System

Abstract

An object of a system according to an embodiment is to propose and generate an optimal bouquet on the basis of preferences and individuality of a person who receives the bouquet.SOLUTION: A system includes a reception unit, an analysis unit, a proposal unit, and a generation unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit, and analyzes the preferences and personality of the other party. The proposal unit proposes a bouquet based on the information analyzed by the analysis unit. The generation unit generates the bouquet proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to suggest a bouquet based on the recipient's preferences and personality, and there is room for improvement.

[0005] The system according to the embodiment aims to propose and create the most suitable bouquet based on the preferences and personality of the recipient. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a generation unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit and analyzes the recipient's preferences and personality. The proposal unit proposes a bouquet based on the information analyzed by the analysis unit. The generation unit generates the bouquet proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose and create the most suitable bouquet based on the preferences and personality of the recipient. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A custom flower gift production system according to an embodiment of the present invention utilizes a generation AI to create a custom flower gift based on the recipient's preferences and personality. The custom flower gift production system accepts information from a user, and the generation AI analyzes the information to analyze the recipient's preferences and personality. The generation AI then proposes the optimal bouquet design and flower materials based on the recipient's preferences and personality, and proposes a bouquet suitable for a special message or event. For example, in the custom flower gift production system, a user inputs information about the recipient, such as the recipient's name, age, gender, hobbies, favorite color, and data on previous bouquets. The custom flower gift production system then analyzes the input information using the generation AI to analyze the recipient's preferences and personality. The generation AI predicts the recipient's preference for bouquets based on past data and trends. For example, if the recipient prefers blue flowers, the system proposes a bouquet centered around blue flowers. The custom flower gift production system then uses the generation AI to propose the optimal bouquet design and flower materials based on the recipient's preferences and personality. For example, the system proposes a bouquet centered around blue flowers combined with flowers related to the recipient's gardening hobby. The system also proposes bouquets suitable for special messages and events. For example, it suggests bouquets suited to special events such as birthdays and wedding anniversaries. This allows the custom flower gift production system to eliminate the time-consuming process of creating a flower gift, and by choosing a bouquet that is more suitable for the recipient, it can increase the emotion and joy of the recipient. This allows the custom flower gift production system to simplify the creation of bouquet gifts, and by providing a bouquet that is more suitable for the recipient, it can maximize the emotion and joy of the recipient. For example, the user can simply choose a bouquet based on the generation AI's suggestions, eliminating the time-consuming process of creating a flower gift. It also makes special moments even more special by providing the recipient with deeper emotion and joy.

[0029] A custom flower gift production system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a generation unit. The reception unit accepts information from a user. The information from the user includes, but is not limited to, the recipient's name, age, gender, hobbies, favorite colors, and data on bouquets previously given. The reception unit, for example, stores the information entered by the user in a database and provides it to the analysis unit. The analysis unit uses a generation AI to analyze the information accepted by the reception unit and analyze the recipient's preferences and personality. The analysis is performed using, for example, data mining or a machine learning algorithm. The analysis unit can also predict the recipient's preferences and personality based on past data and trends. For example, the analysis unit can analyze the recipient's preferences and personality based on past purchase history and seasonal trends. The proposal unit uses the generation AI to propose a bouquet based on the information analyzed by the analysis unit. The proposal can, for example, suggest the optimal bouquet design and flower materials based on the recipient's preferences and personality. The proposal unit can also suggest bouquets suitable for special messages or events. For example, the suggestion unit suggests a bouquet suited to a special event such as a birthday or wedding anniversary. The generation unit generates the bouquet suggested by the suggestion unit. The generation is performed, for example, based on the bouquet design and the flower material selection method. The generation unit, for example, actually creates the suggested bouquet and provides it to the user. In this way, the custom flower gift production system according to the embodiment can suggest and generate an optimal bouquet based on user information. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to execute the bouquet design and flower material selection suggested by the suggestion unit.

[0030] The reception unit can accept data on the recipient's name, age, gender, hobbies, favorite color, and bouquets previously given. The reception unit, for example, stores data on the recipient's name, age, gender, hobbies, favorite color, and bouquets previously given, entered by the user, in a database. For example, the reception unit collects information from the user in the form of a questionnaire and stores the information in a database. The reception unit can also retrieve information previously entered by the user from the database and reuse the information. For example, the reception unit can refer to data on bouquets previously given by the user to understand the recipient's preferences and personality. By accepting more detailed information, a more personalized bouquet can be proposed. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can provide the information entered by the user to a generation AI, which then analyzes the information.

[0031] The analysis unit can analyze the recipient's preferences and personality based on past data or trends. The analysis unit can analyze the recipient's preferences and personality based on, for example, past data. For example, the analysis unit can refer to past purchase history and gift history to predict what type of bouquet the recipient will prefer. The analysis unit can also analyze the recipient's preferences and personality based on trend information. For example, the analysis unit can refer to seasonal trends and popular flower materials to predict what type of bouquet the recipient will prefer. The analysis unit can also combine past data and trend information to more accurately analyze the recipient's preferences and personality. For example, the analysis unit can combine past purchase history and seasonal trends to predict what type of bouquet the recipient will prefer. This enables more accurate analysis based on past data and trends. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can provide past data and trend information to the generation AI, which can then analyze the recipient's preferences and personality.

[0032] The suggestion unit can suggest a bouquet design or flower materials based on the recipient's preferences and personality. For example, the suggestion unit suggests the optimal bouquet design and flower materials based on the recipient's preferences and personality. For example, if the recipient likes blue flowers, the suggestion unit can suggest a bouquet centered around blue flowers. The suggestion unit can also suggest flower materials based on the recipient's hobbies and interests. For example, if the recipient enjoys gardening, the suggestion unit can suggest a bouquet that combines flower materials related to gardening. Furthermore, the suggestion unit can also suggest a bouquet suitable for a special message or event. For example, the suggestion unit can suggest a bouquet tailored to a special event such as a birthday or wedding anniversary. This makes it possible to make suggestions based on the recipient's preferences and personality. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can cause a generation AI to select a bouquet design and flower materials based on the recipient's preferences and personality.

[0033] The suggestion unit can suggest a bouquet suitable for a special message or event. The suggestion unit, for example, suggests a bouquet suitable for a special message or event. For example, the suggestion unit suggests a bouquet suited to a special event such as a birthday or a wedding anniversary. The suggestion unit can also add the recipient's name or a special message to the bouquet. For example, the suggestion unit suggests a message card with the recipient's name attached to the bouquet. The suggestion unit can also suggest a bouquet design and flower materials suitable for a special event. For example, the suggestion unit suggests a bouquet suited to a special event such as Christmas or Valentine's Day. This makes it possible to suggest a bouquet suitable for a special message or event. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can cause a generation AI to select a bouquet design and flower materials suitable for a special message or event.

[0034] The generation unit can generate a proposed bouquet. The generation unit, for example, actually generates the proposed bouquet. For example, the generation unit creates a bouquet based on the proposed bouquet design and flower material selection method. The generation unit can also provide the proposed bouquet to a user. For example, the generation unit creates the proposed bouquet and delivers it to the user. The generation unit can also customize the proposed bouquet design and flower material selection method. For example, the generation unit changes the bouquet design and flower materials according to the user's request. In this way, the proposed bouquet can actually be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can cause a generation AI to execute the proposed bouquet design and flower material selection.

[0035] The reception unit can analyze the user's past input history and provide an optimal input interface. The reception unit, for example, analyzes the user's past input history and provides an optimal input interface. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information to be used in a specific time period based on the user's past input history. For example, the reception unit provides an optimal input interface based on information that the user has input in a specific time period in the past. This makes it possible to provide an optimal interface based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide the user's past input history to a generation AI and cause the generation AI to suggest an optimal input interface.

[0036] The reception unit can customize input items based on the user's current situation and areas of interest when receiving information. For example, when receiving information, the reception unit customizes the input items based on the user's current situation and areas of interest. For example, if the user is participating in a specific event, the reception unit may preferentially display input items related to the event. Furthermore, if the user has a specific hobby, the reception unit may customize and display input items related to the hobby. Furthermore, when the user is in a specific location, the reception unit may automatically display input items related to the location. For example, if the user is traveling, the reception unit may preferentially display input items related to the travel destination. This allows input items to be provided according to the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may provide the user's current situation and areas of interest to a generation AI and cause the generation AI to customize the input items.

[0037] The reception unit can select an appropriate input means according to the user's input method when receiving information. For example, the reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.) when receiving information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Also, if the user selects text input, the reception unit can input information using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can input information using image recognition technology. For example, if the user uploads an image, the reception unit analyzes and inputs the information using image recognition technology. This makes it possible to provide the optimal means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0038] When receiving information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, when receiving information, the reception unit prioritizes receiving highly relevant information taking the user's geographical location information into consideration. For example, when the user is in a specific area, the reception unit prioritizes receiving information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving information related to the travel destination. Furthermore, when the user is participating in a specific event, the reception unit can prioritize receiving information related to the event. For example, when the user is participating in a specific event, the reception unit prioritizes receiving information related to the event. This makes it possible to provide information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may provide the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant information.

[0039] The reception unit can analyze the user's social media activity and accept related information when receiving information. For example, the reception unit can analyze the user's social media activity and accept related information when receiving information. For example, the reception unit can accept information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and accept related information. Furthermore, the reception unit can accept related information by referring to the activities of the user's friends on social media. For example, the reception unit can accept related information by referring to the activities of the user's friends on social media. This makes it possible to provide information based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can provide the user's social media activity to a generation AI and cause the generation AI to accept related information.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when receiving information. The reception unit, for example, customizes the input method by reflecting the user's past feedback when receiving information. For example, the reception unit suggests an optimal input method based on feedback previously provided by the user. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize and provide the input method. For example, the reception unit suggests an optimal input method based on feedback previously provided by the user. This makes it possible to provide an input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can provide the user's past feedback to a generation AI and cause the generation AI to customize the input method.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis based on past data or trends. The analysis unit, for example, more accurately analyzes the recipient's preferences and personality based on past data. For example, the analysis unit can refer to past purchase history and gift history to predict what type of bouquet the recipient will prefer. The analysis unit can also refer to trend information to perform an analysis that reflects the recipient's latest preferences and personality. For example, the analysis unit can refer to seasonal trends and popular flower materials to predict what type of bouquet the recipient will prefer. The analysis unit can also combine past data and trend information to provide optimal analysis results. For example, the analysis unit can combine past purchase history and seasonal trends to predict what type of bouquet the recipient will prefer. This enables improved analysis accuracy based on past data and trends. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can provide past data and trend information to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] The analysis unit can perform analysis based on the user's attribute information during analysis. The analysis unit customizes the analysis results, for example, by taking into account the user's age and gender. For example, the analysis unit suggests optimal bouquet designs and flower materials based on the user's age and gender. The analysis unit can also customize the analysis results by taking into account the user's hobbies and interests. For example, the analysis unit suggests optimal bouquet designs and flower materials based on the user's hobbies and interests. The analysis unit can also customize the analysis results by taking into account the user's past behavioral history. For example, the analysis unit can refer to the user's past purchase history and gift history to suggest optimal bouquet designs and flower materials. This enables analysis based on the user's attribute information. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can provide the user's attribute information to the generation AI and cause the generation AI to customize the analysis.

[0043] During analysis, the analysis unit can weight the analysis based on the frequency of user input. For example, the analysis unit assigns a higher analysis weight to information frequently input by the user. For example, the analysis unit prioritizes analysis of information frequently input by the user and assigns a higher importance to the information. The analysis unit can also assign a lower analysis weight to information rarely input by the user. For example, the analysis unit assigns a lower priority to information rarely input by the user and analyzes it. Furthermore, the analysis unit can adjust the reliability of the analysis results based on the frequency of user input. For example, the analysis unit assigns a higher reliability to the analysis results based on information frequently input by the user. This enables analysis based on the frequency of user input. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide the user's input frequency data to the generation AI and have the generation AI perform the analysis weighting.

[0044] During analysis, the analysis unit can perform analysis based on the user's geographical distribution. For example, if the user is in a specific region, the analysis unit prioritizes analyzing data related to that region. For example, if the user is in a specific region, the analysis unit prioritizes analyzing floral materials and designs related to that region. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing data related to the travel destination. For example, if the user is traveling, the analysis unit prioritizes analyzing floral materials and designs related to the travel destination. Furthermore, if the user is participating in a specific event, the analysis unit can prioritize analyzing data related to the event. For example, if the user is participating in a specific event, the analysis unit prioritizes analyzing floral materials and designs related to the event. This enables analysis based on the user's geographical distribution. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can provide the user's geographical distribution data to the generation AI and cause the generation AI to customize the analysis.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis based on related literature. For example, the analysis unit more accurately analyzes the recipient's preferences and personality based on related literature. For example, the analysis unit refers to related literature to predict what kind of bouquet the recipient will prefer. The analysis unit can also refer to related literature to perform an analysis that reflects the latest preferences and personality. For example, the analysis unit refers to the latest trends and popular flower materials based on related literature to predict what kind of bouquet the recipient will prefer. Furthermore, the analysis unit can combine related literature with past data to provide optimal analysis results. For example, the analysis unit combines related literature with past purchase history to predict what kind of bouquet the recipient will prefer. This enables improved analysis accuracy based on related literature. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can provide related literature to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The analysis unit can perform analysis based on the user's market value during analysis. The analysis unit, for example, adjusts the weighting of the analysis results based on the user's market value. For example, the analysis unit sets a high importance level for the analysis results in consideration of the user's market value. The analysis unit can also customize the analysis results in consideration of the user's market value. For example, the analysis unit suggests optimal bouquet designs and flower materials based on the user's market value. Furthermore, the analysis unit can provide optimal analysis results based on the user's market value. For example, the analysis unit suggests optimal bouquet designs and flower materials based on the user's market value. This enables analysis based on the user's market value. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can provide the user's market value data to the generation AI and have the generation AI customize the analysis.

[0047] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the bouquet. The proposal unit adjusts the level of detail of the proposal based on, for example, the importance of the bouquet. For example, the proposal unit makes detailed proposals in the case of an important event and proposes the most suitable bouquet. In addition, the proposal unit can make simple proposals in the case of an everyday gift and propose an easy-to-choose bouquet. Furthermore, when a special message is to be added, the proposal unit can make proposals tailored to the message. For example, the proposal unit proposes bouquet designs and flower materials that match the special message. This makes it possible to make proposals based on the importance of the bouquet. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can provide bouquet importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the bouquet category. The proposal unit applies different proposal algorithms depending on the bouquet category, for example. For example, in the case of a bouquet for a birthday, the proposal unit proposes flower materials suitable for the birthday. Furthermore, in the case of a bouquet for a wedding anniversary, the proposal unit can also propose flower materials suitable for the wedding anniversary. Furthermore, in the case of a bouquet for a sympathy gift, the proposal unit can also propose flower materials suitable for the sympathy gift. For example, the proposal unit proposes flower materials suitable for the sympathy gift. This makes it possible to make proposals according to the bouquet category. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI, for example. For example, the proposal unit may provide bouquet category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0049] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion by referring to, for example, the user's past suggestion results. For example, the suggestion unit makes an optimal suggestion based on bouquets selected by the user in the past. The suggestion unit can also analyze the user's preferences and trends from the user's past suggestion results to improve the accuracy of the suggestion. Furthermore, the suggestion unit can suggest an optimal bouquet by referring to the user's past suggestion results. For example, the suggestion unit can suggest an optimal bouquet design and flower materials based on the user's past suggestion results. This makes it possible to improve the accuracy of the suggestion based on the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide the user's past suggestion result data to the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0050] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of bouquet submission. The suggestion unit, for example, determines the priority of the suggestions based on the time of bouquet submission. For example, if an important event is approaching, the suggestion unit prioritizes suggestions related to that event. In addition, for everyday gifts, the suggestion unit can also prioritize suggestions equal to other suggestions. Furthermore, if a special message is attached, the suggestion unit can prioritize suggestions tailored to the message. For example, the suggestion unit suggests bouquet designs and flower materials that match the special message. This enables suggestions based on the time of bouquet submission. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can provide bouquet submission time data to the generation AI and cause the generation AI to determine the priority of the suggestions.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the bouquets when making suggestions. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the bouquets. For example, the suggestion unit first suggests the bouquet most relevant to the user's preferences. The suggestion unit can also preferentially suggest bouquets with high relevance based on the user's past selection history. Furthermore, the suggestion unit can preferentially suggest bouquets related to the user's current situation or event. For example, the suggestion unit preferentially suggests bouquets related to the user's current situation or event. This enables suggestions based on the relevance of bouquets. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide bouquet relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is knowledgeable about flowers, the suggestion unit may make a proposal that uses a lot of technical terminology. Furthermore, if the user is not knowledgeable about flowers, the suggestion unit may make a proposal that explains the proposal in simple terms. Furthermore, the suggestion unit may make a proposal using appropriate terminology according to the user's level of expertise. For example, the suggestion unit may make a proposal using appropriate terminology according to the user's level of expertise. This enables a proposal that is appropriate for the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may provide the user's level of expertise data to the generation AI and cause the generation AI to use technical terminology in the proposal.

[0053] During generation, the generation unit can analyze the user's past consumption behavior and select an appropriate generation method. The generation unit, for example, analyzes the user's past consumption behavior and selects the optimal generation method. For example, the generation unit selects the optimal generation method based on data on bouquets the user previously purchased. The generation unit can also analyze the user's preferences and trends from the user's past consumption behavior and select the optimal generation method. Furthermore, the generation unit can generate an optimal bouquet by referring to the user's past consumption behavior. For example, the generation unit selects the optimal bouquet design and flower materials based on the user's past consumption behavior. This makes it possible to provide an optimal generation method based on the user's past consumption behavior. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide the user's past consumption behavior data to the generation AI and have the generation AI select the generation method.

[0054] The generation unit can customize the generation means based on the user's current living situation at the time of generation. The generation unit customizes the generation means based on the user's current living situation, for example. For example, if the user is busy, the generation unit provides a means for quickly generating a bouquet. Also, if the user is relaxed, the generation unit can provide a means for generating a bouquet at a leisurely pace. Furthermore, the generation unit can provide an optimal generation means according to the user's current living situation. For example, the generation unit provides an optimal bouquet generation means based on the user's lifestyle and family composition. This makes it possible to provide a generation means according to the user's living situation. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can provide the user's living situation data to the generation AI and cause the generation AI to customize the generation means.

[0055] The generation unit can improve the generation method by reflecting user feedback during generation. The generation unit improves the generation method, for example, based on feedback provided by the user. For example, the generation unit identifies problems with the generation method from the user feedback and improves it. The generation unit can also provide an optimal generation method by referring to the user feedback. Furthermore, the generation unit can customize the generation method based on the user feedback. For example, the generation unit selects an optimal bouquet design and flower materials based on the user feedback. This makes it possible to improve the generation method based on the user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide user feedback data to the generation AI and cause the generation AI to improve the generation method.

[0056] The generation unit can select an appropriate generation method based on the user's geographical location information at the time of generation. For example, if the user is in a specific area, the generation unit generates a bouquet using flower materials related to that area. For example, if the user is in a specific area, the generation unit generates a bouquet using flower materials related to that area. Furthermore, if the user is traveling, the generation unit can generate a bouquet using flower materials related to the travel destination. Furthermore, if the user is participating in a specific event, the generation unit can generate a bouquet using flower materials related to the event. For example, if the user is participating in a specific event, the generation unit generates a bouquet using flower materials related to the event. This makes it possible to provide a generation method based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide the user's geographical location information data to the generation AI and cause the generation AI to select a generation method.

[0057] At the time of generation, the generation unit can analyze the user's social media activity and suggest a generation method. The generation unit, for example, analyzes the user's social media activity and suggests a generation method. For example, the generation unit generates a bouquet using flowers related to a location where the user checked in on social media. The generation unit can also analyze the user's social media posts and generate a bouquet using related flowers. Furthermore, the generation unit can generate a bouquet using related flowers based on the activities of the user's friends on social media. For example, the generation unit generates a bouquet using related flowers based on the activities of the user's friends on social media. This makes it possible to provide a generation method based on the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide the user's social media activity data to the generation AI and cause the generation AI to execute the generation method suggestion.

[0058] The generation unit can customize the generation method by reflecting the user's past feedback during generation. The generation unit customizes the generation method, for example, based on feedback provided by the user in the past. For example, the generation unit preferentially suggests a specific generation method based on the user's past feedback. The generation unit can also analyze the user's past feedback and provide an optimal generation method. Furthermore, the generation unit can customize the generation method based on the user's past feedback. For example, the generation unit selects an optimal bouquet design and flower materials based on the user's past feedback. This makes it possible to provide a generation method based on the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide the user's past feedback data to the generation AI and cause the generation AI to customize the generation method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The custom flower gift production system may further include a delivery unit. After the generation unit generates the bouquet suggested by the suggestion unit, the delivery unit delivers the bouquet to a delivery destination specified by the user. For example, the delivery unit calculates the optimal delivery route based on address information entered by the user and delivers the bouquet promptly. The delivery unit can also track the delivery status in real time and notify the user. Furthermore, the delivery unit can deliver the bouquet at a specified date and time to coincide with a special event or anniversary. This saves the user the trouble of having to deliver the bouquet and ensures that the bouquet is delivered to the recipient reliably.

[0061] The reception unit can accept voice input from the user. For example, the reception unit allows the user to input the other person's name and preferences by voice, and converts the information into text data using voice recognition technology. The reception unit also provides the information the user has input by voice to the analysis unit, which can then analyze the other person's preferences and personality based on that information. Furthermore, the reception unit can save the information the user has input by voice and reuse it later. This allows the user to input information easily and enables smoother operation.

[0062] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit makes the optimal suggestion based on bouquets selected by the user in the past. The suggestion unit can also analyze the user's preferences and tendencies from the user's past suggestion results to improve the accuracy of suggestions. Furthermore, the suggestion unit can also suggest the optimal bouquet by referring to the user's past suggestion results. This makes it possible to improve the accuracy of suggestions based on the past suggestion results.

[0063] The generation unit can analyze the user's past consumption behavior and select an appropriate generation method. For example, the generation unit selects the optimal generation method based on data on bouquets purchased by the user in the past. The generation unit can also analyze the user's preferences and tendencies from the user's past consumption behavior and select the optimal generation method. Furthermore, the generation unit can generate an optimal bouquet by referring to the user's past consumption behavior. This makes it possible to provide an optimal generation method based on the user's past consumption behavior.

[0064] The generation unit can improve the generation method by reflecting user feedback. For example, the generation unit identifies problems with the generation method from user feedback and improves it. The generation unit can also provide an optimal generation method by referring to user feedback. Furthermore, the generation unit can customize the generation method based on user feedback. This makes it possible to improve the generation method based on user feedback.

[0065] The generation unit can select an appropriate generation method based on the user's geographical location information. For example, if the user is in a specific area, the generation unit can generate a bouquet using flowers related to that area. If the user is traveling, the generation unit can also generate a bouquet using flowers related to the travel destination. Furthermore, if the user is participating in a specific event, the generation unit can also generate a bouquet using flowers related to the event. This makes it possible to provide a generation method based on the user's geographical location information.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives information from the user. This information includes the recipient's name, age, gender, hobbies, favorite color, and data on bouquets sent in the past. The reception unit stores the information entered by the user in a database and provides it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and analyze the recipient's preferences and personality. The analysis is performed using data mining and machine learning algorithms. The analysis unit can also predict the recipient's preferences and personality based on past data and trends. For example, it can analyze the type of bouquet the recipient will prefer by referring to past purchase history and seasonal trends. Step 3: The proposal unit uses the generative AI to propose bouquets based on the information analyzed by the analysis unit. The proposal suggests the optimal bouquet design and flower materials based on the recipient's preferences and personality. It can also suggest bouquets suitable for special messages or events. For example, it suggests bouquets for special events such as birthdays and wedding anniversaries. Step 4: The generation unit generates the bouquet proposed by the proposal unit. The generation is performed based on the bouquet design and the flower material selection method. The generation unit actually creates the proposed bouquet and provides it to the user. Some or all of the processing in the generation unit may be performed using AI, or may be performed without using AI.

[0068] (Example 2) A custom flower gift production system according to an embodiment of the present invention utilizes a generation AI to create a custom flower gift based on the recipient's preferences and personality. The custom flower gift production system accepts information from a user, and the generation AI analyzes the information to analyze the recipient's preferences and personality. The generation AI then proposes the optimal bouquet design and flower materials based on the recipient's preferences and personality, and proposes a bouquet suitable for a special message or event. For example, in the custom flower gift production system, a user inputs information about the recipient, such as the recipient's name, age, gender, hobbies, favorite color, and data on previous bouquets. The custom flower gift production system then analyzes the input information using the generation AI to analyze the recipient's preferences and personality. The generation AI predicts the recipient's preference for bouquets based on past data and trends. For example, if the recipient prefers blue flowers, the system proposes a bouquet centered around blue flowers. The custom flower gift production system then uses the generation AI to propose the optimal bouquet design and flower materials based on the recipient's preferences and personality. For example, the system proposes a bouquet centered around blue flowers combined with flowers related to the recipient's gardening hobby. The system also proposes bouquets suitable for special messages and events. For example, it suggests bouquets suited to special events such as birthdays and wedding anniversaries. This allows the custom flower gift production system to eliminate the time-consuming process of creating a flower gift, and by choosing a bouquet that is more suitable for the recipient, it can increase the emotion and joy of the recipient. This allows the custom flower gift production system to simplify the creation of bouquet gifts, and by providing a bouquet that is more suitable for the recipient, it can maximize the emotion and joy of the recipient. For example, the user can simply choose a bouquet based on the generation AI's suggestions, eliminating the time-consuming process of creating a flower gift. It also makes special moments even more special by providing the recipient with deeper emotion and joy.

[0069] A custom flower gift production system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a generation unit. The reception unit accepts information from a user. The information from the user includes, but is not limited to, the recipient's name, age, gender, hobbies, favorite colors, and data on bouquets previously given. The reception unit, for example, stores the information entered by the user in a database and provides it to the analysis unit. The analysis unit uses a generation AI to analyze the information accepted by the reception unit and analyze the recipient's preferences and personality. The analysis is performed using, for example, data mining or a machine learning algorithm. The analysis unit can also predict the recipient's preferences and personality based on past data and trends. For example, the analysis unit can analyze the recipient's preferences and personality based on past purchase history and seasonal trends. The proposal unit uses the generation AI to propose a bouquet based on the information analyzed by the analysis unit. The proposal can, for example, suggest the optimal bouquet design and flower materials based on the recipient's preferences and personality. The proposal unit can also suggest bouquets suitable for special messages or events. For example, the suggestion unit suggests a bouquet suited to a special event such as a birthday or wedding anniversary. The generation unit generates the bouquet suggested by the suggestion unit. The generation is performed, for example, based on the bouquet design and the flower material selection method. The generation unit, for example, actually creates the suggested bouquet and provides it to the user. In this way, the custom flower gift production system according to the embodiment can suggest and generate an optimal bouquet based on user information. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can cause a generation AI to execute the bouquet design and flower material selection suggested by the suggestion unit.

[0070] The reception unit can accept data on the recipient's name, age, gender, hobbies, favorite color, and bouquets previously given. The reception unit, for example, stores data on the recipient's name, age, gender, hobbies, favorite color, and bouquets previously given, entered by the user, in a database. For example, the reception unit collects information from the user in the form of a questionnaire and stores the information in a database. The reception unit can also retrieve information previously entered by the user from the database and reuse the information. For example, the reception unit can refer to data on bouquets previously given by the user to understand the recipient's preferences and personality. By accepting more detailed information, a more personalized bouquet can be proposed. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can provide the information entered by the user to a generation AI, which then analyzes the information.

[0071] The analysis unit can analyze the recipient's preferences and personality based on past data or trends. The analysis unit can analyze the recipient's preferences and personality based on, for example, past data. For example, the analysis unit can refer to past purchase history and gift history to predict what type of bouquet the recipient will prefer. The analysis unit can also analyze the recipient's preferences and personality based on trend information. For example, the analysis unit can refer to seasonal trends and popular flower materials to predict what type of bouquet the recipient will prefer. The analysis unit can also combine past data and trend information to more accurately analyze the recipient's preferences and personality. For example, the analysis unit can combine past purchase history and seasonal trends to predict what type of bouquet the recipient will prefer. This enables more accurate analysis based on past data and trends. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can provide past data and trend information to the generation AI, which can then analyze the recipient's preferences and personality.

[0072] The suggestion unit can suggest a bouquet design or flower materials based on the recipient's preferences and personality. For example, the suggestion unit suggests the optimal bouquet design and flower materials based on the recipient's preferences and personality. For example, if the recipient likes blue flowers, the suggestion unit can suggest a bouquet centered around blue flowers. The suggestion unit can also suggest flower materials based on the recipient's hobbies and interests. For example, if the recipient enjoys gardening, the suggestion unit can suggest a bouquet that combines flower materials related to gardening. Furthermore, the suggestion unit can also suggest a bouquet suitable for a special message or event. For example, the suggestion unit can suggest a bouquet tailored to a special event such as a birthday or wedding anniversary. This makes it possible to make suggestions based on the recipient's preferences and personality. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can cause a generation AI to select a bouquet design and flower materials based on the recipient's preferences and personality.

[0073] The suggestion unit can suggest a bouquet suitable for a special message or event. The suggestion unit, for example, suggests a bouquet suitable for a special message or event. For example, the suggestion unit suggests a bouquet suited to a special event such as a birthday or a wedding anniversary. The suggestion unit can also add the recipient's name or a special message to the bouquet. For example, the suggestion unit suggests a message card with the recipient's name attached to the bouquet. The suggestion unit can also suggest a bouquet design and flower materials suitable for a special event. For example, the suggestion unit suggests a bouquet suited to a special event such as Christmas or Valentine's Day. This makes it possible to suggest a bouquet suitable for a special message or event. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can cause a generation AI to select a bouquet design and flower materials suitable for a special message or event.

[0074] The generation unit can generate a proposed bouquet. The generation unit, for example, actually generates the proposed bouquet. For example, the generation unit creates a bouquet based on the proposed bouquet design and flower material selection method. The generation unit can also provide the proposed bouquet to a user. For example, the generation unit creates the proposed bouquet and delivers it to the user. The generation unit can also customize the proposed bouquet design and flower material selection method. For example, the generation unit changes the bouquet design and flower materials according to the user's request. In this way, the proposed bouquet can actually be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can cause a generation AI to execute the proposed bouquet design and flower material selection.

[0075] The reception unit can estimate the user's emotions and adjust the information input method based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the information input method based on the estimated user emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. For example, if the user is in a hurry, the reception unit can input information using voice recognition technology. This makes it possible to provide an input method that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0076] The reception unit can analyze the user's past input history and provide an optimal input interface. The reception unit, for example, analyzes the user's past input history and provides an optimal input interface. For example, the reception unit automatically displays information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest information to be used in a specific time period based on the user's past input history. For example, the reception unit provides an optimal input interface based on information that the user has input in a specific time period in the past. This makes it possible to provide an optimal interface based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide the user's past input history to a generation AI and cause the generation AI to suggest an optimal input interface.

[0077] The reception unit can customize input items based on the user's current situation and areas of interest when receiving information. For example, when receiving information, the reception unit customizes the input items based on the user's current situation and areas of interest. For example, if the user is participating in a specific event, the reception unit may preferentially display input items related to the event. Furthermore, if the user has a specific hobby, the reception unit may customize and display input items related to the hobby. Furthermore, when the user is in a specific location, the reception unit may automatically display input items related to the location. For example, if the user is traveling, the reception unit may preferentially display input items related to the travel destination. This allows input items to be provided according to the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may provide the user's current situation and areas of interest to a generation AI and cause the generation AI to customize the input items.

[0078] The reception unit can select an appropriate input means according to the user's input method when receiving information. For example, the reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.) when receiving information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Also, if the user selects text input, the reception unit can input information using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can input information using image recognition technology. For example, if the user uploads an image, the reception unit analyzes and inputs the information using image recognition technology. This makes it possible to provide the optimal means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0079] The reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, when the user is nervous, the reception unit can prioritize important input items and postpone other items. Furthermore, when the user is relaxed, the reception unit can display detailed input items in order. Furthermore, when the user is in a hurry, the reception unit can display only the most important input items to allow the user to complete input quickly. For example, when the user is in a hurry, the reception unit can prioritize important input items using voice recognition technology. This allows the prioritization of input items according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0080] When receiving information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, when receiving information, the reception unit prioritizes receiving highly relevant information taking the user's geographical location information into consideration. For example, when the user is in a specific area, the reception unit prioritizes receiving information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving information related to the travel destination. Furthermore, when the user is participating in a specific event, the reception unit can prioritize receiving information related to the event. For example, when the user is participating in a specific event, the reception unit prioritizes receiving information related to the event. This makes it possible to provide information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may provide the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant information.

[0081] The reception unit can analyze the user's social media activity and accept related information when receiving information. For example, the reception unit can analyze the user's social media activity and accept related information when receiving information. For example, the reception unit can accept information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and accept related information. Furthermore, the reception unit can accept related information by referring to the activities of the user's friends on social media. For example, the reception unit can accept related information by referring to the activities of the user's friends on social media. This makes it possible to provide information based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can provide the user's social media activity to a generation AI and cause the generation AI to accept related information.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when receiving information. The reception unit, for example, customizes the input method by reflecting the user's past feedback when receiving information. For example, the reception unit suggests an optimal input method based on feedback previously provided by the user. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize and provide the input method. For example, the reception unit suggests an optimal input method based on feedback previously provided by the user. This makes it possible to provide an input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can provide the user's past feedback to a generation AI and cause the generation AI to customize the input method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide results quickly. Furthermore, if the user is excited, the analysis unit can provide visually easy-to-understand analysis results. For example, if the user is excited, the analysis unit can provide analysis results using visually easy-to-understand graphs or charts. This allows for providing an analysis algorithm that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis based on past data or trends. The analysis unit, for example, more accurately analyzes the recipient's preferences and personality based on past data. For example, the analysis unit can refer to past purchase history and gift history to predict what type of bouquet the recipient will prefer. The analysis unit can also refer to trend information to perform an analysis that reflects the recipient's latest preferences and personality. For example, the analysis unit can refer to seasonal trends and popular flower materials to predict what type of bouquet the recipient will prefer. The analysis unit can also combine past data and trend information to provide optimal analysis results. For example, the analysis unit can combine past purchase history and seasonal trends to predict what type of bouquet the recipient will prefer. This enables improved analysis accuracy based on past data and trends. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can provide past data and trend information to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The analysis unit can perform analysis based on the user's attribute information during analysis. The analysis unit customizes the analysis results, for example, by taking into account the user's age and gender. For example, the analysis unit suggests optimal bouquet designs and flower materials based on the user's age and gender. The analysis unit can also customize the analysis results by taking into account the user's hobbies and interests. For example, the analysis unit suggests optimal bouquet designs and flower materials based on the user's hobbies and interests. The analysis unit can also customize the analysis results by taking into account the user's past behavioral history. For example, the analysis unit can refer to the user's past purchase history and gift history to suggest optimal bouquet designs and flower materials. This enables analysis based on the user's attribute information. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can provide the user's attribute information to the generation AI and cause the generation AI to customize the analysis.

[0086] During analysis, the analysis unit can weight the analysis based on the frequency of user input. For example, the analysis unit assigns a higher analysis weight to information frequently input by the user. For example, the analysis unit prioritizes analysis of information frequently input by the user and assigns a higher importance to the information. The analysis unit can also assign a lower analysis weight to information rarely input by the user. For example, the analysis unit assigns a lower priority to information rarely input by the user and analyzes it. Furthermore, the analysis unit can adjust the reliability of the analysis results based on the frequency of user input. For example, the analysis unit assigns a higher reliability to the analysis results based on information frequently input by the user. This enables analysis based on the frequency of user input. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide the user's input frequency data to the generation AI and have the generation AI perform the analysis weighting.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is in a hurry, the analysis unit provides a concise display method that focuses on the main points. This makes it possible to provide a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can provide the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0088] During analysis, the analysis unit can perform analysis based on the user's geographical distribution. For example, if the user is in a specific region, the analysis unit prioritizes analyzing data related to that region. For example, if the user is in a specific region, the analysis unit prioritizes analyzing floral materials and designs related to that region. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing data related to the travel destination. For example, if the user is traveling, the analysis unit prioritizes analyzing floral materials and designs related to the travel destination. Furthermore, if the user is participating in a specific event, the analysis unit can prioritize analyzing data related to the event. For example, if the user is participating in a specific event, the analysis unit prioritizes analyzing floral materials and designs related to the event. This enables analysis based on the user's geographical distribution. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can provide the user's geographical distribution data to the generation AI and cause the generation AI to customize the analysis.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis based on related literature. For example, the analysis unit more accurately analyzes the recipient's preferences and personality based on related literature. For example, the analysis unit refers to related literature to predict what kind of bouquet the recipient will prefer. The analysis unit can also refer to related literature to perform an analysis that reflects the latest preferences and personality. For example, the analysis unit refers to the latest trends and popular flower materials based on related literature to predict what kind of bouquet the recipient will prefer. Furthermore, the analysis unit can combine related literature with past data to provide optimal analysis results. For example, the analysis unit combines related literature with past purchase history to predict what kind of bouquet the recipient will prefer. This enables improved analysis accuracy based on related literature. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can provide related literature to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0090] The analysis unit can perform analysis based on the user's market value during analysis. The analysis unit, for example, adjusts the weighting of the analysis results based on the user's market value. For example, the analysis unit sets a high importance level for the analysis results in consideration of the user's market value. The analysis unit can also customize the analysis results in consideration of the user's market value. For example, the analysis unit suggests optimal bouquet designs and flower materials based on the user's market value. Furthermore, the analysis unit can provide optimal analysis results based on the user's market value. For example, the analysis unit suggests optimal bouquet designs and flower materials based on the user's market value. This enables analysis based on the user's market value. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can provide the user's market value data to the generation AI and have the generation AI customize the analysis.

[0091] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. Also, if the user is relaxed, the suggestion unit can provide a suggestion method including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. For example, if the user is in a hurry, the suggestion unit can provide a concise suggestion method that focuses on the main points. This makes it possible to provide a suggestion method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using an AI, for example, or without an AI. For example, the suggestion unit can provide the user's emotion data to the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0092] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the bouquet. The proposal unit adjusts the level of detail of the proposal based on, for example, the importance of the bouquet. For example, the proposal unit makes detailed proposals in the case of an important event and proposes the most suitable bouquet. In addition, the proposal unit can make simple proposals in the case of an everyday gift and propose an easy-to-choose bouquet. Furthermore, when a special message is to be added, the proposal unit can make proposals tailored to the message. For example, the proposal unit proposes bouquet designs and flower materials that match the special message. This makes it possible to make proposals based on the importance of the bouquet. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can provide bouquet importance data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0093] When making a proposal, the proposal unit can apply different proposal algorithms depending on the bouquet category. The proposal unit applies different proposal algorithms depending on the bouquet category, for example. For example, in the case of a bouquet for a birthday, the proposal unit proposes flower materials suitable for the birthday. Furthermore, in the case of a bouquet for a wedding anniversary, the proposal unit can also propose flower materials suitable for the wedding anniversary. Furthermore, in the case of a bouquet for a sympathy gift, the proposal unit can also propose flower materials suitable for the sympathy gift. For example, the proposal unit proposes flower materials suitable for the sympathy gift. This makes it possible to make proposals according to the bouquet category. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI, for example. For example, the proposal unit may provide bouquet category data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0094] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion by referring to, for example, the user's past suggestion results. For example, the suggestion unit makes an optimal suggestion based on bouquets selected by the user in the past. The suggestion unit can also analyze the user's preferences and trends from the user's past suggestion results to improve the accuracy of the suggestion. Furthermore, the suggestion unit can suggest an optimal bouquet by referring to the user's past suggestion results. For example, the suggestion unit can suggest an optimal bouquet design and flower materials based on the user's past suggestion results. This makes it possible to improve the accuracy of the suggestion based on the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide the user's past suggestion result data to the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows the length of the suggestions to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can provide the user's emotion data to the generation AI and cause the generation AI to adjust the length of the suggestions.

[0096] When making suggestions, the suggestion unit can determine the priority of the suggestions based on the time of bouquet submission. The suggestion unit, for example, determines the priority of the suggestions based on the time of bouquet submission. For example, if an important event is approaching, the suggestion unit prioritizes suggestions related to that event. In addition, for everyday gifts, the suggestion unit can also prioritize suggestions equal to other suggestions. Furthermore, if a special message is attached, the suggestion unit can prioritize suggestions tailored to the message. For example, the suggestion unit suggests bouquet designs and flower materials that match the special message. This enables suggestions based on the time of bouquet submission. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can provide bouquet submission time data to the generation AI and cause the generation AI to determine the priority of the suggestions.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the bouquets when making suggestions. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the bouquets. For example, the suggestion unit first suggests the bouquet most relevant to the user's preferences. The suggestion unit can also preferentially suggest bouquets with high relevance based on the user's past selection history. Furthermore, the suggestion unit can preferentially suggest bouquets related to the user's current situation or event. For example, the suggestion unit preferentially suggests bouquets related to the user's current situation or event. This enables suggestions based on the relevance of bouquets. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide bouquet relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0098] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is knowledgeable about flowers, the suggestion unit may make a proposal that uses a lot of technical terminology. Furthermore, if the user is not knowledgeable about flowers, the suggestion unit may make a proposal that explains the proposal in simple terms. Furthermore, the suggestion unit may make a proposal using appropriate terminology according to the user's level of expertise. For example, the suggestion unit may make a proposal using appropriate terminology according to the user's level of expertise. This enables a proposal that is appropriate for the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may provide the user's level of expertise data to the generation AI and cause the generation AI to use technical terminology in the proposal.

[0099] The generation unit can estimate the user's emotion and adjust the bouquet generation method based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the bouquet generation method based on the estimated user's emotion. For example, if the user is relaxed, the generation unit generates a bouquet that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can also generate a bouquet that quickly progresses. Furthermore, if the user is excited, the generation unit can generate a bouquet that adds a visually stimulating effect. For example, if the user is excited, the generation unit generates a bouquet that adds a visually stimulating effect. This provides a method for generating a bouquet that corresponds to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can provide the user's emotion data to the generation AI and cause the generation AI to adjust the bouquet generation method.

[0100] During generation, the generation unit can analyze the user's past consumption behavior and select an appropriate generation method. The generation unit, for example, analyzes the user's past consumption behavior and selects the optimal generation method. For example, the generation unit selects the optimal generation method based on data on bouquets the user previously purchased. The generation unit can also analyze the user's preferences and trends from the user's past consumption behavior and select the optimal generation method. Furthermore, the generation unit can generate an optimal bouquet by referring to the user's past consumption behavior. For example, the generation unit selects the optimal bouquet design and flower materials based on the user's past consumption behavior. This makes it possible to provide an optimal generation method based on the user's past consumption behavior. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide the user's past consumption behavior data to the generation AI and have the generation AI select the generation method.

[0101] The generation unit can customize the generation means based on the user's current living situation at the time of generation. The generation unit customizes the generation means based on the user's current living situation, for example. For example, if the user is busy, the generation unit provides a means for quickly generating a bouquet. Also, if the user is relaxed, the generation unit can provide a means for generating a bouquet at a leisurely pace. Furthermore, the generation unit can provide an optimal generation means according to the user's current living situation. For example, the generation unit provides an optimal bouquet generation means based on the user's lifestyle and family composition. This makes it possible to provide a generation means according to the user's living situation. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can provide the user's living situation data to the generation AI and cause the generation AI to customize the generation means.

[0102] The generation unit can improve the generation method by reflecting user feedback during generation. The generation unit improves the generation method, for example, based on feedback provided by the user. For example, the generation unit identifies problems with the generation method from the user feedback and improves it. The generation unit can also provide an optimal generation method by referring to the user feedback. Furthermore, the generation unit can customize the generation method based on the user feedback. For example, the generation unit selects an optimal bouquet design and flower materials based on the user feedback. This makes it possible to improve the generation method based on the user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide user feedback data to the generation AI and cause the generation AI to improve the generation method.

[0103] The generation unit can estimate the user's emotions and determine the priority of bouquets to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and determines the priority of bouquets to be generated based on the estimated user emotions. For example, if the user is nervous, the generation unit can prioritize generating a simple, highly visible bouquet. Furthermore, if the user is relaxed, the generation unit can prioritize generating a bouquet with a detailed design. Furthermore, if the user is in a hurry, the generation unit can prioritize generating a bouquet that can be generated quickly. For example, if the user is in a hurry, the generation unit prioritizes generating a bouquet that can be generated quickly. This allows the priority of bouquets to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can provide the user's emotion data to the generation AI and have the generation AI determine the priority of the bouquets.

[0104] The generation unit can select an appropriate generation method based on the user's geographical location information at the time of generation. For example, if the user is in a specific area, the generation unit generates a bouquet using flower materials related to that area. For example, if the user is in a specific area, the generation unit generates a bouquet using flower materials related to that area. Furthermore, if the user is traveling, the generation unit can generate a bouquet using flower materials related to the travel destination. Furthermore, if the user is participating in a specific event, the generation unit can generate a bouquet using flower materials related to the event. For example, if the user is participating in a specific event, the generation unit generates a bouquet using flower materials related to the event. This makes it possible to provide a generation method based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide the user's geographical location information data to the generation AI and cause the generation AI to select a generation method.

[0105] At the time of generation, the generation unit can analyze the user's social media activity and suggest a generation method. The generation unit, for example, analyzes the user's social media activity and suggests a generation method. For example, the generation unit generates a bouquet using flowers related to a location where the user checked in on social media. The generation unit can also analyze the user's social media posts and generate a bouquet using related flowers. Furthermore, the generation unit can generate a bouquet using related flowers based on the activities of the user's friends on social media. For example, the generation unit generates a bouquet using related flowers based on the activities of the user's friends on social media. This makes it possible to provide a generation method based on the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide the user's social media activity data to the generation AI and cause the generation AI to execute the generation method suggestion.

[0106] The generation unit can customize the generation method by reflecting the user's past feedback during generation. The generation unit customizes the generation method, for example, based on feedback provided by the user in the past. For example, the generation unit preferentially suggests a specific generation method based on the user's past feedback. The generation unit can also analyze the user's past feedback and provide an optimal generation method. Furthermore, the generation unit can customize the generation method based on the user's past feedback. For example, the generation unit selects an optimal bouquet design and flower materials based on the user's past feedback. This makes it possible to provide a generation method based on the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can provide the user's past feedback data to the generation AI and cause the generation AI to customize the generation method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can accept information from a user using the reception device 38 of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and analyzes the information from the user using a generation AI to analyze the recipient's preferences and personality. The suggestion unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal bouquet designs and flower materials based on the information analyzed by the analysis unit. The generation unit can be realized, for example, by the control unit 46A of the smart device 14, and actually creates the proposed bouquet and provides it to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive information from the user using the microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and analyzes the information from the user using a generation AI to analyze the recipient's preferences and personality. The suggestion unit can be realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal bouquet designs and flower materials based on the information analyzed by the analysis unit. The generation unit can be realized, for example, by the control unit 46A of the smart glasses 214, and actually creates the suggested bouquet and provides it to the user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, suggestion unit, and generation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive information from the user using the microphone 238 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information from the user using a generation AI to analyze the recipient's preferences and personality. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal bouquet designs and flower materials based on the information analyzed by the analysis unit. The generation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and actually creates the suggested bouquet and provides it to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and generation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive information from the user using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the information from the user using a generation AI to analyze the recipient's preferences and personality. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal bouquet designs and flower materials based on the information analyzed by the analysis unit. The generation unit is realized, for example, by the control unit 46A of the robot 414 and actually creates the suggested bouquet and provides it to the user.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The custom flower gift production system may further include a delivery unit. After the generation unit generates the bouquet suggested by the suggestion unit, the delivery unit delivers the bouquet to a delivery destination specified by the user. For example, the delivery unit calculates the optimal delivery route based on address information entered by the user and delivers the bouquet promptly. The delivery unit can also track the delivery status in real time and notify the user. Furthermore, the delivery unit can deliver the bouquet at a specified date and time to coincide with a special event or anniversary. This saves the user the trouble of having to deliver the bouquet and ensures that the bouquet is delivered to the recipient reliably.

[0109] The reception unit can accept voice input from the user. For example, the reception unit allows the user to input the other person's name and preferences by voice, and converts the information into text data using voice recognition technology. The reception unit also provides the information the user has input by voice to the analysis unit, which can then analyze the other person's preferences and personality based on that information. Furthermore, the reception unit can save the information the user has input by voice and reuse it later. This allows the user to input information easily and enables smoother operation.

[0110] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is in a hurry, the analysis unit can also perform a quick analysis and provide results quickly. Furthermore, if the user is excited, the analysis unit can provide analysis results that are visually easy to understand. This makes it possible to perform analysis according to the user's emotions.

[0111] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. If the user is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a suggestion method that focuses on the main points. This makes it possible to make suggestions according to the user's emotions.

[0112] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit makes the optimal suggestion based on bouquets selected by the user in the past. The suggestion unit can also analyze the user's preferences and tendencies from the user's past suggestion results to improve the accuracy of suggestions. Furthermore, the suggestion unit can also suggest the optimal bouquet by referring to the user's past suggestion results. This makes it possible to improve the accuracy of suggestions based on the past suggestion results.

[0113] The generation unit can estimate the user's emotions and adjust the bouquet generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a bouquet that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a bouquet that progresses quickly. Furthermore, if the user is excited, the generation unit can also generate a bouquet that adds a visually stimulating effect. This makes it possible to provide a bouquet generation method that corresponds to the user's emotions.

[0114] The generation unit can analyze the user's past consumption behavior and select an appropriate generation method. For example, the generation unit selects the optimal generation method based on data on bouquets purchased by the user in the past. The generation unit can also analyze the user's preferences and tendencies from the user's past consumption behavior and select the optimal generation method. Furthermore, the generation unit can generate an optimal bouquet by referring to the user's past consumption behavior. This makes it possible to provide an optimal generation method based on the user's past consumption behavior.

[0115] The generation unit can estimate the user's emotions and determine the priority of bouquets to be generated based on the estimated user's emotions. For example, if the user is nervous, the generation unit can prioritize generating a simple, highly visible bouquet. Alternatively, if the user is relaxed, the generation unit can prioritize generating a bouquet with a detailed design. Furthermore, if the user is in a hurry, the generation unit can prioritize generating a bouquet that can be generated quickly. This allows for the priority of bouquets to be determined according to the user's emotions.

[0116] The generation unit can improve the generation method by reflecting user feedback. For example, the generation unit identifies problems with the generation method from user feedback and improves it. The generation unit can also provide an optimal generation method by referring to user feedback. Furthermore, the generation unit can customize the generation method based on user feedback. This makes it possible to improve the generation method based on user feedback.

[0117] The generation unit can select an appropriate generation method based on the user's geographical location information. For example, if the user is in a specific area, the generation unit can generate a bouquet using flowers related to that area. If the user is traveling, the generation unit can also generate a bouquet using flowers related to the travel destination. Furthermore, if the user is participating in a specific event, the generation unit can also generate a bouquet using flowers related to the event. This makes it possible to provide a generation method based on the user's geographical location information.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives information from the user. This information includes the recipient's name, age, gender, hobbies, favorite color, and data on bouquets sent in the past. The reception unit stores the information entered by the user in a database and provides it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and analyze the recipient's preferences and personality. The analysis is performed using data mining and machine learning algorithms. The analysis unit can also predict the recipient's preferences and personality based on past data and trends. For example, it can analyze the type of bouquet the recipient will prefer by referring to past purchase history and seasonal trends. Step 3: The proposal unit uses the generative AI to propose bouquets based on the information analyzed by the analysis unit. The proposal suggests the optimal bouquet design and flower materials based on the recipient's preferences and personality. It can also suggest bouquets suitable for special messages or events. For example, it suggests bouquets for special events such as birthdays and wedding anniversaries. Step 4: The generation unit generates the bouquet proposed by the proposal unit. The generation is performed based on the bouquet design and the flower material selection method. The generation unit actually creates the proposed bouquet and provides it to the user. Some or all of the processing in the generation unit may be performed using AI, or may be performed without using AI.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit and analyzes the preferences and personality of the other party; a proposal unit that proposes a bouquet based on the information analyzed by the analysis unit; a generation unit that generates the bouquet suggested by the suggestion unit. A system characterized by:

2. The reception unit Accepts data such as the recipient's name, age, gender, hobbies, favorite color, and bouquets sent in the past 2. The system of claim 1.

3. The analysis unit Analyze the other person's preferences and personality based on past data or trends 2. The system of claim 1.

4. The proposal unit Suggest bouquet designs or flower materials based on the recipient's preferences and personality 2. The system of claim 1.

5. The proposal unit Propose bouquets suited to special messages and events 2. The system of claim 1.

6. The generation unit Generate a proposed bouquet 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the information input method based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past input history and provide an appropriate input interface 2. The system of claim 1.

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