the API endpoint of the scent generation device. For example, the generation pattern is sent in the format "{ 'ingredients': ['caffeine', 'chlorogenic acid', 'pyrrolidine']}".

[1695] User Roles

[1696] The user receives the scent provided by the scent generating device. Specifically, the scent generating device generates a scent based on the received scent generation pattern and provides it to the user. For example, a smart diffuser mixes the ingredients "caffeine, chlorogenic acid, and pyrrolidine" to generate a coffee scent and release it to the user.

[1697] Specific examples

[1698] Example 1: If the user likes the smell of coffee

[1699] 1. The server collects information that "the user likes the smell of coffee."

[1700] 2. The server sends the prompt "Generate the aroma of coffee" to the generative AI model.

[1701] 3. The device uses the generative AI model to calculate the coffee aroma generation pattern.

[1702] 4. The device sends a pattern to the smart diffuser to create the coffee aroma.

[1703] 5. The user receives the aroma of coffee from the smart diffuser.

[1704] Example 2: If the user has a cold

[1705] 1. The server collects information that "the user has a cold."

[1706] 2. The server sends the prompt to the generative AI model: "Generate a eucalyptus scent that will relieve cold symptoms."

[1707] 3. The device uses a generative AI model to calculate the eucalyptus scent generation pattern.

[1708] 4. The device sends a pattern to the smart diffuser to generate a eucalyptus scent.

[1709] 5. The user receives the eucalyptus scent from the smart diffuser.

[1710] Prompt Sentence Examples

[1711] "Users like the smell of coffee, so generate a coffee smell."

[1712] "The user has a cold, so generate a eucalyptus scent to soothe cold symptoms."

[1713] In this way, a system can be realized that can generate a fragrance according to the user's preferences and circumstances. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1714] Step 1:

[1715] The server collects information about the user's preferences and circumstances. Specifically, it uses information the user enters into the application and health data obtained from the wearable device. For example, if the user enters "I like the smell of coffee," that information is stored in a database. It may also obtain information from the wearable device that "the user has a cold."

[1716] Input: User application input data, health data from wearable devices

[1717] Output: Information data about the user's preferences and situation

[1718] Step 2:

[1719] The server generates prompts to be input into the generative AI model based on the collected information. Specifically, it uses a natural language processing algorithm to create prompts that match the user's preferences and the situation. For example, it generates a prompt such as, "The user likes the smell of coffee, so please generate the smell of coffee."

[1720] Input: User preferences and contextual information data

[1721] Output: Prompt sentence to be input to the generative AI model

[1722] Step 3:

[1723] The server sends the generated prompt text to the device. Specifically, it sends the prompt text to the device using an HTTP POST request. For example, it packages the generated prompt text in JSON format and sends it to the device's API endpoint.

[1724] Input: A prompt to be input to the generative AI model

[1725] Output: Prompt sent to the terminal

[1726] Step 4:

[1727] The terminal receives the prompt sentence sent from the server. Specifically, the terminal receives the HTTP request and analyzes the prompt sentence. For example, it analyzes the received JSON data and extracts the prompt sentence.

[1728] Input: Prompt sent from the server

[1729] Output: Parsed prompt statement

[1730] Step 5:

[1731] The device inputs the received prompt into a generative AI model and calculates the aroma generation pattern. Specifically, the prompt is input into a generative AI model (for example, OpenAI's GPT-3), and the generated text is analyzed to extract the aroma generation pattern. For example, in response to the prompt "Please generate the aroma of coffee," the generative AI model generates the text "The components of coffee aroma are caffeine, chlorogenic acid, and pyrrolidine."

[1732] Input: Parsed prompt statement

[1733] Output: Scent generation pattern

[1734] Step 6:

[1735] The terminal sends the calculated scent generation pattern to the scent generation device. Specifically, the generation pattern is packaged in JSON format and sent to the scent generation device's API endpoint. For example, the generation pattern is sent in the format "{ 'ingredients': ['caffeine', 'chlorogenic acid', 'pyrrolidine']}".

[1736] Input: scent generation pattern

[1737] Output: Generated pattern sent to the scent generating device

[1738] Step 7:

[1739] The user receives the scent provided by the scent generating device. Specifically, the scent generating device generates a scent based on the received scent generation pattern and provides it to the user. For example, a smart diffuser mixes the ingredients "caffeine, chlorogenic acid, and pyrrolidine" to generate a coffee scent and release it to the user.

[1740] Input: Generated pattern sent to the scent generating device

[1741] Output: The scent provided to the user

[1742] (Application example 3)

[1743] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1744] Conventional scent generation systems have difficulty generating scents that suit user preferences and situations, and have not been able to provide optimal scents to improve customer experience in physical stores. Therefore, there is a need for an effective scent generation system that can increase customer satisfaction.

[1745] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1746] In this invention, the server includes a sensor means for performing detailed identification of the scent, a means for inputting scent data identified by the sensor means into a generation AI, which then generates a scent generation pattern based on the data, a scent generation means for generating a scent based on the generation pattern, a means for adjusting the scent generation pattern according to the user's preferences and circumstances, and a means for generating and providing an optimal scent in a physical store based on the customer's preferences and circumstances. This makes it possible to provide an optimal scent in a physical store according to the customer's preferences and circumstances.

[1747] The "sensor means for detailed identification of scents" is a sensor device for detecting and identifying the components and characteristics of scents in detail.

[1748] "Generative AI" is an artificial intelligence system that generates scent generation patterns based on input data.

[1749] A "scent generation pattern" is a data set that shows instructions and steps for generating a specific scent.

[1750] The "fragrance generation means" is a device that actually generates a fragrance based on the fragrance generation pattern generated by the generation AI.

[1751] A "means for adjusting the scent generation pattern according to the user's preferences and circumstances" is a device or system that has the function of dynamically changing the scent generation pattern based on the user's individual preferences and current circumstances.

[1752] "Means for generating and providing optimal fragrances based on customer preferences and circumstances within a physical store" refers to a device or system that detects customer preferences and circumstances within a physical store and generates and provides optimal fragrances based on those preferences and circumstances.

[1753] A "smartphone" is a multi-functional mobile device that can connect to the Internet and run applications in addition to the functions of a mobile phone.

[1754] "Smart glasses" are glasses-type devices that have the ability to display visual information, connect to the Internet, and run applications.

[1755] A "head-mounted display" is a display device worn on the head to display visual information.

[1756] A system for implementing this invention includes a sensor means for performing detailed scent identification, a generation AI, a scent generation means, a means for adjusting the scent generation pattern according to the user's preferences and circumstances, and a means for generating and providing the optimal scent based on the customer's preferences and circumstances within a physical store.

[1757] System program generation

[1758] The server receives data from the sensor means for detailed scent identification and inputs that data into the generation AI. The generation AI generates a scent generation pattern based on the input data. The generated pattern is sent to the scent generation means, which actually generates the scent.

[1759] Program processing explanation

[1760] The server uses sensor means (e.g., gas sensors or electronic noses) to identify the scent in detail and detect the scent's components and characteristics in detail. This data is input into the generation AI. The generation AI uses software such as Python or TensorFlow to generate a scent generation pattern based on the input data. This generation pattern is sent to the scent generation means (e.g., an aroma diffuser), which actually generates the scent.

[1761] The means to adjust the scent generation pattern according to the user's preferences and circumstances is realized through applications installed on devices such as smartphones, smart glasses, and head-mounted displays. These devices detect the user's preferences and current circumstances and send that information to a server. The server then provides prompts to the generation AI based on this information and generates the optimal scent generation pattern.

[1762] Specific examples

[1763] For example, when a user enters a coffee shop, the smartphone app automatically launches and detects the user's preferences and current situation. If the user likes the smell of coffee, the AI ​​will prioritize generating a coffee aroma. If the user has a cold, it will generate a eucalyptus scent.

[1764] Prompt Sentence Examples

[1765] "The user likes coffee and vanilla and is currently healthy. Use this information to generate the optimal scent."

[1766] In this way, it becomes possible to provide optimal scents in physical stores according to customer preferences and circumstances.

[1767] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1768] Step 1:

[1769] The server receives data from a sensor means for detailed scent identification. The sensor means uses a gas sensor, electronic nose, or the like to detect the components and characteristics of the surrounding scent in detail. The input data is scent component information and concentration information. The server receives this data and passes it on to the next processing step.

[1770] Step 2:

[1771] The server inputs the received scent data into the generation AI. The generation AI uses software such as Python and TensorFlow to generate a scent generation pattern based on the input data. Specifically, the generation AI analyzes the scent component information and determines the optimal scent combination and concentration. The output is a scent generation pattern.

[1772] Step 3:

[1773] The server sends the scent generation pattern generated by the generation AI to the scent generation means. The scent generation means is a device such as an aroma diffuser, which actually generates the scent based on the received generation pattern. Specifically, the scent generation means mixes the specified ingredients at the appropriate concentration and releases the scent. The output is the generated scent.

[1774] Step 4:

[1775] The device (smartphone, smart glasses, head-mounted display) detects the user's preferences and situation. The device collects user input and sensor data (e.g., body temperature and heart rate) to determine the user's current state. The input data is the user's preference information and health status information. The device transmits this information to a server.

[1776] Step 5:

[1777] The server provides prompts to the generation AI based on information about the user's preferences and situation received from the device. The generation AI adjusts the scent generation pattern based on these prompts. Specifically, the generation AI takes into account the user's preferences and health condition and recalculates the optimal scent combination and concentration. The output is an adjusted scent generation pattern.

[1778] Step 6:

[1779] The server transmits the adjusted scent generation pattern to the scent generation means again. The scent generation means regenerates the scent based on the received generation pattern. Specifically, the scent generation means mixes the recalculated ingredients at appropriate concentrations and releases the scent. The output is the adjusted scent.

[1780] Step 7:

[1781] Users can experience the scents generated in the physical store. By smelling the scents generated, users can experience effects such as relaxation and improved concentration. Specifically, users can enjoy the changing scents as they walk around the store.

[1782] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1783] "Example 1"

[1784] In one embodiment of the present invention, an emotion engine recognizes a user's emotion and provides that information to a generation AI. The generation AI generates a scent generation pattern that takes this emotion information into account. For example, if the emotion engine recognizes that the user is feeling joy, the generation AI generates a scent generation pattern that amplifies that joy, such as a citrus scent.

[1785] "Example 2"

[1786] The generation AI also generates a scent generation pattern that takes into account the user's emotions recognized by the emotion engine as well as time and space constraints. For example, if the emotion engine recognizes that the user wants to relax at night, the generation AI will generate a scent generation pattern for lavender, which has a relaxing effect, taking into account the user's desire to relax and the time constraints of nighttime.

[1787] "Example 3"

[1788] Furthermore, the generation AI adjusts the scent generation pattern taking into account the user's emotions recognized by the emotion engine, as well as the user's preferences and situation. For example, if the emotion engine recognizes that the user is feeling sad and there is information that the user likes the scent of roses, the generation AI will generate a rose scent generation pattern that alleviates the sadness and at the same time satisfies the user's preferences.

[1789] The processing flow of each embodiment will be described below.

[1790] "Example 1"

[1791] Step 1: The emotion engine recognizes the user's emotion.

[1792] Step 2: The emotion engine provides the recognized emotion information to the generation AI.

[1793] Step 3: The generation AI generates a scent generation pattern taking into account the provided emotional information.

[1794] Step 4: Generate a scent based on the scent generation pattern generated by the generation AI.

[1795] "Example 2"

[1796] Step 1: The emotion engine recognizes the user's emotions and time and space constraints.

[1797] Step 2: The emotion engine provides the recognized emotions and time and space constraint information to the generation AI.

[1798] Step 3: The generation AI generates a scent generation pattern taking into account the provided information. Step 4: The generation AI generates a scent based on the scent generation pattern generated.

[1799] "Example 3"

[1800] Step 1: The emotion engine recognizes the user's emotions, preferences, and situation.

[1801] Step 2: The emotion engine provides the recognized information to the generation AI.

[1802] Step 3: The generation AI adjusts the scent generation pattern taking into account the provided information. Step 4: The generation AI generates a scent based on the adjusted scent generation pattern.

[1803] Example 1

[1804] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1805] Conventional scent generation systems have difficulty identifying scent components in detail and generating scents that match the user's emotions and preferences. Furthermore, they have not been able to generate scents that reflect the user's emotional state in real time. This makes it impossible to optimize the user experience and maximize the effects of the scent.

[1806] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. 【1...

Claims

1. A means for analyzing an existing fragrance and acquiring component data of the existing fragrance; A means for making a generation AI learn the existing scent generation pattern based on the acquired component data; an emotion engine means for analyzing an emotional state of a user; A means for generating a prompt sentence that instructs a trained generation AI to generate a scent generation pattern to be provided based on the analyzed emotional state; A means for controlling a generation pattern of the scent to be provided using the trained generation AI based on the prompt sentence; An aroma generating means for mixing compounds based on the generated aroma generation pattern to generate the aroma to be provided; A system including:

2. A means for acquiring a current time, generating a prompt sentence for instructing the trained generation AI to generate a generation pattern of the scent to be provided based on the current time and the emotional state; The system of claim 1 .

3. A means for acquiring user preference information; generating a prompt sentence for instructing the trained generation AI to generate a generation pattern of the scent to be provided based on the preference information, the current time, and the emotional state; The system of claim 2.

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