system

Abstract

The system according to this embodiment aims to coordinate, purchase, and ship the most suitable bouquet based on user information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a coordination unit, a generation unit, and a purchase unit. The reception unit receives user information. The analysis unit analyzes the information received by the reception unit. The coordination unit coordinates a bouquet based on the information analyzed by the analysis unit. The generation unit generates an image of the bouquet coordinated by the coordination unit. The purchase unit purchases and ships the bouquet generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to select an optimal bouquet based on the feelings and relationships that the user wants to convey through the bouquet.

[0005] The system according to the embodiment aims to coordinate, purchase, and deliver an optimal bouquet based on user information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a coordination unit, a generation unit, and a purchase unit. The reception unit receives user information. The analysis unit analyzes the information received by the reception unit. The coordination unit coordinates a bouquet based on the information analyzed by the analysis unit. The generation unit generates an image of the bouquet coordinated by the coordination unit. The purchase unit purchases and ships the bouquet generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can coordinate, purchase, and ship the most suitable bouquet based on user information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The bouquet coordination system according to an embodiment of the present invention is a system that coordinates the optimal bouquet based on user information and handles the entire process from purchase to delivery. This bouquet coordination system allows users to input information such as "the feelings they want to convey through the bouquet" and "the relationship" online. The AI ​​analyzes this information and coordinates the optimal bouquet, taking into account color schemes and flower meanings. Furthermore, an image generation AI provides an image diagram, allowing the user to purchase and have their desired bouquet delivered online. For example, if a user inputs information such as "I want to express my gratitude" or "This is a gift for my lover," the AI ​​analyzes this information and selects yellow or orange flowers for expressing gratitude, or red or pink flowers for a gift for a lover. Next, the image generation AI generates an image diagram of the coordinated bouquet. The user can then review this diagram, select their desired bouquet, and enter delivery information to complete the purchase. This allows users to effectively convey their feelings through color schemes and flower meanings, and to easily purchase and have bouquets delivered online. Thus, the bouquet coordination system coordinates the optimal bouquet based on user information and handles the entire process from purchase to delivery.

[0029] The bouquet coordination system according to this embodiment comprises a reception unit, an analysis unit, a coordination unit, a generation unit, and a purchase unit. The reception unit receives user information. User information includes, but is not limited to, names, addresses, preferences, and past purchase history. The reception unit accepts, for example, information that the user inputs online, such as "feelings you want to convey through the bouquet" or "relationship." The analysis unit analyzes the information received by the reception unit. The analysis is performed by, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes, for example, information entered by the user, such as "I want to express my gratitude" or "a gift for my lover," and extracts information for coordinating the optimal bouquet, taking into account color schemes and flower meanings. The coordination unit coordinates the bouquet based on the information analyzed by the analysis unit. The coordination is performed based on, for example, criteria such as color combinations, flower types, and arrangement methods, but is not limited to, examples. The coordination unit selects, for example, yellow or orange flowers if the user wants to express gratitude, and red or pink flowers if it is a gift for a lover. The generation unit generates an image of the bouquet coordinated by the coordination unit. The image may be generated by methods such as 2D drawings, 3D models, or rendering techniques, but is not limited to these examples. The generation unit may use, for example, an image generation AI to generate an image of the coordinated bouquet. The purchase unit purchases and ships the bouquet generated by the generation unit. The purchase and shipping may be carried out by procedures such as online payment, selection of a delivery company, and specification of delivery time, but is not limited to these examples. The purchase unit accepts a purchase from a user who likes the generated image, selects the bouquet, and enters delivery information. Thus, the bouquet coordination system according to this embodiment can coordinate the optimal bouquet based on user information and handle the entire process from purchase to shipping in a consistent manner.

[0030] The analysis unit may include a characteristics unit based on seasonal and regional characteristics. The characteristics unit takes seasonal and regional characteristics into consideration. These characteristics include, but are not limited to, seasonal flower types and regionally specific climate conditions. For example, the characteristics unit considers seasonal flower types such as cherry blossoms and tulips in spring, sunflowers and lavender in summer, cosmos and dahlias in autumn, and poinsettias and cyclamen in winter. The characteristics unit also considers regionally specific climate conditions, for example, selecting cold-hardy flowers in cold regions and heat-tolerant flowers in warm regions. This allows for more appropriate bouquet coordination by considering seasonal and regional characteristics. Some or all of the above processing in the characteristics unit may be performed using, for example, AI, or not. For example, the characteristics unit can analyze seasonal flower types and regionally specific climate conditions based on a database to coordinate the optimal bouquet.

[0031] The generation unit may include a candidate presentation unit that presents multiple candidates. The candidate presentation unit presents multiple candidates. These candidates may include, but are not limited to, bouquets of different designs or bouquets in different price ranges. For example, if the user inputs "I want to express my gratitude," the candidate presentation unit may present multiple bouquet designs using yellow and orange flowers. Alternatively, if the user inputs "A gift for my lover," the candidate presentation unit may present multiple bouquet designs using red and pink flowers. By presenting multiple candidates, the user can have choices. Some or all of the above processing in the candidate presentation unit may be performed using, for example, AI, or not using AI. For example, the candidate presentation unit may use generation AI to generate image diagrams of multiple bouquet designs and present them to the user.

[0032] The coordination unit may include a proposal unit that provides additional advice and suggestions. The proposal unit provides additional advice and suggestions. These additional advice and suggestions may include, but are not limited to, flower care instructions or suggestions for special events. For example, if the user enters "I want to express my gratitude," the proposal unit may provide flower care instructions for expressing gratitude or suggestions for special events to express gratitude. Also, if the user enters "A gift for my lover," the proposal unit may provide flower care instructions suitable for a gift for a lover or suggestions for special events suitable for a gift for a lover. This allows the user to be offered better options by providing additional advice and suggestions. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit may use generative AI to generate the content of additional advice and suggestions and provide them to the user.

[0033] The reception desk can receive information about the sender's gender, age, and specific flower preferences. This information may include, but is not limited to, whether the sender is male or female, their age, and their favorite flower type. For example, the reception desk can accept user input such as, "The sender is male, 30 years old, and likes roses." This allows for more personalized bouquet coordination by providing detailed sender information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can analyze the user's input regarding the sender's gender, age, and specific flower preferences based on a database to create the optimal bouquet.

[0034] The reception desk can analyze the user's past input history and suggest the optimal input method. Past input history includes, but is not limited to, past purchase history and past search history. For example, the reception desk can suggest similar input methods based on information the user has previously entered. The reception desk can also prioritize suggesting input formats (text, voice, etc.) the user has used in the past. Furthermore, the reception desk can predict and suggest information related to specific events from the user's past input history. This allows the reception desk to suggest the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0035] The reception unit can filter the input information based on the user's current lifestyle and areas of interest. This includes, but is not limited to, the user's occupation, hobbies, and recent activities. For example, if the user is busy, the reception unit can suggest a simpler input method. Furthermore, if the user is interested in a particular flower, the reception unit can prioritize retrieving information related to that flower. Additionally, if the user is participating in a particular event, the reception unit can prioritize retrieving information related to that event. This allows for the retrieval of more relevant information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not. For example, the reception unit can input the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0036] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location when acquiring input information. Geographical location information includes, but is not limited to, GPS data and location estimation from IP addresses. For example, if the user is in a specific region, the reception unit can prioritize retrieving information about flowers related to that region. Furthermore, if the user is traveling, the reception unit can prioritize retrieving information about flowers related to their travel destination. Additionally, if the user is participating in a specific event, the reception unit can prioritize retrieving information about flowers related to that event. This allows for the prioritization of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI retrieve highly relevant information.

[0037] The reception unit can analyze the user's social media activity and obtain relevant information when acquiring input information. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the reception unit can acquire information about relevant flowers based on information shared by the user on social media. The reception unit can also acquire information about relevant flowers based on information about accounts followed by the user on social media. Furthermore, the reception unit can acquire information about relevant flowers based on information about events the user participates in on social media. This allows for the efficient acquisition of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity into a generating AI and have the generating AI acquire the relevant information.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. The importance of the input information includes, but is not limited to, the user's priority and the freshness of the information. For example, if the user inputs information related to an important event, the analysis unit can perform a detailed analysis. If the user inputs everyday information, the analysis unit can perform a concise analysis. Furthermore, if the user inputs information about a specific flower, the analysis unit can perform a detailed analysis of information related to that flower. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the input information during analysis. The categories of input information include, but are not limited to, text data, image data, and audio data. For example, if the user inputs "I want to express my gratitude," the analysis unit can apply an algorithm related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the analysis unit can apply an algorithm related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the analysis unit can apply an algorithm related to celebrations. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of the input information. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's input information into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0040] The analysis unit can determine the priority of analysis based on the timing of input information submission during analysis. The analysis unit can determine the priority of analysis based on the timing of input information submission during analysis. The submission timing includes, but is not limited to, timestamps and submission order. For example, if the user is in a hurry, the analysis unit can determine the priority of analysis based on the submission timing. Furthermore, if the user inputs information related to a specific event, the analysis unit can determine the priority of analysis based on the timing of that event. In addition, if the user inputs everyday information, the analysis unit can determine the priority of analysis based on the submission timing. This enables efficient analysis by determining the priority of analysis based on the submission timing. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user input information into a generating AI and have the generating AI perform the determination of analysis priorities.

[0041] The analysis unit can adjust the order of analysis based on the relationships between the input information during analysis. The relationships between the input information include, but are not limited to, co-occurrence relationships and correlation relationships. For example, if the user inputs "I want to express my gratitude," the analysis unit will prioritize analyzing information related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the analysis unit can prioritize analyzing information related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the analysis unit can prioritize analyzing information related to celebrations. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the input information. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's input information into a generating AI and have the generating AI adjust the order of analysis.

[0042] The coordination unit can adjust the level of detail of the coordination based on the importance of the input information during the coordination process. The importance of the input information includes, but is not limited to, the user's priority and the freshness of the information. For example, if the user inputs information related to an important event, the coordination unit will perform a detailed coordination. If the user inputs everyday information, the coordination unit can perform a concise coordination. Furthermore, if the user inputs information about a specific flower, the coordination unit can perform a detailed coordination related to that flower. This allows for efficient coordination by adjusting the level of detail of the coordination based on the importance of the input information. Some or all of the above processing in the coordination unit may be performed using, for example, AI, or not using AI. For example, the coordination unit can input the user's input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the coordination.

[0043] The coordination unit can apply different coordination algorithms depending on the category of the input information during the coordination process. The categories of input information include, but are not limited to, text data, image data, and audio data. For example, if the user inputs "I want to express my gratitude," the coordination unit can apply an algorithm related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the coordination unit can apply an algorithm related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the coordination unit can apply an algorithm related to celebrations. This improves the accuracy of the coordination by applying the appropriate coordination algorithm according to the category of the input information. Some or all of the above-described processes in the coordination unit may be performed using, for example, AI, or not. For example, the coordination unit can input the user's input information into a generating AI and have the generating AI perform the application of the coordination algorithm.

[0044] The coordination unit can adjust the order of coordination based on the timing of submission of input information during the coordination process. The timing of submission includes, but is not limited to, timestamps and submission order. For example, if the user is in a hurry, the coordination unit can adjust the order of coordination based on the timing of submission. Furthermore, if the user inputs information related to a specific event, the coordination unit can adjust the order of coordination based on the timing of that event. In addition, if the user inputs everyday information, the coordination unit can adjust the order of coordination based on the timing of submission. This allows for efficient coordination by adjusting the order of coordination based on the timing of submission. Some or all of the above processing in the coordination unit may be performed using, for example, AI, or not using AI. For example, the coordination unit can input user input information into a generating AI and have the generating AI perform the adjustment of the order of coordination.

[0045] The coordination unit can adjust the order of coordination based on the relationships between the input information during the coordination process. The relationships between the input information include, but are not limited to, co-occurrence relationships and correlation relationships. For example, if the user inputs "I want to express my gratitude," the coordination unit will prioritize coordinating information related to gratitude. Also, if the user inputs "I'm thinking about a gift for my lover," the coordination unit can prioritize coordinating information related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the coordination unit can prioritize coordinating information related to celebrations. This allows for efficient coordination by adjusting the order of coordination based on the relationships between the input information. Some or all of the above processing in the coordination unit may be performed using, for example, AI, or not using AI. For example, the coordination unit can input the user's input information into a generating AI and have the generating AI perform the adjustment of the coordination order.

[0046] The generation unit can adjust the level of detail of the image based on the importance of the coordinated bouquet during generation. The level of detail of the image includes, but is not limited to, detailed design, simple design, etc. For example, if the user coordinates a bouquet related to an important event, the generation unit can generate a detailed image. Also, if the user coordinates an everyday bouquet, the generation unit can generate a concise image. Furthermore, if the user coordinates a bouquet related to a specific flower, the generation unit can generate a detailed image related to that flower. This allows for efficient generation by adjusting the level of detail of the image based on the importance of the bouquet. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user input information into a generation AI and have the generation AI perform the adjustment of the level of detail of the image.

[0047] The generation unit can apply different generation algorithms depending on the category of the coordinated bouquet during generation. These generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, if the user inputs "I want to express my gratitude," the generation unit can apply an algorithm related to gratitude to generate an image. Similarly, if the user inputs "I'm thinking of a gift for my lover," the generation unit can apply an algorithm related to romance to generate an image. Furthermore, if the user inputs "I'm thinking of a celebration for a friend," the generation unit can apply an algorithm related to celebrations to generate an image. This improves generation accuracy by applying the appropriate generation algorithm according to the bouquet category. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user information into a generation AI and have the generation AI apply the generation algorithm.

[0048] The generation unit can adjust the order of the image diagrams based on the submission timing of the coordinated bouquet during generation. The submission timing includes, but is not limited to, timestamps and submission order. For example, if the user is in a hurry, the generation unit can adjust the order of the image diagrams based on the submission timing. Furthermore, if the user has coordinated a bouquet related to a specific event, the generation unit can adjust the order of the image diagrams based on the timing of that event. In addition, if the user has coordinated an everyday bouquet, the generation unit can adjust the order of the image diagrams based on the submission timing. This allows for efficient generation by adjusting the order of the image diagrams based on the submission timing. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user input information into a generation AI and have the generation AI perform the adjustment of the order of the image diagrams.

[0049] The generation unit can adjust the order of the images based on the relationships between the coordinated bouquets during generation. These relationships include, but are not limited to, co-occurrence and correlation. For example, if the user inputs "I want to express my gratitude," the generation unit will prioritize generating images related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the generation unit can prioritize generating images related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the generation unit can prioritize generating images related to celebrations. This allows for efficient generation by adjusting the order of the images based on their relationships. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user information into a generation AI and have the generation AI adjust the order of the images.

[0050] The purchasing department can analyze a user's past purchase history and suggest the optimal purchasing method at the time of purchase. Past purchase history includes, but is not limited to, previously purchased items and purchase frequency. For example, the purchasing department can suggest similar purchasing methods based on information about bouquets previously purchased by the user. Furthermore, the purchasing department can prioritize suggesting purchasing methods previously used by the user (online, in-store, etc.). In addition, the purchasing department can predict and suggest purchasing methods related to specific events based on the user's past purchase history. Thus, by analyzing past purchase history, the purchasing department can suggest the optimal purchasing method for the user. Some or all of the above processes in the purchasing department may be performed using, for example, AI, or not. For example, the purchasing department can input the user's past purchase history into a generating AI and have the generating AI suggest the optimal purchasing method.

[0051] The purchasing unit can customize the purchase process based on the user's current circumstances at the time of purchase. The purchasing unit customizes the purchase process based on the user's current circumstances at the time of purchase. Current circumstances include, but are not limited to, occupation, family structure, and daily routine. For example, if the user is busy, the purchasing unit can suggest a simplified purchase process. Furthermore, if the user is interested in a particular flower, the purchasing unit can suggest a purchase process related to that flower. Additionally, if the user is participating in a particular event, the purchasing unit can suggest a purchase process related to that event. By customizing the purchase process based on the user's circumstances, a more appropriate purchase process becomes possible. Some or all of the above processing in the purchasing unit may be performed using, for example, AI, or not. For example, the purchasing unit can input the user's current circumstances into a generating AI and have the generating AI perform the customization of the purchase process.

[0052] The purchasing unit can select the optimal purchasing procedure at the time of purchase, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data or location estimation from an IP address. For example, if the user is in a specific region, the purchasing unit can suggest a purchasing procedure relevant to that region. Furthermore, if the user is traveling, the purchasing unit can suggest a purchasing procedure relevant to their travel destination. Additionally, if the user is participating in a specific event, the purchasing unit can suggest a purchasing procedure relevant to that event. This allows the purchasing unit to select the optimal purchasing procedure by considering geographical location information. Some or all of the above processing in the purchasing unit may be performed using, for example, AI, or not. For example, the purchasing unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal purchasing procedure.

[0053] The purchasing unit can analyze the user's social media activity at the time of purchase and suggest a purchase procedure. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. The purchasing unit can suggest relevant purchase procedures based on information shared by the user on social media. It can also suggest relevant purchase procedures based on information of accounts the user follows on social media. Furthermore, it can suggest relevant purchase procedures based on information of events the user participates in on social media. In this way, relevant purchase procedures can be efficiently suggested by analyzing social media activity. Some or all of the above processing in the purchasing unit may be performed using, for example, AI, or not using AI. For example, the purchasing unit can input the user's social media activity into a generating AI and have the generating AI suggest a purchase procedure.

[0054] The characteristics unit can improve the accuracy of its analysis by referring to past seasonal and regional data. Past seasonal and regional data includes, but is not limited to, past weather data and regional event information. For example, the characteristics unit can select the optimal flower based on past seasonal data. It can also select the optimal flower based on past regional data. Furthermore, it can propose the optimal flower combination based on past seasonal and regional data. This improves the accuracy of the analysis by referring to past data. Some or all of the above processing in the characteristics unit may be performed using, for example, AI, or without AI. For example, the characteristics unit can input past seasonal and regional data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0055] The characteristics unit can improve the accuracy of the analysis by considering the user's geographical location information during the analysis. Geographical location information includes, but is not limited to, GPS data and location estimation from IP addresses. For example, if the user is in a specific region, the characteristics unit will analyze by considering characteristics related to that region. Furthermore, if the user is traveling, the characteristics unit can analyze by considering characteristics related to the travel destination. Additionally, if the user is participating in a specific event, the characteristics unit can analyze by considering characteristics related to that event. This improves the accuracy of the analysis by considering geographical location information. Some or all of the above processing in the characteristics unit may be performed using, for example, AI, or without AI. For example, the characteristics unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0056] The candidate presentation unit can adjust the level of detail of the candidates based on the importance of the coordinated bouquet when presenting candidates. The level of detail of the candidates includes, but is not limited to, detailed designs and simple designs. For example, if the user coordinates a bouquet related to an important event, the candidate presentation unit will present detailed candidates. If the user coordinates an everyday bouquet, the candidate presentation unit can present concise candidates. Furthermore, if the user coordinates a bouquet related to a specific flower, the candidate presentation unit can present detailed candidates related to that flower. This allows for efficient candidate presentation by adjusting the level of detail of the candidates based on the importance of the bouquet. Some or all of the above processing in the candidate presentation unit may be performed using, for example, AI, or not using AI. For example, the candidate presentation unit can input user input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the candidates.

[0057] The candidate presentation unit can adjust the order of candidates based on the submission timing of the coordinated bouquets when presenting candidates. The submission timing includes, but is not limited to, timestamps and submission order. For example, if the user is in a hurry, the candidate presentation unit can adjust the order of candidates based on the submission timing. Furthermore, if the user has coordinated a bouquet related to a specific event, the candidate presentation unit can adjust the order of candidates based on the timing of that event. In addition, if the user has coordinated an everyday bouquet, the candidate presentation unit can adjust the order of candidates based on the submission timing. This allows for efficient candidate presentation by adjusting the order of candidates based on the submission timing. Some or all of the above processing in the candidate presentation unit may be performed using, for example, AI, or not using AI. For example, the candidate presentation unit can input user input information into a generating AI and have the generating AI perform the adjustment of the candidate order.

[0058] The suggestion unit can provide optimal advice and suggestions by referring to the user's past input history when making suggestions. Past input history includes, but is not limited to, past purchase history and past search history. The suggestion unit can provide similar advice and suggestions based on information the user has previously entered. The suggestion unit can also prioritize providing suggestions in formats the user has used in the past (text, voice, etc.). Furthermore, the suggestion unit can predict and provide advice and suggestions related to specific events from the user's past input history. This allows the suggestion unit to provide the user with the most suitable advice and suggestions by referring to past input history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past input history into a generating AI and have the generating AI perform the task of providing optimal advice and suggestions.

[0059] The suggestion unit can provide optimal advice and suggestions by considering the user's geographical location information when making suggestions. Geographical location information includes, but is not limited to, GPS data and location estimation from IP addresses. For example, if the user is in a specific region, the suggestion unit can provide advice and suggestions relevant to that region. Furthermore, if the user is traveling, the suggestion unit can provide advice and suggestions relevant to their travel destination. Additionally, if the user is participating in a specific event, the suggestion unit can provide advice and suggestions relevant to that event. This allows the suggestion unit to provide optimal advice and suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI provide optimal advice and suggestions.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can analyze a user's past purchase history and extract their preferences and tendencies regarding specific flowers. For example, if a user has frequently purchased roses in the past, it can prioritize coordinating bouquets that include roses. Furthermore, if a user tends to purchase specific flowers during certain seasons, it can suggest bouquets appropriate for that season. Additionally, if a user tends to purchase specific flowers for certain events (e.g., birthdays or anniversaries), it can suggest bouquets tailored to those events. This allows for more personalized bouquet coordination by analyzing a user's past purchase history.

[0062] The coordination function can refer to the user's past input history and suggest specific flower combinations and arrangement methods. For example, if the user has previously preferred a particular flower combination, that combination will be suggested preferentially. Similarly, if the user prefers a specific arrangement method (e.g., symmetrical or casual), that arrangement method can be suggested. Furthermore, if the user has previously selected a bouquet for a specific event, a bouquet suitable for that event can be suggested. This allows for more personalized bouquet coordination by referencing the user's past input history.

[0063] The reception desk can analyze users' social media activity to understand their current interests and trends. For example, if a user frequently posts about a particular flower on social media, it can prioritize retrieving information related to that flower. Similarly, if a user posts about a specific event (e.g., a wedding or party), it can prioritize retrieving information related to that event. Furthermore, it can retrieve relevant flower information based on the accounts a user follows and the groups they participate in. In this way, by analyzing social media activity, it can provide information based on the user's current interests and trends.

[0064] The coordination department can suggest flowers and designs specific to a region, taking into account the user's geographical location. For example, if the user is in a specific area, it will prioritize suggesting flowers and designs popular in that region. If the user is traveling, it can suggest flowers and designs specific to their destination. Furthermore, if the user is participating in a specific event, it can suggest flowers and designs related to that event. In this way, by considering geographical location, it can suggest flowers and designs unique to a region.

[0065] The purchasing department can analyze a user's past purchase history and suggest the most suitable purchasing method. For example, if a user has previously made online purchases, it will prioritize suggesting online purchases. Similarly, if a user has previously used a specific delivery service, it can prioritize suggesting that service. Furthermore, if a user has previously used a specific payment method, it can prioritize suggesting that payment method. In this way, by analyzing past purchase history, the department can suggest the most suitable purchasing method for each user.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives user information. This information includes name, address, preferences, and past purchase history. Furthermore, users can also enter information online such as "the message they want to convey through the bouquet" and "the nature of their relationship." Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, it analyzes information entered by the user, such as "I want to express my gratitude" or "A gift for my lover," and extracts information to coordinate the optimal bouquet, taking into account the colors and meanings of the flowers. Step 3: The coordination department arranges the bouquet based on the information analyzed by the analysis department. The coordination is based on criteria such as color combinations, flower types, and arrangement methods. For example, yellow or orange flowers are chosen to express gratitude, while red or pink flowers are chosen for a gift to a lover. Step 4: The generation unit generates an image of the bouquet coordinated by the coordination unit. The image is generated using methods such as 2D diagrams, 3D models, and rendering techniques. For example, an image generation AI is used to generate an image of the coordinated bouquet. Step 5: The purchasing department purchases and ships the bouquets generated by the generation department. The purchase and shipping process involves online payment, selection of a delivery company, and specification of a delivery time. For example, if a user likes the generated image, they can select the bouquet, enter their delivery information, and proceed with the purchase.

[0068] (Example of form 2) The bouquet coordination system according to an embodiment of the present invention is a system that coordinates the optimal bouquet based on user information and handles the entire process from purchase to delivery. This bouquet coordination system allows users to input information such as "the feelings they want to convey through the bouquet" and "the relationship" online. The AI ​​analyzes this information and coordinates the optimal bouquet, taking into account color schemes and flower meanings. Furthermore, an image generation AI provides an image diagram, allowing the user to purchase and have their desired bouquet delivered online. For example, if a user inputs information such as "I want to express my gratitude" or "This is a gift for my lover," the AI ​​analyzes this information and selects yellow or orange flowers for expressing gratitude, or red or pink flowers for a gift for a lover. Next, the image generation AI generates an image diagram of the coordinated bouquet. The user can then review this diagram, select their desired bouquet, and enter delivery information to complete the purchase. This allows users to effectively convey their feelings through color schemes and flower meanings, and to easily purchase and have bouquets delivered online. Thus, the bouquet coordination system coordinates the optimal bouquet based on user information and handles the entire process from purchase to delivery.

[0069] The bouquet coordination system according to this embodiment comprises a reception unit, an analysis unit, a coordination unit, a generation unit, and a purchase unit. The reception unit receives user information. User information includes, but is not limited to, names, addresses, preferences, and past purchase history. The reception unit accepts, for example, information that the user inputs online, such as "feelings you want to convey through the bouquet" or "relationship." The analysis unit analyzes the information received by the reception unit. The analysis is performed by, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes, for example, information entered by the user, such as "I want to express my gratitude" or "a gift for my lover," and extracts information for coordinating the optimal bouquet, taking into account color schemes and flower meanings. The coordination unit coordinates the bouquet based on the information analyzed by the analysis unit. The coordination is performed based on, for example, criteria such as color combinations, flower types, and arrangement methods, but is not limited to, examples. The coordination unit selects, for example, yellow or orange flowers if the user wants to express gratitude, and red or pink flowers if it is a gift for a lover. The generation unit generates an image of the bouquet coordinated by the coordination unit. The image may be generated by methods such as 2D drawings, 3D models, or rendering techniques, but is not limited to these examples. The generation unit may use, for example, an image generation AI to generate an image of the coordinated bouquet. The purchase unit purchases and ships the bouquet generated by the generation unit. The purchase and shipping may be carried out by procedures such as online payment, selection of a delivery company, and specification of delivery time, but is not limited to these examples. The purchase unit accepts a purchase from a user who likes the generated image, selects the bouquet, and enters delivery information. Thus, the bouquet coordination system according to this embodiment can coordinate the optimal bouquet based on user information and handle the entire process from purchase to shipping in a consistent manner.

[0070] The analysis unit may include a characteristics unit based on seasonal and regional characteristics. The characteristics unit takes seasonal and regional characteristics into consideration. These characteristics include, but are not limited to, seasonal flower types and regionally specific climate conditions. For example, the characteristics unit considers seasonal flower types such as cherry blossoms and tulips in spring, sunflowers and lavender in summer, cosmos and dahlias in autumn, and poinsettias and cyclamen in winter. The characteristics unit also considers regionally specific climate conditions, for example, selecting cold-hardy flowers in cold regions and heat-tolerant flowers in warm regions. This allows for more appropriate bouquet coordination by considering seasonal and regional characteristics. Some or all of the above processing in the characteristics unit may be performed using, for example, AI, or not. For example, the characteristics unit can analyze seasonal flower types and regionally specific climate conditions based on a database to coordinate the optimal bouquet.

[0071] The generation unit may include a candidate presentation unit that presents multiple candidates. The candidate presentation unit presents multiple candidates. These candidates may include, but are not limited to, bouquets of different designs or bouquets in different price ranges. For example, if the user inputs "I want to express my gratitude," the candidate presentation unit may present multiple bouquet designs using yellow and orange flowers. Alternatively, if the user inputs "A gift for my lover," the candidate presentation unit may present multiple bouquet designs using red and pink flowers. By presenting multiple candidates, the user can have choices. Some or all of the above processing in the candidate presentation unit may be performed using, for example, AI, or not using AI. For example, the candidate presentation unit may use generation AI to generate image diagrams of multiple bouquet designs and present them to the user.

[0072] The coordination unit may include a proposal unit that provides additional advice and suggestions. The proposal unit provides additional advice and suggestions. These additional advice and suggestions may include, but are not limited to, flower care instructions or suggestions for special events. For example, if the user enters "I want to express my gratitude," the proposal unit may provide flower care instructions for expressing gratitude or suggestions for special events to express gratitude. Also, if the user enters "A gift for my lover," the proposal unit may provide flower care instructions suitable for a gift for a lover or suggestions for special events suitable for a gift for a lover. This allows the user to be offered better options by providing additional advice and suggestions. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit may use generative AI to generate the content of additional advice and suggestions and provide them to the user.

[0073] The reception desk can receive information about the sender's gender, age, and specific flower preferences. This information may include, but is not limited to, whether the sender is male or female, their age, and their favorite flower type. For example, the reception desk can accept user input such as, "The sender is male, 30 years old, and likes roses." This allows for more personalized bouquet coordination by providing detailed sender information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can analyze the user's input regarding the sender's gender, age, and specific flower preferences based on a database to create the optimal bouquet.

[0074] The reception unit can estimate the user's emotions and prioritize input information based on those emotions. User emotions include, but are not limited to, feelings of gratitude, romantic feelings, or celebratory feelings. For example, if the user inputs "I want to express my gratitude," the reception unit will prioritize input related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the reception unit will prioritize input related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the reception unit will prioritize input related to celebrations. This allows for the prioritization of more appropriate information by determining input information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0075] The reception desk can analyze the user's past input history and suggest the optimal input method. Past input history includes, but is not limited to, past purchase history and past search history. For example, the reception desk can suggest similar input methods based on information the user has previously entered. The reception desk can also prioritize suggesting input formats (text, voice, etc.) the user has used in the past. Furthermore, the reception desk can predict and suggest information related to specific events from the user's past input history. This allows the reception desk to suggest the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI suggest the optimal input method.

[0076] The reception unit can filter the input information based on the user's current lifestyle and areas of interest. This includes, but is not limited to, the user's occupation, hobbies, and recent activities. For example, if the user is busy, the reception unit can suggest a simpler input method. Furthermore, if the user is interested in a particular flower, the reception unit can prioritize retrieving information related to that flower. Additionally, if the user is participating in a particular event, the reception unit can prioritize retrieving information related to that event. This allows for the retrieval of more relevant information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not. For example, the reception unit can input the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0077] The reception unit can estimate the user's emotions and adjust the timing of acquiring input information based on the estimated emotions. The user's emotions include, but are not limited to, feelings of gratitude, romantic feelings, or celebratory feelings. For example, if the user is relaxed, the reception unit can adjust the timing of requesting detailed input. If the user is in a hurry, the reception unit can adjust the timing of requesting concise input. Furthermore, if the user is emotional, the reception unit can adjust the timing of prioritizing the acquisition of emotion-related information. This allows for information to be acquired at a more appropriate time by adjusting the timing of input information acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception desk can input user information into a generating AI and have the AI ​​perform emotion estimation.

[0078] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location when acquiring input information. Geographical location information includes, but is not limited to, GPS data and location estimation from IP addresses. For example, if the user is in a specific region, the reception unit can prioritize retrieving information about flowers related to that region. Furthermore, if the user is traveling, the reception unit can prioritize retrieving information about flowers related to their travel destination. Additionally, if the user is participating in a specific event, the reception unit can prioritize retrieving information about flowers related to that event. This allows for the prioritization of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI retrieve highly relevant information.

[0079] The reception unit can analyze the user's social media activity and obtain relevant information when acquiring input information. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the reception unit can acquire information about relevant flowers based on information shared by the user on social media. The reception unit can also acquire information about relevant flowers based on information about accounts followed by the user on social media. Furthermore, the reception unit can acquire information about relevant flowers based on information about events the user participates in on social media. This allows for the efficient acquisition of relevant information by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity into a generating AI and have the generating AI acquire the relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. The user's emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the analysis unit will analyze information related to gratitude in detail. Similarly, if the user inputs "I'm thinking about a gift for my lover," the analysis unit can analyze information related to romance in detail. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the analysis unit can analyze information related to celebration in detail. By adjusting the accuracy of the analysis based on the user's emotions, more accurate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input user information into a generating AI and have the generating AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. The importance of the input information includes, but is not limited to, the user's priority and the freshness of the information. For example, if the user inputs information related to an important event, the analysis unit can perform a detailed analysis. If the user inputs everyday information, the analysis unit can perform a concise analysis. Furthermore, if the user inputs information about a specific flower, the analysis unit can perform a detailed analysis of information related to that flower. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the input information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the input information during analysis. The categories of input information include, but are not limited to, text data, image data, and audio data. For example, if the user inputs "I want to express my gratitude," the analysis unit can apply an algorithm related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the analysis unit can apply an algorithm related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the analysis unit can apply an algorithm related to celebrations. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of the input information. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's input information into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. The user's emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the analysis unit will highlight and display information related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the analysis unit can highlight and display information related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the analysis unit can highlight and display information related to celebration. This allows for a more easily understandable display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on the timing of input information submission during analysis. The analysis unit can determine the priority of analysis based on the timing of input information submission during analysis. The submission timing includes, but is not limited to, timestamps and submission order. For example, if the user is in a hurry, the analysis unit can determine the priority of analysis based on the submission timing. Furthermore, if the user inputs information related to a specific event, the analysis unit can determine the priority of analysis based on the timing of that event. In addition, if the user inputs everyday information, the analysis unit can determine the priority of analysis based on the submission timing. This enables efficient analysis by determining the priority of analysis based on the submission timing. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user input information into a generating AI and have the generating AI perform the determination of analysis priorities.

[0085] The analysis unit can adjust the order of analysis based on the relationships between the input information during analysis. The relationships between the input information include, but are not limited to, co-occurrence relationships and correlation relationships. For example, if the user inputs "I want to express my gratitude," the analysis unit will prioritize analyzing information related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the analysis unit can prioritize analyzing information related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the analysis unit can prioritize analyzing information related to celebrations. This allows for efficient analysis by adjusting the order of analysis based on the relationships between the input information. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's input information into a generating AI and have the generating AI adjust the order of analysis.

[0086] The coordination unit can estimate the user's emotions and adjust the coordination method based on the estimated emotions. These emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the coordination unit will prioritize selecting flowers related to gratitude. Similarly, if the user inputs "I'm thinking of a gift for my lover," the coordination unit can prioritize selecting flowers related to romance. Furthermore, if the user inputs "I'm thinking of a celebration for a friend," the coordination unit can prioritize selecting flowers related to celebrations. This allows for more appropriate bouquet coordination by adjusting the coordination method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0087] The coordination unit can adjust the level of detail of the coordination based on the importance of the input information during the coordination process. The importance of the input information includes, but is not limited to, the user's priority and the freshness of the information. For example, if the user inputs information related to an important event, the coordination unit will perform a detailed coordination. If the user inputs everyday information, the coordination unit can perform a concise coordination. Furthermore, if the user inputs information about a specific flower, the coordination unit can perform a detailed coordination related to that flower. This allows for efficient coordination by adjusting the level of detail of the coordination based on the importance of the input information. Some or all of the above processing in the coordination unit may be performed using, for example, AI, or not using AI. For example, the coordination unit can input the user's input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the coordination.

[0088] The coordination unit can apply different coordination algorithms depending on the category of the input information during the coordination process. The categories of input information include, but are not limited to, text data, image data, and audio data. For example, if the user inputs "I want to express my gratitude," the coordination unit can apply an algorithm related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the coordination unit can apply an algorithm related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the coordination unit can apply an algorithm related to celebrations. This improves the accuracy of the coordination by applying the appropriate coordination algorithm according to the category of the input information. Some or all of the above-described processes in the coordination unit may be performed using, for example, AI, or not. For example, the coordination unit can input the user's input information into a generating AI and have the generating AI perform the application of the coordination algorithm.

[0089] The coordination unit can estimate the user's emotions and determine the priority of coordination based on the estimated emotions. The emotions of the user include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the coordination unit will prioritize coordination related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the coordination unit will prioritize coordination related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the coordination unit will prioritize coordination related to celebrations. This allows for coordination to be performed in a more appropriate order by determining the priority of coordination based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0090] The coordination unit can adjust the order of coordination based on the timing of submission of input information during the coordination process. The timing of submission includes, but is not limited to, timestamps and submission order. For example, if the user is in a hurry, the coordination unit can adjust the order of coordination based on the timing of submission. Furthermore, if the user inputs information related to a specific event, the coordination unit can adjust the order of coordination based on the timing of that event. In addition, if the user inputs everyday information, the coordination unit can adjust the order of coordination based on the timing of submission. This allows for efficient coordination by adjusting the order of coordination based on the timing of submission. Some or all of the above processing in the coordination unit may be performed using, for example, AI, or not using AI. For example, the coordination unit can input user input information into a generating AI and have the generating AI perform the adjustment of the order of coordination.

[0091] The coordination unit can adjust the order of coordination based on the relationships between the input information during the coordination process. The relationships between the input information include, but are not limited to, co-occurrence relationships and correlation relationships. For example, if the user inputs "I want to express my gratitude," the coordination unit will prioritize coordinating information related to gratitude. Also, if the user inputs "I'm thinking about a gift for my lover," the coordination unit can prioritize coordinating information related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the coordination unit can prioritize coordinating information related to celebrations. This allows for efficient coordination by adjusting the order of coordination based on the relationships between the input information. Some or all of the above processing in the coordination unit may be performed using, for example, AI, or not using AI. For example, the coordination unit can input the user's input information into a generating AI and have the generating AI perform the adjustment of the coordination order.

[0092] The generation unit can estimate the user's emotions and adjust the representation of the generated image based on the estimated user emotions. User emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the generation unit can generate an image emphasizing colors and designs related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the generation unit can generate an image emphasizing colors and designs related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the generation unit can generate an image emphasizing colors and designs related to celebration. By adjusting the representation of the image based on the user's emotions, a more appropriate image can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user input information into a generation AI and have the generation AI perform emotion estimation.

[0093] The generation unit can adjust the level of detail of the image based on the importance of the coordinated bouquet during generation. The level of detail of the image includes, but is not limited to, detailed design, simple design, etc. For example, if the user coordinates a bouquet related to an important event, the generation unit can generate a detailed image. Also, if the user coordinates an everyday bouquet, the generation unit can generate a concise image. Furthermore, if the user coordinates a bouquet related to a specific flower, the generation unit can generate a detailed image related to that flower. This allows for efficient generation by adjusting the level of detail of the image based on the importance of the bouquet. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user input information into a generation AI and have the generation AI perform the adjustment of the level of detail of the image.

[0094] The generation unit can apply different generation algorithms depending on the category of the coordinated bouquet during generation. These generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, if the user inputs "I want to express my gratitude," the generation unit can apply an algorithm related to gratitude to generate an image. Similarly, if the user inputs "I'm thinking of a gift for my lover," the generation unit can apply an algorithm related to romance to generate an image. Furthermore, if the user inputs "I'm thinking of a celebration for a friend," the generation unit can apply an algorithm related to celebrations to generate an image. This improves generation accuracy by applying the appropriate generation algorithm according to the bouquet category. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user information into a generation AI and have the generation AI apply the generation algorithm.

[0095] The generation unit can estimate the user's emotions and determine the priority of the images to be generated based on the estimated emotions. The user's emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the generation unit will prioritize generating images related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the generation unit can prioritize generating images related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the generation unit can prioritize generating images related to celebrations. By prioritizing the images based on the user's emotions, images are generated in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user input information into a generation AI and have the generation AI perform emotion estimation.

[0096] The generation unit can adjust the order of the image diagrams based on the submission timing of the coordinated bouquet during generation. The submission timing includes, but is not limited to, timestamps and submission order. For example, if the user is in a hurry, the generation unit can adjust the order of the image diagrams based on the submission timing. Furthermore, if the user has coordinated a bouquet related to a specific event, the generation unit can adjust the order of the image diagrams based on the timing of that event. In addition, if the user has coordinated an everyday bouquet, the generation unit can adjust the order of the image diagrams based on the submission timing. This allows for efficient generation by adjusting the order of the image diagrams based on the submission timing. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user input information into a generation AI and have the generation AI perform the adjustment of the order of the image diagrams.

[0097] The generation unit can adjust the order of the images based on the relationships between the coordinated bouquets during generation. These relationships include, but are not limited to, co-occurrence and correlation. For example, if the user inputs "I want to express my gratitude," the generation unit will prioritize generating images related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the generation unit can prioritize generating images related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the generation unit can prioritize generating images related to celebrations. This allows for efficient generation by adjusting the order of the images based on their relationships. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user information into a generation AI and have the generation AI adjust the order of the images.

[0098] The purchasing unit can estimate the user's emotions and adjust the purchasing process based on those emotions. These emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user enters "I want to express my gratitude," the purchasing unit will prioritize purchases related to gratitude. Similarly, if the user enters "I'm thinking of buying a gift for my lover," the purchasing unit will prioritize purchases related to romance. Furthermore, if the user enters "I'm thinking of celebrating a friend," the purchasing unit will prioritize purchases related to celebrations. This allows for more appropriate purchases by adjusting the purchasing process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the purchasing department may be performed using AI, for example, or without AI. For example, the purchasing department can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0099] The purchasing department can analyze a user's past purchase history and suggest the optimal purchasing method at the time of purchase. Past purchase history includes, but is not limited to, previously purchased items and purchase frequency. For example, the purchasing department can suggest similar purchasing methods based on information about bouquets previously purchased by the user. Furthermore, the purchasing department can prioritize suggesting purchasing methods previously used by the user (online, in-store, etc.). In addition, the purchasing department can predict and suggest purchasing methods related to specific events based on the user's past purchase history. Thus, by analyzing past purchase history, the purchasing department can suggest the optimal purchasing method for the user. Some or all of the above processes in the purchasing department may be performed using, for example, AI, or not. For example, the purchasing department can input the user's past purchase history into a generating AI and have the generating AI suggest the optimal purchasing method.

[0100] The purchasing unit can customize the purchase process based on the user's current circumstances at the time of purchase. The purchasing unit customizes the purchase process based on the user's current circumstances at the time of purchase. Current circumstances include, but are not limited to, occupation, family structure, and daily routine. For example, if the user is busy, the purchasing unit can suggest a simplified purchase process. Furthermore, if the user is interested in a particular flower, the purchasing unit can suggest a purchase process related to that flower. Additionally, if the user is participating in a particular event, the purchasing unit can suggest a purchase process related to that event. By customizing the purchase process based on the user's circumstances, a more appropriate purchase process becomes possible. Some or all of the above processing in the purchasing unit may be performed using, for example, AI, or not. For example, the purchasing unit can input the user's current circumstances into a generating AI and have the generating AI perform the customization of the purchase process.

[0101] The purchasing unit can estimate the user's emotions and determine the priority of the purchase process based on those emotions. These emotions include, but are not limited to, feelings of gratitude, romantic feelings, or celebratory feelings. For example, if the user enters "I want to express my gratitude," the purchasing unit will prioritize purchases related to gratitude. Similarly, if the user enters "I'm thinking about a gift for my lover," the purchasing unit will prioritize purchases related to romance. Furthermore, if the user enters "I'm thinking about a celebration for a friend," the purchasing unit will prioritize purchases related to celebrations. This allows for a more appropriate order of purchases by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the purchasing department may be performed using AI, for example, or without AI. For example, the purchasing department can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0102] The purchasing unit can select the optimal purchasing procedure at the time of purchase, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data or location estimation from an IP address. For example, if the user is in a specific region, the purchasing unit can suggest a purchasing procedure relevant to that region. Furthermore, if the user is traveling, the purchasing unit can suggest a purchasing procedure relevant to their travel destination. Additionally, if the user is participating in a specific event, the purchasing unit can suggest a purchasing procedure relevant to that event. This allows the purchasing unit to select the optimal purchasing procedure by considering geographical location information. Some or all of the above processing in the purchasing unit may be performed using, for example, AI, or not. For example, the purchasing unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal purchasing procedure.

[0103] The purchasing unit can analyze the user's social media activity at the time of purchase and suggest a purchase procedure. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. The purchasing unit can suggest relevant purchase procedures based on information shared by the user on social media. It can also suggest relevant purchase procedures based on information of accounts the user follows on social media. Furthermore, it can suggest relevant purchase procedures based on information of events the user participates in on social media. In this way, relevant purchase procedures can be efficiently suggested by analyzing social media activity. Some or all of the above processing in the purchasing unit may be performed using, for example, AI, or not using AI. For example, the purchasing unit can input the user's social media activity into a generating AI and have the generating AI suggest a purchase procedure.

[0104] The characteristics unit can estimate the user's emotions and adjust the method of considering seasonal and regional characteristics based on the estimated user emotions. User emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the characteristics unit will consider seasonal and regional characteristics related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the characteristics unit can consider seasonal and regional characteristics related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the characteristics unit can consider seasonal and regional characteristics related to celebrations. This allows for more appropriate bouquet coordination by adjusting the method of considering seasonal and regional characteristics based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the characteristics unit may be performed using AI, for example, or without AI. For example, the characteristics unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0105] The characteristics unit can improve the accuracy of its analysis by referring to past seasonal and regional data. Past seasonal and regional data includes, but is not limited to, past weather data and regional event information. For example, the characteristics unit can select the optimal flower based on past seasonal data. It can also select the optimal flower based on past regional data. Furthermore, it can propose the optimal flower combination based on past seasonal and regional data. This improves the accuracy of the analysis by referring to past data. Some or all of the above processing in the characteristics unit may be performed using, for example, AI, or without AI. For example, the characteristics unit can input past seasonal and regional data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0106] The characteristics unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. User emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the characteristics unit will highlight and display characteristics related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the characteristics unit can highlight and display characteristics related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the characteristics unit can highlight and display characteristics related to celebration. This allows for a more easily understandable display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the characteristics unit may be performed using AI, for example, or without AI. For example, the characteristics unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0107] The characteristics unit can improve the accuracy of the analysis by considering the user's geographical location information during the analysis. Geographical location information includes, but is not limited to, GPS data and location estimation from IP addresses. For example, if the user is in a specific region, the characteristics unit will analyze by considering characteristics related to that region. Furthermore, if the user is traveling, the characteristics unit can analyze by considering characteristics related to the travel destination. Additionally, if the user is participating in a specific event, the characteristics unit can analyze by considering characteristics related to that event. This improves the accuracy of the analysis by considering geographical location information. Some or all of the above processing in the characteristics unit may be performed using, for example, AI, or without AI. For example, the characteristics unit can input the user's geographical location information into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0108] The candidate suggestion unit can estimate the user's emotions and determine the priority of the candidates to present based on the estimated emotions. The user's emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the candidate suggestion unit will prioritize presenting candidates related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the candidate suggestion unit can prioritize presenting candidates related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the candidate suggestion unit can prioritize presenting candidates related to celebrations. This ensures that candidates are presented in a more appropriate order by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the candidate presentation unit may be performed using AI, for example, or without AI. For example, the candidate presentation unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0109] The candidate presentation unit can adjust the level of detail of the candidates based on the importance of the coordinated bouquet when presenting candidates. The level of detail of the candidates includes, but is not limited to, detailed designs and simple designs. For example, if the user coordinates a bouquet related to an important event, the candidate presentation unit will present detailed candidates. If the user coordinates an everyday bouquet, the candidate presentation unit can present concise candidates. Furthermore, if the user coordinates a bouquet related to a specific flower, the candidate presentation unit can present detailed candidates related to that flower. This allows for efficient candidate presentation by adjusting the level of detail of the candidates based on the importance of the bouquet. Some or all of the above processing in the candidate presentation unit may be performed using, for example, AI, or not using AI. For example, the candidate presentation unit can input user input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the candidates.

[0110] The suggestion display unit can estimate the user's emotions and adjust the display method of the suggested candidates based on the estimated emotions. The user's emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user enters "I want to express my gratitude," the suggestion display unit will highlight and display candidates related to gratitude. Similarly, if the user enters "I'm thinking about a gift for my lover," the suggestion display unit can highlight and display candidates related to romance. Furthermore, if the user enters "I'm thinking about a celebration for a friend," the suggestion display unit can highlight and display candidates related to celebrations. By adjusting the display method of candidates based on the user's emotions, a more easily understandable display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the candidate presentation unit may be performed using AI, for example, or without AI. For example, the candidate presentation unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0111] The candidate presentation unit can adjust the order of candidates based on the submission timing of the coordinated bouquets when presenting candidates. The submission timing includes, but is not limited to, timestamps and submission order. For example, if the user is in a hurry, the candidate presentation unit can adjust the order of candidates based on the submission timing. Furthermore, if the user has coordinated a bouquet related to a specific event, the candidate presentation unit can adjust the order of candidates based on the timing of that event. In addition, if the user has coordinated an everyday bouquet, the candidate presentation unit can adjust the order of candidates based on the submission timing. This allows for efficient candidate presentation by adjusting the order of candidates based on the submission timing. Some or all of the above processing in the candidate presentation unit may be performed using, for example, AI, or not using AI. For example, the candidate presentation unit can input user input information into a generating AI and have the generating AI perform the adjustment of the candidate order.

[0112] The suggestion unit can estimate the user's emotions and adjust the method of providing additional advice and suggestions based on the estimated emotions. The user's emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the suggestion unit will provide advice and suggestions related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the suggestion unit can provide advice and suggestions related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the suggestion unit can provide advice and suggestions related to celebrations. This allows for more appropriate advice and suggestions by adjusting the method of advice and suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0113] The suggestion unit can provide optimal advice and suggestions by referring to the user's past input history when making suggestions. Past input history includes, but is not limited to, past purchase history and past search history. The suggestion unit can provide similar advice and suggestions based on information the user has previously entered. The suggestion unit can also prioritize providing suggestions in formats the user has used in the past (text, voice, etc.). Furthermore, the suggestion unit can predict and provide advice and suggestions related to specific events from the user's past input history. This allows the suggestion unit to provide the user with the most suitable advice and suggestions by referring to past input history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past input history into a generating AI and have the generating AI perform the task of providing optimal advice and suggestions.

[0114] The suggestion unit can estimate the user's emotions and prioritize additional advice and suggestions based on those emotions. User emotions include, but are not limited to, feelings of gratitude, romantic feelings, and celebratory feelings. For example, if the user inputs "I want to express my gratitude," the suggestion unit will prioritize advice and suggestions related to gratitude. Similarly, if the user inputs "I'm thinking about a gift for my lover," the suggestion unit will prioritize advice and suggestions related to romance. Furthermore, if the user inputs "I'm thinking about a celebration for a friend," the suggestion unit will prioritize advice and suggestions related to celebrations. This allows for more appropriate order in which advice and suggestions are provided by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user input information into a generating AI and have the generating AI perform emotion estimation.

[0115] The suggestion unit can provide optimal advice and suggestions by considering the user's geographical location information when making suggestions. Geographical location information includes, but is not limited to, GPS data and location estimation from IP addresses. For example, if the user is in a specific region, the suggestion unit can provide advice and suggestions relevant to that region. Furthermore, if the user is traveling, the suggestion unit can provide advice and suggestions relevant to their travel destination. Additionally, if the user is participating in a specific event, the suggestion unit can provide advice and suggestions relevant to that event. This allows the suggestion unit to provide optimal advice and suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI provide optimal advice and suggestions.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The reception desk can analyze the user's tone and speed of voice to estimate their emotions. For example, if a user speaks in an excited voice, it can estimate that they are expressing joy or excitement, and use this to prioritize input information. If a user speaks in a slow tone, it can estimate that they are expressing calm emotions, and use this to request more detailed input. Furthermore, if a user is in a hurry, it can estimate that they are speaking in a fast tone and request concise input. In this way, by estimating emotions based on the user's tone and speed of voice and adjusting the priority of input information, more appropriate information can be obtained.

[0118] The analysis unit can analyze a user's past purchase history and extract their preferences and tendencies regarding specific flowers. For example, if a user has frequently purchased roses in the past, it can prioritize coordinating bouquets that include roses. Furthermore, if a user tends to purchase specific flowers during certain seasons, it can suggest bouquets appropriate for that season. Additionally, if a user tends to purchase specific flowers for certain events (e.g., birthdays or anniversaries), it can suggest bouquets tailored to those events. This allows for more personalized bouquet coordination by analyzing a user's past purchase history.

[0119] The generation unit can estimate the user's emotions and adjust the color scheme and design of the generated image based on those emotions. For example, if the user inputs that they want to express gratitude, the unit can generate an image emphasizing warm colors and a soft design. If the user is thinking about a gift for their lover, the unit can generate an image emphasizing passionate colors and a romantic design. Furthermore, if the user is thinking about celebrating a friend, the unit can generate an image emphasizing bright and cheerful colors and a cheerful design. In this way, by adjusting the color scheme and design of the image based on the user's emotions, the unit can provide a more appropriate image.

[0120] The coordination function can refer to the user's past input history and suggest specific flower combinations and arrangement methods. For example, if the user has previously preferred a particular flower combination, that combination will be suggested preferentially. Similarly, if the user prefers a specific arrangement method (e.g., symmetrical or casual), that arrangement method can be suggested. Furthermore, if the user has previously selected a bouquet for a specific event, a bouquet suitable for that event can be suggested. This allows for more personalized bouquet coordination by referencing the user's past input history.

[0121] The reception desk can analyze users' social media activity to understand their current interests and trends. For example, if a user frequently posts about a particular flower on social media, it can prioritize retrieving information related to that flower. Similarly, if a user posts about a specific event (e.g., a wedding or party), it can prioritize retrieving information related to that event. Furthermore, it can retrieve relevant flower information based on the accounts a user follows and the groups they participate in. In this way, by analyzing social media activity, it can provide information based on the user's current interests and trends.

[0122] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user inputs that they want to express gratitude, the system will analyze information related to gratitude in detail. If the user is thinking about a gift for their significant other, the system can analyze information related to romance in detail. Furthermore, if the user is thinking about celebrating a friend, the system can analyze information related to celebrations in detail. By adjusting the accuracy of the analysis based on the user's emotions, more accurate analysis results can be obtained.

[0123] The coordination department can suggest flowers and designs specific to a region, taking into account the user's geographical location. For example, if the user is in a specific area, it will prioritize suggesting flowers and designs popular in that region. If the user is traveling, it can suggest flowers and designs specific to their destination. Furthermore, if the user is participating in a specific event, it can suggest flowers and designs related to that event. In this way, by considering geographical location, it can suggest flowers and designs unique to a region.

[0124] The generation unit can estimate the user's emotions and adjust the representation of the generated image based on those emotions. For example, if the user inputs that they want to express gratitude, the unit can generate an image that emphasizes colors and designs related to gratitude. If the user is thinking about a gift for their lover, the unit can generate an image that emphasizes colors and designs related to romance. Furthermore, if the user is thinking about a celebration for a friend, the unit can generate an image that emphasizes colors and designs related to celebrations. In this way, by adjusting the representation of the image based on the user's emotions, a more appropriate image can be generated.

[0125] The purchasing department can analyze a user's past purchase history and suggest the most suitable purchasing method. For example, if a user has previously made online purchases, it will prioritize suggesting online purchases. Similarly, if a user has previously used a specific delivery service, it can prioritize suggesting that service. Furthermore, if a user has previously used a specific payment method, it can prioritize suggesting that payment method. In this way, by analyzing past purchase history, the department can suggest the most suitable purchasing method for each user.

[0126] The reception desk can estimate the user's emotions and adjust the timing of information acquisition based on those estimates. For example, if the user is relaxed, it can adjust the timing of requests for detailed information. If the user is in a hurry, it can adjust the timing of requests for concise information. Furthermore, if the user is emotional, it can adjust the timing of information acquisition prioritizing information related to those emotions. By adjusting the timing of information acquisition based on the user's emotions, information can be obtained at a more appropriate time.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk receives user information. This information includes name, address, preferences, and past purchase history. Furthermore, users can also enter information online such as "the message they want to convey through the bouquet" and "the nature of their relationship." Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, it analyzes information entered by the user, such as "I want to express my gratitude" or "A gift for my lover," and extracts information to coordinate the optimal bouquet, taking into account the colors and meanings of the flowers. Step 3: The coordination department arranges the bouquet based on the information analyzed by the analysis department. The coordination is based on criteria such as color combinations, flower types, and arrangement methods. For example, yellow or orange flowers are chosen to express gratitude, while red or pink flowers are chosen for a gift to a lover. Step 4: The generation unit generates an image of the bouquet coordinated by the coordination unit. The image is generated using methods such as 2D diagrams, 3D models, and rendering techniques. For example, an image generation AI is used to generate an image of the coordinated bouquet. Step 5: The purchasing department purchases and ships the bouquets generated by the generation department. The purchase and shipping process involves online payment, selection of a delivery company, and specification of a delivery time. For example, if a user likes the generated image, they can select the bouquet, enter their delivery information, and proceed with the purchase.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, coordination unit, generation unit, and purchase unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and coordinates a bouquet based on the analyzed information. The generation unit is implemented by, for example, the output device 40 of the smart device 14 and generates an image of the coordinated bouquet. The purchase unit is implemented by, for example, the reception device 38 of the smart device 14 and purchases and ships the generated bouquet. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, coordination unit, generation unit, and purchase unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user information. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the received information. The coordination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and coordinates the bouquet based on the analyzed information. The generation unit is implemented by, for example, the speaker 240 of the smart glasses 214 and generates an image of the coordinated bouquet. The purchase unit is implemented by, for example, the microphone 238 of the smart glasses 214 and purchases and ships the generated bouquet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, coordination unit, generation unit, and purchase unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and coordinates the bouquet based on the analyzed information. The generation unit is implemented by, for example, the display 343 of the headset terminal 314 and generates an image of the coordinated bouquet. The purchase unit is implemented by, for example, the microphone 238 of the headset terminal 314 and purchases and ships the generated bouquet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the reception unit, analysis unit, coordination unit, generation unit, and purchasing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user information. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and coordinates the bouquet based on the analyzed information. The generation unit is implemented by, for example, the speaker 240 of the robot 414 and generates an image of the coordinated bouquet. The purchasing unit is implemented by, for example, the microphone 238 of the robot 414 and purchases and ships the generated bouquet. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A reception desk that receives user information, An analysis unit that analyzes the information received by the reception unit, A coordination unit that coordinates a bouquet based on the information analyzed by the aforementioned analysis unit, A generation unit that generates an image of the bouquet coordinated by the aforementioned coordination unit, The system includes a purchasing unit that purchases and ships bouquets produced by the aforementioned production unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Equipped with special features based on seasonal and regional characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It includes a candidate presentation unit that presents multiple candidates. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned coordination unit is We have a proposal department that provides additional advice and suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system accepts information such as the sender's gender, age, and preferences for specific flowers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When acquiring input information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of acquiring input information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When retrieving input information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When acquiring input information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the input information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the input information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned coordination unit is It estimates the user's emotions and adjusts the coordination method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned coordination unit is When creating a coordinated outfit, the level of detail is adjusted based on the importance of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned coordination unit is When coordinating, different coordination algorithms are applied depending on the category of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned coordination unit is It estimates the user's emotions and determines the priority of the coordination based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned coordination unit is During the coordination process, the order of coordination will be adjusted based on when the input information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned coordination unit is When coordinating outfits, the order of the outfits is adjusted based on the relevance of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and adjusts the representation of the image diagrams generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the level of detail in the image is adjusted based on the importance of the coordinated bouquet. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, different generation algorithms are applied depending on the category of the coordinated bouquet. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and determines the priority of the image diagrams to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the order of the image diagrams is adjusted based on the timing of the delivery of the coordinated bouquet. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the order of the image diagrams is adjusted based on the relationships of the coordinated bouquets. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned purchasing department, It estimates the user's emotions and adjusts the purchase process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned purchasing department, When a user makes a purchase, we analyze their past purchase history and suggest the most suitable purchase method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned purchasing department, At the time of purchase, the purchase process is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned purchasing department, It estimates the user's emotions and determines the priority of the purchase process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned purchasing department, When making a purchase, the system will select the most suitable purchase procedure based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned purchasing department, When a user makes a purchase, we analyze their social media activity and suggest alternative purchase methods. The system described in Appendix 1, characterized by the features described herein. (Note 36) The characteristic section is, We estimate user sentiment and adjust the method to account for seasonal and regional characteristics based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 37) The characteristic section is, When performing analysis, we refer to past seasonal and regional data to improve the accuracy of the analysis. The system described in Appendix 2, characterized by the features described herein. (Note 38) The characteristic section is, The system estimates the user's emotions and adjusts the display method of the analysis results in the characteristics section based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The characteristic section is, When performing analysis, the accuracy of the analysis is improved by taking into account the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 40) The candidate presentation unit, It estimates the user's emotions and determines the priority of the suggested options based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The candidate presentation unit, When presenting candidates, adjust the level of detail based on the importance of the coordinated bouquet. The system described in Appendix 3, characterized by the features described herein. (Note 42) The candidate presentation unit, It estimates the user's emotions and adjusts how suggestions are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The candidate presentation unit, When presenting candidates, adjust the order of candidates based on the timing of the delivery of the coordinated bouquets. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way it provides additional advice and suggestions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned proposal section is, When making suggestions, we refer to the user's past input history to provide the most suitable advice and suggestions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned proposal section is, It estimates the user's emotions and prioritizes additional advice and suggestions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most appropriate advice and suggestions. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives user information, An analysis unit that analyzes the information received by the reception unit, A coordination unit that coordinates a bouquet based on the information analyzed by the aforementioned analysis unit, A generation unit that generates an image of the bouquet coordinated by the aforementioned coordination unit, The system includes a purchasing unit that purchases and ships bouquets produced by the aforementioned production unit. A system characterized by the following features.

2. The aforementioned analysis unit, Equipped with special features based on seasonal and regional characteristics. The system according to feature 1.

3. The generating unit is It includes a candidate presentation unit that presents multiple candidates. The system according to feature 1.

4. The aforementioned coordination unit is We have a proposal department that provides additional advice and suggestions. The system according to feature 1.

5. The aforementioned reception unit is The system accepts information such as the sender's gender, age, and preferences for specific flowers. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When acquiring input information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of acquiring input information based on the estimated emotions. The system according to feature 1.

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