System

Abstract

An object of a system according to an embodiment is to visualize a state of sold fresh flowers with the passage of days and determine whether or not the flowers are in a purchasable state.SOLUTION: A system includes a reception part, a visualization part, and a determination part. The reception unit receives flower information from a user. The visualization unit visualizes a state of the sold fresh flowers with the passage of days on the basis of the information received by the reception unit. The determination unit determines whether or not the commodity is in a purchasable state based on the state of the flower visualized by the visualization 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] Conventional techniques have had the problem of making it difficult to accurately grasp the condition of fresh flowers for sale over time and determine whether they are in a condition suitable for purchase.

[0005] The system according to the embodiment aims to visualize the condition of fresh flowers for sale over time and determine whether they are available for purchase. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a visualization unit, and a determination unit. The reception unit receives information about flowers from a user. The visualization unit visualizes the condition of the fresh flowers over time based on the information received by the reception unit. The determination unit determines whether the flowers are available for purchase based on the condition visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to the embodiment visualizes the condition of fresh flowers for sale over time and can determine whether they are available for purchase. [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) An e-commerce site specializing in flowers according to an embodiment of the present invention is a system that accepts flower information from users and uses a generation AI to visualize the condition of fresh flowers over time and determine whether they are available for purchase. The e-commerce site allows users to calculate the number of days and confirm whether the flowers are in optimal condition before purchasing. This system prevents beautiful flowers from being discarded and allows users to obtain fresh flowers at affordable prices. For example, a user accesses an e-commerce site and inputs information about the flowers they are considering purchasing, such as the specific type of flower and the desired purchase date. This information is then input into the generation AI. The generation AI then analyzes the input information and visualizes the condition of the fresh flowers over time. The generation AI predicts the condition of the flowers based on data such as the type of flower, storage condition, and age. For example, it visualizes how long a specific flower will remain fresh and the degree of deterioration. Based on the condition of the flowers visualized by the generation AI, users can determine whether they are available for purchase. For example, if the flowers are still fresh, they can decide to purchase them, but if they have deteriorated, they can forgo the purchase. This system allows users to confirm that flowers are in optimal condition before purchasing. It also prevents beautiful flowers from being discarded, allowing users to obtain fresh flowers at an affordable price. For example, even if a particular flower is offered at a lower price than usual, users can purchase it with confidence if the AI ​​can confirm that the flower is still fresh. The AI ​​can also provide advice on how to store flowers and the best time to purchase them. For example, it can provide information on how to store a particular flower to ensure its longevity and the best time to purchase it. This allows users to enjoy flowers for a longer period of time. In this way, using AI allows users to visualize the condition of flowers and purchase them in optimal condition. It also prevents beautiful flowers from being discarded, allowing users to obtain fresh flowers at an affordable price. This allows flower-specializing e-commerce sites to allow users to check the condition of flowers and purchase them in optimal condition.For example, users can visualize the condition of flowers and determine whether they are available for purchase, preventing beautiful flowers from being discarded and allowing them to obtain fresh flowers at an affordable price.

[0029] An e-commerce website specializing in flowers according to an embodiment includes a reception unit, a visualization unit, and a determination unit. The reception unit receives flower information from a user. The flower information from the user includes, but is not limited to, the type, color, size, and storage condition of the flower. For example, the reception unit receives information about the flower the user is considering purchasing from the e-commerce website. For example, the user inputs information such as the type of flower and the desired purchase date. This information is then input to the generation AI. The visualization unit uses the generation AI to visualize the condition of the fresh flowers over time based on the information received by the reception unit. The visualization can be performed using, for example, graphs, charts, simulations, and other methods, but is not limited to, these examples. For example, the generation AI predicts the condition of the flowers based on data such as the type of flower, storage condition, and the number of days that have passed. The generation AI visualizes, for example, how long a specific flower will remain fresh and how much deterioration is observed. The visualization unit can also use the generation AI to provide advice on how to store flowers and the best time to purchase them. For example, the visualization unit can provide information such as how to store a specific flower to ensure its longevity and the best time to purchase it. The determination unit determines whether a flower is available for purchase based on the condition of the flower visualized by the visualization unit. The determination is made based on criteria such as, but not limited to, the freshness of the flower, the vividness of the color, and the strength of the fragrance. For example, the determination unit determines whether a flower is fresh based on the visualized condition of the flower. For example, the determination unit can decide to purchase the flower if it is still fresh, and can forgo the purchase if it has deteriorated. This allows the flower-specializing e-commerce site according to the embodiment to allow users to check the condition of the flower and purchase it in the optimal condition. Some or all of the above-described processing by the determination unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the determination unit can make a determination using a generation AI model that inputs the condition of the flower visualized by the visualization unit and outputs whether the flower is available for purchase.

[0030] The visualization unit can predict the state of a flower based on data on the type of flower, storage state, and elapsed days. The visualization unit predicts the state of a flower according to, for example, the type of flower. For example, the visualization unit predicts the state of a flower based on the type of flower, such as rose, tulip, or carnation. The visualization unit can also predict the state of a flower based on the storage state. For example, the visualization unit predicts the state of a flower based on storage conditions such as temperature, humidity, and light conditions. The visualization unit can also predict the state of a flower based on the elapsed days. For example, the visualization unit predicts the state of a flower based on the number of days since the harvest date or the number of days since the start of storage. This allows the visualization unit to accurately predict the state of a flower based on data on the type of flower, storage state, and elapsed days. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input data on the type of flower, storage state, and elapsed days into the generation AI and cause the generation AI to predict the state of a flower.

[0031] The determination unit can determine whether a flower is fresh based on the visualized state of the flower. The determination unit determines whether a flower is fresh, for example, based on the visualized state of the flower. For example, the determination unit determines whether a flower is fresh based on criteria such as the color, fragrance, and condition of the leaves of the flower. The determination unit can also use an algorithm to determine whether a flower is fresh based on the visualized state of the flower. For example, an algorithm is used to determine whether a flower is fresh based on data such as the vividness of the flower's color, the intensity of the fragrance, and the degree of leaf wilting. This allows the determination unit to accurately determine whether a flower is fresh based on the visualized state of the flower. Some or all of the above-described processing in the determination unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the determination unit can input the state of the flower visualized by the visualization unit into the generation AI and cause the generation AI to determine whether the flower is fresh.

[0032] The visualization unit can visualize whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible. For example, the visualization unit visualizes whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible. For example, the visualization unit visualizes whether a flower will remain fresh over the passage of time and whether the degree of deterioration is visible based on data such as the flower's color, fragrance, and leaf condition. The visualization unit can also use an algorithm to visualize whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible. For example, the visualization unit uses an algorithm to visualize whether a flower will remain fresh over the passage of time and whether the degree of deterioration is visible based on data such as a change in flower color, loss of fragrance, and leaf wilting. This allows the visualization unit to visualize whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can cause a generation AI to execute a visualization of whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible.

[0033] The determination unit can decide to purchase the flower if it is still fresh and forego the purchase if it has deteriorated. For example, the determination unit decides to purchase the flower if it is still fresh and foregoes the purchase if it has deteriorated. For example, based on criteria such as the freshness of the flower, price, and user preferences, the determination unit decides to purchase the flower if it is still fresh and foregoes the purchase if it has deteriorated. The determination unit can also use an algorithm for deciding to purchase the flower if it is still fresh and foregoing the purchase if it has deteriorated. For example, an algorithm is used that decides to purchase the flower if it is still fresh and foregoes the purchase if it has deteriorated, based on data such as the vividness of the flower's color, the strength of the fragrance, and the degree of leaf wilting. This allows the determination unit to make a purchase decision based on the condition of the flower. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the determination unit can input the condition of the flower visualized by the visualization unit into the generation AI and cause the generation AI to make a decision to purchase the flower if it is still fresh and to forego the purchase if it has deteriorated.

[0034] The visualization unit can provide advice on how to preserve flowers and the appropriate timing to purchase them. The visualization unit provides advice on, for example, how to preserve flowers and the appropriate timing to purchase them. For example, the visualization unit provides information such as how a particular flower will last longer when stored and when the best time to purchase it is. The visualization unit can also use an algorithm to provide advice on how to preserve flowers and the appropriate timing to purchase them. For example, an algorithm is used to provide advice on how to preserve flowers and the appropriate timing to purchase them based on data such as the type of flower, its storage condition, and the number of days since storage. This allows the visualization unit to provide advice on how to preserve flowers and the appropriate timing to purchase them. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can cause the generation AI to provide advice on how to preserve flowers and the appropriate timing to purchase them.

[0035] The reception unit can analyze the user's past purchase history and suggest an appropriate method for inputting flower information. For example, the reception unit can automatically display as candidates the types of flowers that the user has frequently purchased in the past. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the types of flowers that the user will purchase in a particular season based on the user's past purchase history. This allows the reception unit to suggest the optimal input method based on the user's past purchase history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past purchase history data into the generation AI and cause the generation AI to suggest the optimal flower information input method.

[0036] When flower information is input, the reception unit can filter the flower information based on the user's current season and climate. For example, the reception unit can automatically display flower types suitable for the current season as candidates. The reception unit can also suggest long-lasting flower types based on current weather conditions. The reception unit can also suggest flower types suitable for seasonal events and occasions. This allows the reception unit to provide appropriate flower information based on the current season and climate. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input current season and weather data into the generation AI and have the generation AI perform filtering.

[0037] When inputting flower information, the reception unit can select an appropriate input means depending on the user's input method. For example, when a user inputs the type of flower by voice, the reception unit automatically converts the input into text using voice recognition technology. Furthermore, when a user uploads an image, the reception unit can also identify the type of flower using image recognition technology. Furthermore, when a user inputs flower information by text, the reception unit can provide an input completion function to enable quick input of information. This allows the reception unit to provide the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input voice data or image data into the generation AI and have the generation AI select an appropriate input means.

[0038] When inputting flower information, the reception unit can prioritize inputting relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize displaying information about nearby florists based on the user's current location. The reception unit can also suggest types of flowers unique to a region based on the user's geographical location information. The reception unit can also prioritize displaying information about flowers within a delivery range based on the user's geographical location information. This allows the reception unit to provide highly relevant 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, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize inputting relevant information.

[0039] When flower information is input, the reception unit can analyze the user's social media activity and input related information. The reception unit can, for example, suggest flower information related to a location 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 suggest related flower types. The reception unit can also suggest related flower information based on the activity of the user's friends on social media. This allows the reception unit to provide related information based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to input related information.

[0040] The reception unit can adjust the input method by reflecting the user's past feedback when inputting flower information. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input interface. This allows the reception unit to customize the input method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the input method.

[0041] The visualization unit can adjust the accuracy of visualization based on the type and preservation state of the flower during visualization. The visualization unit provides detailed visualization information according to, for example, the type of flower. The visualization unit can also visualize the progress of deterioration in detail based on the preservation state of the flower. The visualization unit can also provide an optimal visualization method by combining the type of flower and the preservation state. This allows the visualization unit to provide appropriate visualization information based on the type of flower and the preservation state. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the visualization unit can input data on the type of flower and the preservation state to the generation AI and cause the generation AI to adjust the accuracy of the visualization.

[0042] The visualization unit can apply different visualization algorithms depending on the category of the flower during visualization. For example, the visualization unit can apply different visualization algorithms to cut flowers and potted flowers. The visualization unit can also apply different visualization algorithms to seasonal flowers and year-round flowers. The visualization unit can also apply different visualization algorithms depending on the purpose of the flower (for gift use, decoration, etc.). This allows the visualization unit to provide an appropriate visualization algorithm depending on the category of the flower. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, the generation AI. For example, the visualization unit can input flower category data to the generation AI and cause the generation AI to apply different visualization algorithms.

[0043] The visualization unit can improve the accuracy of visualization by referring to the user's past visualization results when visualizing. For example, the visualization unit can suggest an optimal visualization method based on the user's past visualization results. The visualization unit can also preferentially apply a specific visualization algorithm based on the user's past visualization results. The visualization unit can also analyze the user's past visualization results and improve the accuracy of the visualization. In this way, the visualization unit can improve the accuracy of the visualization based on the user's past visualization results. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's past visualization result data into the generation AI and cause the generation AI to improve the accuracy of the visualization.

[0044] The visualization unit can determine the visualization order based on the flower preservation method during visualization. For example, the visualization unit prioritizes visualization of the progression of deterioration based on the flower preservation method. The visualization unit can also prioritize visualization of long-lasting flower types based on the flower preservation method. The visualization unit can also suggest an optimal preservation method based on the flower preservation method. This allows the visualization unit to provide appropriate visualization information based on the flower preservation method. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, the generation AI. For example, the visualization unit can input flower preservation method data into the generation AI and have the generation AI determine the visualization order.

[0045] The visualization unit can adjust the use of visualization terminology during visualization according to the user's level of expertise. For example, if the user is a beginner, the visualization unit can provide visualized information in simple language. If the user is an intermediate user, the visualization unit can also provide visualized information using appropriate technical terminology. If the user is an advanced user, the visualization unit can also provide visualized information using detailed technical terminology. This allows the visualization unit to provide appropriate visualized information according to the user's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terminology.

[0046] When making a judgment, the judgment unit can adjust the accuracy of the judgment based on the condition of the flowers and the storage method. The judgment unit makes a purchase decision based on detailed information, for example, on the condition of the flowers. The judgment unit can also make a purchase decision based on the progression of deterioration, based on the storage method of the flowers. The judgment unit can also make an optimal purchase decision by combining the condition of the flowers and the storage method. This allows the judgment unit to make an appropriate decision based on the condition of the flowers and the storage method. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the judgment unit can input data on the condition of the flowers and the storage method into the generation AI and have the generation AI adjust the accuracy of the judgment.

[0047] The determination unit can apply different determination algorithms depending on the category of the flower when making the determination. For example, the determination unit applies different determination algorithms to cut flowers and potted flowers. The determination unit can also apply different determination algorithms to seasonal flowers and year-round flowers. The determination unit can also apply different determination algorithms depending on the purpose of the flower (for example, as a gift or for decoration). This allows the determination unit to provide an appropriate determination algorithm depending on the category of the flower. Some or all of the above-mentioned processing in the determination unit may be performed using, or without, the generation AI. For example, the determination unit can input flower category data into the generation AI and cause the generation AI to apply different determination algorithms.

[0048] When making a judgment, the judgment unit can improve the accuracy of the judgment by referring to the user's past judgment results. The judgment unit, for example, suggests an optimal judgment method based on the user's past judgment results. The judgment unit can also preferentially apply a specific judgment algorithm based on the user's past judgment results. The judgment unit can also analyze the user's past judgment results and improve the accuracy of the judgment. In this way, the judgment unit can improve the accuracy of the judgment based on the user's past judgment results. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the judgment unit can input the user's past judgment result data into the generation AI and have the generation AI improve the accuracy of the judgment.

[0049] The judgment unit can determine the order of judgment based on the flower preservation method when making judgments. The judgment unit can, for example, determine the priority of judgment based on the progress of deterioration based on the flower preservation method. The judgment unit can also prioritize long-lasting flower types based on the flower preservation method. The judgment unit can also suggest the optimal preservation method based on the flower preservation method. This allows the judgment unit to make appropriate judgments based on the flower preservation method. Some or all of the above-mentioned processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input flower preservation method data into the generation AI and have the generation AI determine the order of judgments.

[0050] The determination unit can adjust the order of determination based on the relevance of the flowers when making a determination. For example, the determination unit prioritizes determination of the most relevant flower information based on the relevance of the flowers. The determination unit can also group and determine related flower information based on the relevance of the flowers. The determination unit can also consecutively determine related flower information based on the relevance of the flowers. This allows the determination unit to make an appropriate determination based on the relevance of the flowers. Some or all of the above-described processing in the determination unit may be performed using, or without, the generation AI. For example, the determination unit can input flower relevance data into the generation AI and cause the generation AI to adjust the order of determination.

[0051] When making a judgment, the judgment unit can adjust the use of judgment terms depending on the user's level of expertise. For example, if the user is a beginner, the judgment unit can provide judgment information in simple language. If the user is an intermediate user, the judgment unit can also provide judgment information using appropriate technical terms. If the user is an advanced user, the judgment unit can also provide judgment information using detailed technical terms. This allows the judgment unit to provide appropriate judgment information depending on the user's level of expertise. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the judgment unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terms.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The visualization unit can provide advice on how to preserve flowers and the appropriate timing to purchase them. For example, it can provide information such as how to preserve a particular flower to make it last longer and the best time to purchase it. The visualization unit can also use an algorithm to provide advice on how to preserve flowers and the appropriate timing to purchase them. For example, it can use an algorithm to provide advice on how to preserve flowers and the appropriate timing to purchase them based on data such as the type of flower, its storage condition, and the number of days since it was last preserved. This allows the visualization unit to provide advice on how to preserve flowers and the appropriate timing to purchase them. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can cause the generation AI to provide advice on how to preserve flowers and the appropriate timing to purchase them.

[0054] When making a judgment, the judgment unit can adjust the accuracy of the judgment based on the condition of the flowers and the storage method. For example, the judgment unit makes a purchase decision based on detailed information, such as the condition of the flowers. The judgment unit can also make a purchase decision based on the progress of deterioration, such as the storage method of the flowers. The judgment unit can also make an optimal purchase decision by combining the condition of the flowers and the storage method. This allows the judgment unit to make an appropriate decision based on the condition of the flowers and the storage method. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, the generation AI. For example, the judgment unit can input data on the condition of the flowers and the storage method into the generation AI and have the generation AI adjust the accuracy of the judgment.

[0055] The reception unit can analyze the user's past purchase history and suggest an appropriate method for inputting flower information. For example, the reception unit can automatically display as candidates the types of flowers that the user has frequently purchased in the past. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the types of flowers that the user will purchase in a particular season based on the user's past purchase history. This allows the reception unit to suggest the optimal input method based on the user's past purchase history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past purchase history data into the generation AI and cause the generation AI to suggest the optimal method for inputting flower information.

[0056] During visualization, the visualization unit can determine the visualization order based on the flower preservation method. For example, the visualization unit can prioritize visualization of the progression of deterioration based on the flower preservation method. The visualization unit can also prioritize visualization of long-lasting flower types based on the flower preservation method. The visualization unit can also suggest an optimal preservation method based on the flower preservation method. This allows the visualization unit to provide appropriate visualization information based on the flower preservation method. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, the generation AI. For example, the visualization unit can input flower preservation method data to the generation AI and have the generation AI determine the visualization order.

[0057] When making a judgment, the judgment unit can apply different judgment algorithms depending on the category of the flower. For example, different judgment algorithms can be applied to cut flowers and potted flowers. The judgment unit can also apply different judgment algorithms to seasonal flowers and year-round flowers. The judgment unit can also apply different judgment algorithms depending on the purpose of the flower (for gift use, decoration, etc.). This allows the judgment unit to provide an appropriate judgment algorithm depending on the category of the flower. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, the generation AI, for example. For example, the judgment unit can input flower category data into the generation AI and cause the generation AI to apply different judgment algorithms.

[0058] During visualization, the visualization unit can improve the accuracy of the visualization by referring to the user's past visualization results. For example, the visualization unit can suggest an optimal visualization method based on the user's past visualization results. The visualization unit can also preferentially apply a specific visualization algorithm based on the user's past visualization results. The visualization unit can also analyze the user's past visualization results and improve the accuracy of the visualization. In this way, the visualization unit can improve the accuracy of the visualization based on the user's past visualization results. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's past visualization result data into the generation AI and cause the generation AI to improve the accuracy of the visualization.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives flower information from the user. The flower information from the user includes, for example, the type, color, size, and storage condition of the flower. The user accesses the e-commerce site and inputs information about the flower they are considering purchasing. For example, they input information such as the specific type of flower and the desired purchase date. Step 2: The visualization unit uses the generation AI to visualize the condition of the fresh flowers over time based on the information received by the reception unit. Visualization is performed using methods such as graphs, charts, and simulations. The generation AI predicts the condition of the flowers based on data such as the type of flower, storage condition, and number of days elapsed. The visualization unit can also use the generation AI to provide advice on how to store the flowers and the best time to purchase them. Step 3: The determination unit determines whether the flower is available for purchase based on the condition of the flower visualized by the visualization unit. The determination is made based on criteria such as the freshness of the flower, the vividness of the color, and the strength of the fragrance. The determination unit determines whether the flower is fresh based on the visualized condition of the flower. For example, if the flower is still fresh, the purchase can be decided, but if it has deteriorated, the purchase can be postponed.

[0061] (Example 2) An e-commerce site specializing in flowers according to an embodiment of the present invention is a system that accepts flower information from users and uses a generation AI to visualize the condition of fresh flowers over time and determine whether they are available for purchase. The e-commerce site allows users to calculate the number of days and confirm whether the flowers are in optimal condition before purchasing. This system prevents beautiful flowers from being discarded and allows users to obtain fresh flowers at affordable prices. For example, a user accesses an e-commerce site and inputs information about the flowers they are considering purchasing, such as the specific type of flower and the desired purchase date. This information is then input into the generation AI. The generation AI then analyzes the input information and visualizes the condition of the fresh flowers over time. The generation AI predicts the condition of the flowers based on data such as the type of flower, storage condition, and age. For example, it visualizes how long a specific flower will remain fresh and the degree of deterioration. Based on the condition of the flowers visualized by the generation AI, users can determine whether they are available for purchase. For example, if the flowers are still fresh, they can decide to purchase them, but if they have deteriorated, they can forgo the purchase. This system allows users to confirm that flowers are in optimal condition before purchasing. It also prevents beautiful flowers from being discarded, allowing users to obtain fresh flowers at an affordable price. For example, even if a particular flower is offered at a lower price than usual, users can purchase it with confidence if the AI ​​can confirm that the flower is still fresh. The AI ​​can also provide advice on how to store flowers and the best time to purchase them. For example, it can provide information on how to store a particular flower to ensure its longevity and the best time to purchase it. This allows users to enjoy flowers for a longer period of time. In this way, using AI allows users to visualize the condition of flowers and purchase them in optimal condition. It also prevents beautiful flowers from being discarded, allowing users to obtain fresh flowers at an affordable price. This allows flower-specializing e-commerce sites to allow users to check the condition of flowers and purchase them in optimal condition.For example, users can visualize the condition of flowers and determine whether they are available for purchase, preventing beautiful flowers from being discarded and allowing them to obtain fresh flowers at an affordable price.

[0062] An e-commerce website specializing in flowers according to an embodiment includes a reception unit, a visualization unit, and a determination unit. The reception unit receives flower information from a user. The flower information from the user includes, but is not limited to, the type, color, size, and storage condition of the flower. For example, the reception unit receives information about the flower the user is considering purchasing from the e-commerce website. For example, the user inputs information such as the type of flower and the desired purchase date. This information is then input to the generation AI. The visualization unit uses the generation AI to visualize the condition of the fresh flowers over time based on the information received by the reception unit. The visualization can be performed using, for example, graphs, charts, simulations, and other methods, but is not limited to, these examples. For example, the generation AI predicts the condition of the flowers based on data such as the type of flower, storage condition, and the number of days that have passed. The generation AI visualizes, for example, how long a specific flower will remain fresh and how much deterioration is observed. The visualization unit can also use the generation AI to provide advice on how to store flowers and the best time to purchase them. For example, the visualization unit can provide information such as how to store a specific flower to ensure its longevity and the best time to purchase it. The determination unit determines whether a flower is available for purchase based on the condition of the flower visualized by the visualization unit. The determination is made based on criteria such as, but not limited to, the freshness of the flower, the vividness of the color, and the strength of the fragrance. For example, the determination unit determines whether a flower is fresh based on the visualized condition of the flower. For example, the determination unit can decide to purchase the flower if it is still fresh, and can forgo the purchase if it has deteriorated. This allows the flower-specializing e-commerce site according to the embodiment to allow users to check the condition of the flower and purchase it in the optimal condition. Some or all of the above-described processing by the determination unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the determination unit can make a determination using a generation AI model that inputs the condition of the flower visualized by the visualization unit and outputs whether the flower is available for purchase.

[0063] The visualization unit can predict the state of a flower based on data on the type of flower, storage state, and elapsed days. The visualization unit predicts the state of a flower according to, for example, the type of flower. For example, the visualization unit predicts the state of a flower based on the type of flower, such as rose, tulip, or carnation. The visualization unit can also predict the state of a flower based on the storage state. For example, the visualization unit predicts the state of a flower based on storage conditions such as temperature, humidity, and light conditions. The visualization unit can also predict the state of a flower based on the elapsed days. For example, the visualization unit predicts the state of a flower based on the number of days since the harvest date or the number of days since the start of storage. This allows the visualization unit to accurately predict the state of a flower based on data on the type of flower, storage state, and elapsed days. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input data on the type of flower, storage state, and elapsed days into the generation AI and cause the generation AI to predict the state of a flower.

[0064] The determination unit can determine whether a flower is fresh based on the visualized state of the flower. The determination unit determines whether a flower is fresh, for example, based on the visualized state of the flower. For example, the determination unit determines whether a flower is fresh based on criteria such as the color, fragrance, and condition of the leaves of the flower. The determination unit can also use an algorithm to determine whether a flower is fresh based on the visualized state of the flower. For example, an algorithm is used to determine whether a flower is fresh based on data such as the vividness of the flower's color, the intensity of the fragrance, and the degree of leaf wilting. This allows the determination unit to accurately determine whether a flower is fresh based on the visualized state of the flower. Some or all of the above-described processing in the determination unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the determination unit can input the state of the flower visualized by the visualization unit into the generation AI and cause the generation AI to determine whether the flower is fresh.

[0065] The visualization unit can visualize whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible. For example, the visualization unit visualizes whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible. For example, the visualization unit visualizes whether a flower will remain fresh over the passage of time and whether the degree of deterioration is visible based on data such as the flower's color, fragrance, and leaf condition. The visualization unit can also use an algorithm to visualize whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible. For example, the visualization unit uses an algorithm to visualize whether a flower will remain fresh over the passage of time and whether the degree of deterioration is visible based on data such as a change in flower color, loss of fragrance, and leaf wilting. This allows the visualization unit to visualize whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can cause a generation AI to execute a visualization of whether a specific flower will remain fresh over the passage of time and whether the degree of deterioration is visible.

[0066] The determination unit can decide to purchase the flower if it is still fresh and forego the purchase if it has deteriorated. For example, the determination unit decides to purchase the flower if it is still fresh and foregoes the purchase if it has deteriorated. For example, based on criteria such as the freshness of the flower, price, and user preferences, the determination unit decides to purchase the flower if it is still fresh and foregoes the purchase if it has deteriorated. The determination unit can also use an algorithm for deciding to purchase the flower if it is still fresh and foregoing the purchase if it has deteriorated. For example, an algorithm is used that decides to purchase the flower if it is still fresh and foregoes the purchase if it has deteriorated, based on data such as the vividness of the flower's color, the strength of the fragrance, and the degree of leaf wilting. This allows the determination unit to make a purchase decision based on the condition of the flower. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the determination unit can input the condition of the flower visualized by the visualization unit into the generation AI and cause the generation AI to make a decision to purchase the flower if it is still fresh and to forego the purchase if it has deteriorated.

[0067] The visualization unit can provide advice on how to preserve flowers and the appropriate timing to purchase them. The visualization unit provides advice on, for example, how to preserve flowers and the appropriate timing to purchase them. For example, the visualization unit provides information such as how a particular flower will last longer when stored and when the best time to purchase it is. The visualization unit can also use an algorithm to provide advice on how to preserve flowers and the appropriate timing to purchase them. For example, an algorithm is used to provide advice on how to preserve flowers and the appropriate timing to purchase them based on data such as the type of flower, its storage condition, and the number of days since storage. This allows the visualization unit to provide advice on how to preserve flowers and the appropriate timing to purchase them. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can cause the generation AI to provide advice on how to preserve flowers and the appropriate timing to purchase them.

[0068] The reception unit can estimate the user's emotions and adjust the input method for flower information based on the estimated user emotions. For example, if the user is feeling 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 input of flower information. This allows the reception unit to adjust the input method according 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, 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, the generation AI, or without the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] The reception unit can analyze the user's past purchase history and suggest an appropriate method for inputting flower information. For example, the reception unit can automatically display as candidates the types of flowers that the user has frequently purchased in the past. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the types of flowers that the user will purchase in a particular season based on the user's past purchase history. This allows the reception unit to suggest the optimal input method based on the user's past purchase history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past purchase history data into the generation AI and cause the generation AI to suggest the optimal flower information input method.

[0070] When flower information is input, the reception unit can filter the flower information based on the user's current season and climate. For example, the reception unit can automatically display flower types suitable for the current season as candidates. The reception unit can also suggest long-lasting flower types based on current weather conditions. The reception unit can also suggest flower types suitable for seasonal events and occasions. This allows the reception unit to provide appropriate flower information based on the current season and climate. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input current season and weather data into the generation AI and have the generation AI perform filtering.

[0071] When inputting flower information, the reception unit can select an appropriate input means depending on the user's input method. For example, when a user inputs the type of flower by voice, the reception unit automatically converts the input into text using voice recognition technology. Furthermore, when a user uploads an image, the reception unit can also identify the type of flower using image recognition technology. Furthermore, when a user inputs flower information by text, the reception unit can provide an input completion function to enable quick input of information. This allows the reception unit to provide the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input voice data or image data into the generation AI and have the generation AI select an appropriate input means.

[0072] The reception unit can estimate the user's emotions and prioritize the input flower information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize displaying simple and important information. Furthermore, if the user is relaxed, the reception unit can prioritize displaying detailed information. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying the most important information. This allows the reception unit to prioritize information according to the user's emotions. The 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, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] When inputting flower information, the reception unit can prioritize inputting relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize displaying information about nearby florists based on the user's current location. The reception unit can also suggest types of flowers unique to a region based on the user's geographical location information. The reception unit can also prioritize displaying information about flowers within a delivery range based on the user's geographical location information. This allows the reception unit to provide highly relevant 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, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize inputting relevant information.

[0074] When flower information is input, the reception unit can analyze the user's social media activity and input related information. The reception unit can, for example, suggest flower information related to a location 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 suggest related flower types. The reception unit can also suggest related flower information based on the activity of the user's friends on social media. This allows the reception unit to provide related information based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to input related information.

[0075] The reception unit can adjust the input method by reflecting the user's past feedback when inputting flower information. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input interface. This allows the reception unit to customize the input method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the input method.

[0076] The visualization unit can estimate the user's emotions and adjust the visualization method of the flower state based on the estimated user's emotions. For example, if the user is relaxed, the visualization unit can provide detailed visualization information. If the user is in a hurry, the visualization unit can provide concise and to-the-point visualization information. If the user is excited, the visualization unit can provide visualization information with a visually stimulating effect. This allows the visualization unit to adjust the visualization method according 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, 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 visualization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the visualization unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] The visualization unit can adjust the accuracy of visualization based on the type and preservation state of the flower during visualization. The visualization unit provides detailed visualization information according to, for example, the type of flower. The visualization unit can also visualize the progress of deterioration in detail based on the preservation state of the flower. The visualization unit can also provide an optimal visualization method by combining the type of flower and the preservation state. This allows the visualization unit to provide appropriate visualization information based on the type of flower and the preservation state. Some or all of the above-mentioned processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the visualization unit can input data on the type of flower and the preservation state to the generation AI and cause the generation AI to adjust the accuracy of the visualization.

[0078] The visualization unit can apply different visualization algorithms depending on the category of the flower during visualization. For example, the visualization unit can apply different visualization algorithms to cut flowers and potted flowers. The visualization unit can also apply different visualization algorithms to seasonal flowers and year-round flowers. The visualization unit can also apply different visualization algorithms depending on the purpose of the flower (for gift use, decoration, etc.). This allows the visualization unit to provide an appropriate visualization algorithm depending on the category of the flower. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, the generation AI. For example, the visualization unit can input flower category data to the generation AI and cause the generation AI to apply different visualization algorithms.

[0079] The visualization unit can improve the accuracy of visualization by referring to the user's past visualization results when visualizing. For example, the visualization unit can suggest an optimal visualization method based on the user's past visualization results. The visualization unit can also preferentially apply a specific visualization algorithm based on the user's past visualization results. The visualization unit can also analyze the user's past visualization results and improve the accuracy of the visualization. In this way, the visualization unit can improve the accuracy of the visualization based on the user's past visualization results. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's past visualization result data into the generation AI and cause the generation AI to improve the accuracy of the visualization.

[0080] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated user emotions. For example, if the user is in a hurry, the visualization unit can provide short, to-the-point visualization information. Furthermore, if the user is relaxed, the visualization unit can provide longer visualization information with detailed explanations. Furthermore, if the user is excited, the visualization unit can provide visualization information with visually stimulating effects. This allows the visualization unit to adjust the length of the visualization 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, 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 visualization unit can be performed using, for example, the generation AI, or without the generation AI. For example, the visualization unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The visualization unit can determine the visualization order based on the flower preservation method during visualization. For example, the visualization unit prioritizes visualization of the progression of deterioration based on the flower preservation method. The visualization unit can also prioritize visualization of long-lasting flower types based on the flower preservation method. The visualization unit can also suggest an optimal preservation method based on the flower preservation method. This allows the visualization unit to provide appropriate visualization information based on the flower preservation method. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, the generation AI. For example, the visualization unit can input flower preservation method data into the generation AI and have the generation AI determine the visualization order.

[0082] The visualization unit can adjust the use of visualization terminology during visualization according to the user's level of expertise. For example, if the user is a beginner, the visualization unit can provide visualized information in simple language. If the user is an intermediate user, the visualization unit can also provide visualized information using appropriate technical terminology. If the user is an advanced user, the visualization unit can also provide visualized information using detailed technical terminology. This allows the visualization unit to provide appropriate visualized information according to the user's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terminology.

[0083] The judgment unit can estimate the user's emotions and adjust the criteria for purchase decision based on the estimated user emotions. For example, if the user is relaxed, the judgment unit can make a purchase decision based on detailed information. Furthermore, if the user is in a hurry, the judgment unit can make a purchase decision based on concise information. Furthermore, if the user is excited, the judgment unit can make a purchase decision based on visually stimulating information. This allows the judgment unit to adjust the criteria for purchase decision according to 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 may 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 judgment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the judgment unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] When making a judgment, the judgment unit can adjust the accuracy of the judgment based on the condition of the flowers and the storage method. The judgment unit makes a purchase decision based on detailed information, for example, on the condition of the flowers. The judgment unit can also make a purchase decision based on the progression of deterioration, based on the storage method of the flowers. The judgment unit can also make an optimal purchase decision by combining the condition of the flowers and the storage method. This allows the judgment unit to make an appropriate decision based on the condition of the flowers and the storage method. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the judgment unit can input data on the condition of the flowers and the storage method into the generation AI and have the generation AI adjust the accuracy of the judgment.

[0085] The determination unit can apply different determination algorithms depending on the category of the flower when making the determination. For example, the determination unit applies different determination algorithms to cut flowers and potted flowers. The determination unit can also apply different determination algorithms to seasonal flowers and year-round flowers. The determination unit can also apply different determination algorithms depending on the purpose of the flower (for example, as a gift or for decoration). This allows the determination unit to provide an appropriate determination algorithm depending on the category of the flower. Some or all of the above-mentioned processing in the determination unit may be performed using, or without, the generation AI. For example, the determination unit can input flower category data into the generation AI and cause the generation AI to apply different determination algorithms.

[0086] When making a judgment, the judgment unit can improve the accuracy of the judgment by referring to the user's past judgment results. The judgment unit, for example, suggests an optimal judgment method based on the user's past judgment results. The judgment unit can also preferentially apply a specific judgment algorithm based on the user's past judgment results. The judgment unit can also analyze the user's past judgment results and improve the accuracy of the judgment. In this way, the judgment unit can improve the accuracy of the judgment based on the user's past judgment results. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the judgment unit can input the user's past judgment result data into the generation AI and have the generation AI improve the accuracy of the judgment.

[0087] The determination unit can estimate the user's emotions and determine the priority of the judgments based on the estimated user's emotions. For example, if the user is relaxed, the determination unit can determine the priority of the judgments based on detailed information. Furthermore, if the user is in a hurry, the determination unit can determine the priority of the judgments based on concise information. Furthermore, if the user is excited, the determination unit can determine the priority of the judgments based on visually stimulating information. This allows the determination unit to determine the priority of the judgments according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 determination unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the determination unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0088] The judgment unit can determine the order of judgment based on the flower preservation method when making judgments. The judgment unit can, for example, determine the priority of judgment based on the progress of deterioration based on the flower preservation method. The judgment unit can also prioritize long-lasting flower types based on the flower preservation method. The judgment unit can also suggest the optimal preservation method based on the flower preservation method. This allows the judgment unit to make appropriate judgments based on the flower preservation method. Some or all of the above-mentioned processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input flower preservation method data into the generation AI and have the generation AI determine the order of judgments.

[0089] The determination unit can adjust the order of determination based on the relevance of the flowers when making a determination. For example, the determination unit prioritizes determination of the most relevant flower information based on the relevance of the flowers. The determination unit can also group and determine related flower information based on the relevance of the flowers. The determination unit can also consecutively determine related flower information based on the relevance of the flowers. This allows the determination unit to make an appropriate determination based on the relevance of the flowers. Some or all of the above-described processing in the determination unit may be performed using, or without, the generation AI. For example, the determination unit can input flower relevance data into the generation AI and cause the generation AI to adjust the order of determination.

[0090] When making a judgment, the judgment unit can adjust the use of judgment terms depending on the user's level of expertise. For example, if the user is a beginner, the judgment unit can provide judgment information in simple language. If the user is an intermediate user, the judgment unit can also provide judgment information using appropriate technical terms. If the user is an advanced user, the judgment unit can also provide judgment information using detailed technical terms. This allows the judgment unit to provide appropriate judgment information depending on the user's level of expertise. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the judgment unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terms. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, visualization unit, determination unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, when a user accesses an e-commerce site and inputs information about a flower they are considering purchasing, the information is received via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. The visualization unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and visualizes the state of the flower using a generation AI. The determination unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and determines whether the flower is available for purchase based on the visualized state of the flower. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the input method depending on the user's emotion. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, visualization unit, determination unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, when a user accesses an e-commerce site and inputs information about a flower they are considering purchasing, the information is received via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The visualization unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and visualizes the state of the flower using a generation AI. The determination unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and determines whether the flower is available for purchase based on the visualized state of the flower. The emotion estimation function is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the input method depending on the user's emotion. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, visualization unit, determination unit, and emotion estimation function, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, when a user accesses an e-commerce site and inputs information about a flower they are considering purchasing, the information is received via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The visualization unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and visualizes the state of the flower using a generation AI. The determination unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and determines whether the flower is available for purchase based on the visualized state of the flower. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the input method depending on the user's emotion. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, visualization unit, determination unit, and emotion estimation function, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, when a user accesses an e-commerce site and inputs information about a flower they are considering purchasing, the information is received via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. The visualization unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and visualizes the state of the flower using a generative AI. The determination unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and determines whether the flower is available for purchase based on the visualized state of the flower. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the input method depending on the user's emotion.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can estimate the user's emotions and adjust the input method for flower information based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to 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 input of flower information. This allows the reception unit to adjust the input method according 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, 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, the generation AI, or without the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] The visualization unit can provide advice on how to preserve flowers and the appropriate timing to purchase them. For example, it can provide information such as how to preserve a particular flower to make it last longer and the best time to purchase it. The visualization unit can also use an algorithm to provide advice on how to preserve flowers and the appropriate timing to purchase them. For example, it can use an algorithm to provide advice on how to preserve flowers and the appropriate timing to purchase them based on data such as the type of flower, its storage condition, and the number of days since it was last preserved. This allows the visualization unit to provide advice on how to preserve flowers and the appropriate timing to purchase them. Some or all of the above-described processing in the visualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visualization unit can cause the generation AI to provide advice on how to preserve flowers and the appropriate timing to purchase them.

[0094] The judgment unit can estimate the user's emotions and adjust the criteria for purchase decision based on the estimated user emotions. For example, if the user is relaxed, the judgment unit can make the purchase decision based on detailed information. If the user is in a hurry, the judgment unit can make the purchase decision based on concise information. If the user is excited, the judgment unit can make the purchase decision based on visually stimulating information. This allows the judgment unit to adjust the criteria for purchase decision according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the judgment unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the judgment unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0095] The visualization unit can estimate the user's emotions and adjust the visualization method of the flower state based on the estimated user's emotions. For example, if the user is relaxed, detailed visualization information is provided. If the user is in a hurry, the visualization unit can provide concise and to-the-point visualization information. If the user is excited, the visualization unit can provide visualization information with a visually stimulating effect. This allows the visualization unit to adjust the visualization method 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 may 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 visualization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the visualization unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0096] When making a judgment, the judgment unit can adjust the accuracy of the judgment based on the condition of the flowers and the storage method. For example, the judgment unit makes a purchase decision based on detailed information, such as the condition of the flowers. The judgment unit can also make a purchase decision based on the progress of deterioration, such as the storage method of the flowers. The judgment unit can also make an optimal purchase decision by combining the condition of the flowers and the storage method. This allows the judgment unit to make an appropriate decision based on the condition of the flowers and the storage method. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, the generation AI. For example, the judgment unit can input data on the condition of the flowers and the storage method into the generation AI and have the generation AI adjust the accuracy of the judgment.

[0097] The reception unit can analyze the user's past purchase history and suggest an appropriate method for inputting flower information. For example, the reception unit can automatically display as candidates the types of flowers that the user has frequently purchased in the past. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the types of flowers that the user will purchase in a particular season based on the user's past purchase history. This allows the reception unit to suggest the optimal input method based on the user's past purchase history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past purchase history data into the generation AI and cause the generation AI to suggest the optimal method for inputting flower information.

[0098] During visualization, the visualization unit can determine the visualization order based on the flower preservation method. For example, the visualization unit can prioritize visualization of the progression of deterioration based on the flower preservation method. The visualization unit can also prioritize visualization of long-lasting flower types based on the flower preservation method. The visualization unit can also suggest an optimal preservation method based on the flower preservation method. This allows the visualization unit to provide appropriate visualization information based on the flower preservation method. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, the generation AI. For example, the visualization unit can input flower preservation method data to the generation AI and have the generation AI determine the visualization order.

[0099] When making a judgment, the judgment unit can apply different judgment algorithms depending on the category of the flower. For example, different judgment algorithms can be applied to cut flowers and potted flowers. The judgment unit can also apply different judgment algorithms to seasonal flowers and year-round flowers. The judgment unit can also apply different judgment algorithms depending on the purpose of the flower (for gift use, decoration, etc.). This allows the judgment unit to provide an appropriate judgment algorithm depending on the category of the flower. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, the generation AI, for example. For example, the judgment unit can input flower category data into the generation AI and cause the generation AI to apply different judgment algorithms.

[0100] The reception unit can estimate the user's emotions and prioritize the input flower information based on the estimated user emotions. For example, if the user is feeling stressed, simple and important information can be displayed preferentially. Furthermore, if the user is relaxed, the reception unit can also display detailed information preferentially. Furthermore, if the user is in a hurry, the reception unit can also display the most important information preferentially. This allows the reception unit to prioritize information according to the user's emotions. The 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, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0101] During visualization, the visualization unit can improve the accuracy of the visualization by referring to the user's past visualization results. For example, the visualization unit can suggest an optimal visualization method based on the user's past visualization results. The visualization unit can also preferentially apply a specific visualization algorithm based on the user's past visualization results. The visualization unit can also analyze the user's past visualization results and improve the accuracy of the visualization. In this way, the visualization unit can improve the accuracy of the visualization based on the user's past visualization results. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's past visualization result data into the generation AI and cause the generation AI to improve the accuracy of the visualization.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit receives flower information from the user. The flower information from the user includes, for example, the type, color, size, and storage condition of the flower. The user accesses the e-commerce site and inputs information about the flower they are considering purchasing. For example, they input information such as the specific type of flower and the desired purchase date. Step 2: The visualization unit uses the generation AI to visualize the condition of the fresh flowers over time based on the information received by the reception unit. Visualization is performed using methods such as graphs, charts, and simulations. The generation AI predicts the condition of the flowers based on data such as the type of flower, storage condition, and number of days elapsed. The visualization unit can also use the generation AI to provide advice on how to store the flowers and the best time to purchase them. Step 3: The determination unit determines whether the flower is available for purchase based on the condition of the flower visualized by the visualization unit. The determination is made based on criteria such as the freshness of the flower, the vividness of the color, and the strength of the fragrance. The determination unit determines whether the flower is fresh based on the visualized condition of the flower. For example, if the flower is still fresh, the purchase can be decided, but if it has deteriorated, the purchase can be postponed.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 AI 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] 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.

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

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 AI 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 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.

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

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 AI 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 flower information from a user; a visualization unit that visualizes the state of the fresh flowers over time based on the information received by the reception unit; a determination unit that determines whether the flower is available for purchase based on the state of the flower visualized by the visualization unit. A system characterized by:

2. The visualization unit Predict the condition of flowers based on the type of flower, storage condition, and elapsed time 2. The system of claim 1.

3. The determination unit Determine whether flowers are fresh based on the visualized state of the flowers 2. The system of claim 1.

4. The visualization unit Visualize how many days a particular flower will remain fresh and how much it will deteriorate 2. The system of claim 1.

5. The determination unit If the flowers are still fresh, decide to buy them, but if they have deteriorated, hold off on the purchase.

2. The system of claim 1.

6. The visualization unit Providing advice on how to preserve flowers and the best time to buy them 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the flower information input method based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past purchase history and suggests the appropriate way to input flower information 2. The system of claim 1.

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