System

Abstract

An object of a system according to an embodiment is to provide an appropriate product to each user by utilizing information on a producer and an end user.SOLUTION: A system includes a collection unit, an analysis unit, and a delivery unit. The collection unit collects information on a producer. The collection unit collects information of an end user. The analysis unit specifies a product suitable for each user based on the information collected by the collection unit. The delivery unit delivers the product specified by the analysis unit from the producer to the end user.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 technologies have not been able to effectively utilize information from producers and end users to provide appropriate products, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate products to each user by utilizing information on producers and end users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a delivery unit. The collection unit collects information about producers. The collection unit collects information about end users. The analysis unit identifies products appropriate for each user based on the information collected by the collection unit. The delivery unit delivers the products identified by the analysis unit from the producer to the end user. [Effects of the Invention]

[0007] The system according to the embodiment can utilize information on producers and end users to provide appropriate products to each user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A distribution system according to an embodiment of the present invention is a storeless, inventory-free distribution system that inputs and learns information about producers and end users into AI. This distribution system inputs producer information (e.g., product type, inventory status, production capacity, etc.) and end user information (e.g., purchase history, preferences, address, etc.) into AI, which then learns from this information to identify the optimal product for each user and delivers it directly from the producer to the end user. This reduces the producer's risk of waste and allows end users to directly deliver the optimal product. For example, the distribution system collects producer information, uses AI to learn from that information, and determines supply capacity. Next, it collects end user information, and uses AI to learn from that information to determine demand. The AI ​​compares supply capacity with demand to identify the optimal product for each user and delivers the product directly from the producer to the end user. This allows the distribution system to achieve efficient distribution without stores or inventory, reducing the risk of waste for producers and allowing end users to directly receive the optimal product.

[0029] The distribution system according to the embodiment includes a collection unit, an analysis unit, and a delivery unit. The collection unit collects information about producers. The producer information includes, but is not limited to, product types, inventory status, and production capacity. The collection unit acquires information from, for example, a database provided by the producer. The collection unit can also monitor inventory status in real time using sensors and IoT devices. The collection unit can also analyze past production data to evaluate the producer's production capacity. For example, the collection unit acquires product types and inventory status from the producer's database. The collection unit can also monitor inventory status in real time using sensors. The collection unit can also evaluate production capacity by analyzing past production data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input information acquired from the producer's database into AI, which may analyze and collect the information. The analysis unit identifies the optimal product for each user based on the information collected by the collection unit. The analysis unit may, for example, use AI to compare the producer's supply capacity with end-user demand. For example, the analysis unit uses AI to analyze the producer's supply capacity and end user demand and identify the optimal product. The analysis unit can also use AI to build a demand forecasting model and predict future demand. For example, the analysis unit uses AI to build a demand forecasting model and predict future demand. The analysis unit can also use AI to analyze end user preferences and identify the optimal product. For example, the analysis unit uses AI to analyze end user preferences and identify the optimal product. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs information collected by the collection unit into AI, which analyzes the information and identifies the optimal product. The delivery unit delivers the product identified by the analysis unit from the producer to the end user. The delivery unit delivers the product in cooperation with, for example, a logistics company. For example, the delivery unit delivers the product to the end user's address in cooperation with a logistics company.The delivery unit can also use AI to calculate the optimal delivery route, thereby achieving efficient delivery. For example, the delivery unit can calculate the optimal delivery route using AI, thereby achieving efficient delivery. The delivery unit can also monitor the delivery status in real time and notify the end user. For example, the delivery unit monitors the delivery status in real time and notifies the end user. Some or all of the above-mentioned processing in the delivery unit may be performed using AI, for example, or may be performed without using AI. For example, the delivery unit can deliver products in cooperation with a logistics company, and AI can calculate the optimal delivery route. As a result, the distribution system according to the embodiment can collect information on producers and end users, identify optimal products, and deliver the products from the producers to the end users.

[0030] The collection unit can collect information about the producer, such as product type, inventory status, and production capacity. The collection unit, for example, acquires the product type, inventory status, and production capacity from a database provided by the producer. The collection unit can also monitor the inventory status in real time using sensors and IoT devices. For example, the collection unit monitors the inventory status in real time using sensors. The collection unit can also analyze past production data to evaluate production capacity. For example, the collection unit analyzes past production data to evaluate production capacity. In this way, by collecting detailed information about the producer, it is possible to accurately grasp the supply capacity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information acquired from the producer's database into AI, which then analyzes and collects the information.

[0031] The collection unit can collect end user information such as purchase history, preferences, and addresses. For example, the collection unit acquires purchase history, preferences, and addresses from a database provided by the end user. The collection unit can also collect end user preferences using a questionnaire survey. For example, the collection unit collects end user preferences using a questionnaire survey. The collection unit can also understand end user preferences by analyzing the end user's past purchase history. For example, the collection unit analyzes the end user's past purchase history to understand preferences. This allows detailed information about end users to be collected, thereby accurately understanding demand. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information acquired from the end user's database into AI, which then analyzes and collects the information.

[0032] The analysis unit can compare the producer's supply capacity with the end user's demand and identify appropriate products for each user. The analysis unit, for example, uses AI to compare the producer's supply capacity with the end user's demand. For example, the analysis unit uses AI to analyze the producer's supply capacity and the end user's demand and identify the optimal product. The analysis unit can also use AI to build a demand forecasting model and predict future demand. For example, the analysis unit uses AI to build a demand forecasting model and predict future demand. The analysis unit can also use AI to analyze the end user's preferences and identify the optimal product. For example, the analysis unit uses AI to analyze the end user's preferences and identify the optimal product. In this way, the optimal product can be identified by comparing the supply capacity with the demand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the information collected by the collection unit into AI, and the AI ​​can analyze the information and identify the optimal product.

[0033] The delivery unit can deliver the identified products directly from the producer to the end user. The delivery unit, for example, works with a logistics company to deliver the products. For example, the delivery unit works with a logistics company to deliver the products to the end user's address. The delivery unit can also use AI to calculate the optimal delivery route to achieve efficient delivery. For example, the delivery unit uses AI to calculate the optimal delivery route to achieve efficient delivery. The delivery unit can also monitor the delivery status in real time and notify the end user. For example, the delivery unit monitors the delivery status in real time and notifies the end user. This allows direct delivery from the producer to the end user, thereby realizing a distribution system without stores or inventory. Some or all of the above-mentioned processing in the delivery unit may be performed using AI, for example, or may be performed without AI. For example, the delivery unit can deliver the products in cooperation with a logistics company, and AI can calculate the optimal delivery route.

[0034] The collection unit can analyze the producer's past production history and select an appropriate information collection method. For example, the collection unit selects the most efficient information collection method based on information provided by the producer in the past. The collection unit can also predict and collect information needed at a specific time from the producer's past production history. For example, the collection unit analyzes the producer's past production history and optimizes the frequency and timing of information collection. In this way, the analysis of past production history can select the optimal information collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's past production history data into AI, which analyzes the information and selects the optimal information collection method.

[0035] When collecting information, the collection unit can filter the information based on the producer's current production status and seasonal factors. For example, the collection unit grasps the producer's current production status in real time and collects only necessary information. The collection unit can also take seasonal factors into consideration and prioritize collection of information necessary for a specific season. For example, the collection unit adjusts the content and method of information collection depending on the producer's production status. This makes it possible to collect only necessary information by taking the current production status and seasonal factors into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's current production status data into AI, which then analyzes the information and performs filtering.

[0036] When collecting information, the collection unit can select an appropriate collection means depending on the producer's input method. For example, if the producer prefers voice input, the collection unit can collect information using voice recognition technology. Alternatively, if the producer prefers text input, the collection unit can provide a simple form to collect information. For example, if the producer prefers image input, the collection unit can collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the producer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's input data into AI, which then analyzes the information and selects the optimal collection means.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information based on the producer's geographical location information. For example, the collection unit prioritizes collecting region-specific information based on the producer's geographical location information. The collection unit can also prioritize collecting information related to climate and the environment, taking into account the producer's geographical location information. For example, the collection unit prioritizes collecting information related to logistics and delivery based on the producer's geographical location information. This allows highly relevant information to be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's geographical location information into AI, which analyzes the information and preferentially collects highly relevant information.

[0038] When collecting information, the collection unit can analyze the social media activities of producers and collect related information. For example, the collection unit can analyze the social media posts of producers and collect the latest production status and trends. The collection unit can also collect information useful for demand forecasting from the social media activities of producers. For example, the collection unit can collect improvements and new ideas based on feedback from producers on social media. In this way, the latest production status and trends can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the social media data of producers into AI, which can analyze the information and collect related information.

[0039] The collection unit can customize the collection method by reflecting the producer's past feedback when collecting information. For example, the collection unit improves the content of information collection questions based on the producer's past feedback. The collection unit can also customize the information collection interface by reflecting the producer's past feedback. For example, the collection unit adjusts the frequency and timing of information collection by referring to the producer's past feedback. In this way, the content of information collection questions and the interface can be improved by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's past feedback data into AI, which analyzes the information and customizes the collection method.

[0040] The collection unit can analyze the end user's past purchase history and select an appropriate information collection method. For example, the collection unit selects the most efficient information collection method based on products the end user has purchased in the past. The collection unit can also predict and collect information needed at a specific time from the end user's past purchase history. For example, the collection unit analyzes the end user's past purchase history and optimizes the frequency and timing of information collection. In this way, the analysis of the past purchase history can select the optimal information collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's past purchase history data into AI, which analyzes the information and selects the optimal information collection method.

[0041] When collecting information, the collection unit can perform filtering based on the end user's current living situation and areas of interest. For example, the collection unit grasps the end user's current living situation in real time and collects only necessary information. The collection unit can also take into account the end user's areas of interest and prioritize collecting related information. For example, the collection unit adjusts the content and method of information collection according to the end user's living situation. This makes it possible to collect only necessary information by taking into account the end user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's current living situation data into AI, which then analyzes the information and performs filtering.

[0042] When collecting information, the collection unit can select an appropriate collection means depending on the end user's input method. For example, if the end user prefers voice input, the collection unit can collect information using voice recognition technology. Alternatively, if the end user prefers text input, the collection unit can provide a simple form to collect information. For example, if the end user prefers image input, the collection unit can collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the end user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's input data into AI, which then analyzes the information and selects the optimal collection means.

[0043] When collecting information, the collection unit can prioritize collecting highly relevant information based on the end user's geographical location information. For example, the collection unit prioritizes collecting region-specific information based on the end user's geographical location information. The collection unit can also prioritize collecting information related to climate and the environment, taking the end user's geographical location information into consideration. For example, the collection unit prioritizes collecting information related to logistics and delivery based on the end user's geographical location information. This allows highly relevant information to be collected preferentially by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's geographical location information into AI, which analyzes the information and preferentially collects highly relevant information.

[0044] When collecting information, the collection unit can analyze the end user's social media activities and collect related information. For example, the collection unit can analyze the end user's social media posts and collect the latest areas of interest and trends. The collection unit can also collect information useful for demand forecasting from the end user's social media activities. For example, the collection unit can collect improvements and new ideas based on the end user's feedback on social media. In this way, the latest areas of interest and trends can be collected by analyzing the social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the end user's social media data into AI, which can analyze the information and collect related information.

[0045] The collection unit can customize the collection method by reflecting the end user's past feedback when collecting information. For example, the collection unit improves the content of questions for information collection based on the end user's past feedback. The collection unit can also customize the information collection interface by reflecting the end user's past feedback. For example, the collection unit adjusts the frequency and timing of information collection by referring to the end user's past feedback. In this way, the content of questions for information collection and the interface can be improved by reflecting the past feedback. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's past feedback data into AI, which analyzes the information and customizes the collection method.

[0046] During analysis, the analysis unit can compare the producer's supply capacity with the end user's demand to improve the accuracy of the analysis. For example, the analysis unit compares the producer's supply capacity with the end user's demand to identify the optimal product. The analysis unit can also consider the producer's supply capacity and identify products that meet the end user's demand. For example, the analysis unit optimizes the producer's supply capacity based on the end user's demand. This improves the accuracy of the analysis by comparing the supply capacity with the demand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the producer's supply capacity data and the end user's demand data into AI, which can analyze the information and identify the optimal product.

[0047] During analysis, the analysis unit can perform analysis based on the end user's attribute information. The analysis unit can identify the most suitable product, for example, by taking into account the end user's age and gender. The analysis unit can also identify the most suitable product based on the end user's purchase history. For example, the analysis unit can identify the most suitable product by taking into account the end user's preferences and areas of interest. This makes it possible to identify a more suitable product by taking into account the end user's attribute information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the end user's attribute information data into AI, which can analyze the information and identify the most suitable product.

[0048] During analysis, the analysis unit can weight the analysis based on the end user's purchase frequency. For example, the analysis unit prioritizes identifying products that are frequently purchased by the end user. The analysis unit can also weight the importance of products based on the end user's purchase frequency. For example, the analysis unit takes into account the end user's purchase frequency to identify optimal products. By weighting the analysis based on purchase frequency, more important products can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input end user purchase frequency data into AI, which can analyze the information to identify optimal products.

[0049] During analysis, the analysis unit can perform analysis based on the geographic distribution of end users. For example, the analysis unit identifies region-specific products based on the geographic distribution of end users. The analysis unit can also propose optimal delivery routes taking the geographic distribution of end users into consideration. For example, the analysis unit performs demand forecasting based on the geographic distribution of end users and identifies optimal products. This makes it possible to identify region-specific products by taking the geographic distribution into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographic distribution data of end users into AI, which then analyzes the information to identify optimal products.

[0050] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user's purchasing history. The analysis unit can also identify optimal products by referring to literature related to the end user's preferences. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user's attribute information. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the end user's relevant literature data into AI, which can analyze the information and identify optimal products.

[0051] During analysis, the analysis unit can perform analysis based on the market value of the end user. For example, the analysis unit identifies the most suitable product based on the market value of the end user. The analysis unit can also weight the importance of the product by taking the market value of the end user into consideration. For example, the analysis unit identifies the most suitable product based on the market value of the end user. In this way, by taking market value into consideration, more important products can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input end user market value data into AI, which analyzes the information to identify the most suitable product.

[0052] At the time of delivery, the delivery unit can select the optimal delivery method by analyzing the end user's past delivery history. For example, the delivery unit selects the optimal delivery method based on the end user's past delivery history. The delivery unit can also predict the optimal delivery method for a specific period based on the end user's past delivery history. For example, the delivery unit analyzes the end user's past delivery history to optimize the frequency and timing of deliveries. In this way, the optimal delivery method can be selected by analyzing the past delivery history. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's past delivery history data into AI, which then analyzes the information and selects the optimal delivery method.

[0053] The delivery unit can customize the delivery method based on the end user's current living situation at the time of delivery. For example, the delivery unit grasps the end user's current living situation in real time and selects the optimal delivery method. The delivery unit can also adjust the content and method of delivery according to the end user's living situation. For example, the delivery unit optimizes the timing of delivery by taking the end user's living situation into consideration. This enables more appropriate delivery by customizing the delivery method based on the current living situation. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input data on the end user's current living situation into AI, which analyzes the information and selects the optimal delivery method.

[0054] The delivery unit can improve the delivery method by reflecting end user feedback during delivery. The delivery unit can improve the delivery method, for example, based on end user feedback. The delivery unit can also customize the delivery interface by reflecting end user feedback. For example, the delivery unit can adjust the frequency and timing of delivery based on end user feedback. In this way, the delivery method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the delivery unit can be performed using, for example, AI, or can be performed without using AI. For example, the delivery unit can input end user feedback data into AI, which can analyze the information and improve the delivery method.

[0055] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the end user's geographical location information. The delivery unit, for example, selects the optimal delivery method based on the end user's geographical location information. The delivery unit can also propose the optimal delivery route, taking into account the end user's geographical location information. For example, the delivery unit optimizes the timing of delivery based on the end user's geographical location information. This allows the optimal delivery method to be selected by taking into account the geographical location information. Some or all of the above-described processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI. For example, the delivery unit can input the end user's geographical location information into AI, which analyzes the information and selects the optimal delivery method.

[0056] At the time of delivery, the delivery unit can analyze the end user's social media activity and suggest a delivery method. For example, the delivery unit can analyze the end user's social media posts and suggest the optimal delivery method. The delivery unit can also suggest a delivery method that is useful for demand forecasting based on the end user's social media activity. For example, the delivery unit improves the delivery method based on the end user's feedback on social media. In this way, the optimal delivery method can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's social media data into AI, which then analyzes the information and suggests the optimal delivery method.

[0057] The delivery unit can customize the delivery method by reflecting the end user's past feedback at the time of delivery. The delivery unit can improve the delivery method based on the end user's past feedback, for example. The delivery unit can also customize the delivery interface by reflecting the end user's past feedback. For example, the delivery unit can adjust the frequency and timing of delivery by referring to the end user's past feedback. In this way, the delivery method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's past feedback data into AI, which analyzes the information and customizes the delivery method.

[0058] During analysis, the analysis unit can compare the producer's supply capacity with the end user's demand to improve the accuracy of the analysis. For example, the analysis unit compares the producer's supply capacity with the end user's demand to identify the optimal product. The analysis unit can also consider the producer's supply capacity and identify products that meet the end user's demand. For example, the analysis unit optimizes the producer's supply capacity based on the end user's demand. This improves the accuracy of the analysis by comparing the supply capacity with the demand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the producer's supply capacity data and the end user's demand data into AI, which can analyze the information and identify the optimal product.

[0059] During analysis, the analysis unit can perform analysis based on the end user's attribute information. The analysis unit can identify the most suitable product, for example, by taking into account the end user's age and gender. The analysis unit can also identify the most suitable product based on the end user's purchase history. For example, the analysis unit can identify the most suitable product by taking into account the end user's preferences and areas of interest. This makes it possible to identify a more suitable product by taking into account the end user's attribute information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the end user's attribute information data into AI, which can analyze the information and identify the most suitable product.

[0060] During analysis, the analysis unit can weight the analysis based on the end user's purchase frequency. For example, the analysis unit prioritizes identifying products that are frequently purchased by the end user. The analysis unit can also weight the importance of products based on the end user's purchase frequency. For example, the analysis unit takes into account the end user's purchase frequency to identify optimal products. By weighting the analysis based on purchase frequency, more important products can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input end user purchase frequency data into AI, which can analyze the information to identify optimal products.

[0061] During analysis, the analysis unit can perform analysis based on the geographic distribution of end users. For example, the analysis unit identifies region-specific products based on the geographic distribution of end users. The analysis unit can also propose optimal delivery routes taking the geographic distribution of end users into consideration. For example, the analysis unit performs demand forecasting based on the geographic distribution of end users and identifies optimal products. This makes it possible to identify region-specific products by taking the geographic distribution into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographic distribution data of end users into AI, which then analyzes the information to identify optimal products.

[0062] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user's purchasing history. The analysis unit can also identify optimal products by referring to literature related to the end user's preferences. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user's attribute information. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the end user's relevant literature data into AI, which can analyze the information and identify optimal products.

[0063] During analysis, the analysis unit can perform analysis based on the market value of the end user. For example, the analysis unit identifies the most suitable product based on the market value of the end user. The analysis unit can also weight the importance of the product by taking the market value of the end user into consideration. For example, the analysis unit identifies the most suitable product based on the market value of the end user. In this way, by taking market value into consideration, more important products can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input end user market value data into AI, which analyzes the information to identify the most suitable product.

[0064] At the time of delivery, the delivery unit can select the optimal delivery method by analyzing the end user's past delivery history. For example, the delivery unit selects the optimal delivery method based on the end user's past delivery history. The delivery unit can also predict the optimal delivery method for a specific period based on the end user's past delivery history. For example, the delivery unit analyzes the end user's past delivery history to optimize the frequency and timing of deliveries. In this way, the optimal delivery method can be selected by analyzing the past delivery history. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's past delivery history data into AI, which then analyzes the information and selects the optimal delivery method.

[0065] The delivery unit can customize the delivery method based on the end user's current living situation at the time of delivery. For example, the delivery unit grasps the end user's current living situation in real time and selects the optimal delivery method. The delivery unit can also adjust the content and method of delivery according to the end user's living situation. For example, the delivery unit optimizes the timing of delivery by taking the end user's living situation into consideration. This enables more appropriate delivery by customizing the delivery method based on the current living situation. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input data on the end user's current living situation into AI, which analyzes the information and selects the optimal delivery method.

[0066] The delivery unit can improve the delivery method by reflecting end user feedback during delivery. The delivery unit can improve the delivery method, for example, based on end user feedback. The delivery unit can also customize the delivery interface by reflecting end user feedback. For example, the delivery unit can adjust the frequency and timing of delivery based on end user feedback. In this way, the delivery method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the delivery unit can be performed using, for example, AI, or can be performed without using AI. For example, the delivery unit can input end user feedback data into AI, which can analyze the information and improve the delivery method.

[0067] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the end user's geographical location information. The delivery unit, for example, selects the optimal delivery method based on the end user's geographical location information. The delivery unit can also propose the optimal delivery route, taking into account the end user's geographical location information. For example, the delivery unit optimizes the timing of delivery based on the end user's geographical location information. This allows the optimal delivery method to be selected by taking into account the geographical location information. Some or all of the above-described processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI. For example, the delivery unit can input the end user's geographical location information into AI, which analyzes the information and selects the optimal delivery method.

[0068] At the time of delivery, the delivery unit can analyze the end user's social media activity and suggest a delivery method. For example, the delivery unit can analyze the end user's social media posts and suggest the optimal delivery method. The delivery unit can also suggest a delivery method that is useful for demand forecasting based on the end user's social media activity. For example, the delivery unit improves the delivery method based on the end user's feedback on social media. In this way, the optimal delivery method can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's social media data into AI, which then analyzes the information and suggests the optimal delivery method.

[0069] The delivery unit can customize the delivery method by reflecting the end user's past feedback at the time of delivery. The delivery unit can improve the delivery method based on the end user's past feedback, for example. The delivery unit can also customize the delivery interface by reflecting the end user's past feedback. For example, the delivery unit can adjust the frequency and timing of delivery by referring to the end user's past feedback. In this way, the delivery method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's past feedback data into AI, which analyzes the information and customizes the delivery method.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The analysis unit can predict future purchasing patterns based on the end user's purchasing history. For example, the analysis unit analyzes data on products the end user has purchased in the past and identifies products that the end user is likely to purchase next. The analysis unit can also predict products that will be in high demand at specific times, taking into account the end user's purchasing frequency and seasonal purchasing trends. Furthermore, the analysis unit can suggest related products based on the end user's purchasing history. This makes it possible to predict future purchasing patterns and suggest more appropriate products by utilizing the end user's purchasing history.

[0072] The collection unit can analyze the end user's social media activities to understand the end user's areas of interest and trends. For example, the collection unit can analyze the posts and comments shared by the end user on social media to identify the end user's current areas of interest. The collection unit can also analyze the accounts the end user follows and groups the end user joins to understand the topics in which the end user is interested. Furthermore, the collection unit can evaluate the strength of the end user's interests based on the frequency of the end user's social media activity and engagement. This makes it possible to understand the end user's areas of interest and trends by utilizing the end user's social media activities, and to make more appropriate product suggestions.

[0073] The delivery unit can propose the optimal delivery route based on the end user's geographical location information. For example, the delivery unit calculates the shortest delivery route based on the end user's address information. The delivery unit can also propose the optimal delivery time by taking into account traffic conditions and weather information. Furthermore, the delivery unit can also optimize the route to make deliveries to multiple end users more efficient. This makes it possible to utilize the end user's geographical location information to achieve efficient deliveries.

[0074] The collection unit can customize the information collection method based on the end user's past feedback. For example, the collection unit can analyze feedback provided by the end user in the past and improve the content of the information collection questions. The collection unit can also reflect the end user's feedback and make the information collection interface easier to use. Furthermore, the collection unit can adjust the frequency and timing of information collection based on the end user's feedback. In this way, the end user's feedback can be utilized to customize the information collection method and enable more efficient information collection.

[0075] The analysis unit can make bundle suggestions for related products based on the end user's purchase history. For example, the analysis unit can suggest products that are highly related to products purchased in the past by the end user. The analysis unit can also analyze the end user's purchase history and suggest product bundles based on specific themes. Furthermore, the analysis unit can also make seasonal bundle suggestions by taking into account the end user's purchasing patterns. This makes it possible to make bundle suggestions for related products by utilizing the end user's purchase history, resulting in more attractive product suggestions.

[0076] The delivery department can select the optimal delivery method based on the end user's past delivery history. For example, the delivery department can analyze the delivery methods used by the end user in the past and select the method that provides the highest level of satisfaction. The delivery department can also predict the optimal delivery method for a specific period based on the end user's past delivery history. Furthermore, the delivery department can improve the delivery method by reflecting past feedback from the end user. In this way, the optimal delivery method can be selected by utilizing the end user's past delivery history, allowing for the provision of a service with a higher level of satisfaction.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The collection unit collects information from producers. This information includes product types, inventory status, production capacity, etc. The collection unit obtains information from databases provided by producers and can also monitor inventory status in real time using sensors and IoT devices. It can also analyze past production data to evaluate production capacity. These processes can sometimes be performed using AI. Step 2: The collection unit collects end-user information. End-user information includes purchase history, preferences, and demand forecasts. The collection unit obtains information from a database provided by the end user and can also monitor demand in real time using sensors and IoT devices. It can also analyze past purchase data to make demand forecasts. These processes can sometimes be performed using AI. Step 3: The analysis unit identifies the best product for each user based on the information collected by the collection unit. The analysis unit uses AI to compare the producer's supply capacity with the end user's demand and identify the best product. It can also build a demand forecasting model to predict future demand. It can also analyze the end user's preferences and identify the best product. Step 4: The delivery unit delivers the products identified by the analysis unit from the producer to the end user. The delivery unit works in cooperation with logistics companies to deliver the products and uses AI to calculate the optimal delivery route, ensuring efficient delivery. It can also monitor delivery status in real time and notify the end user.

[0079] (Example 2) A distribution system according to an embodiment of the present invention is a storeless, inventory-free distribution system that inputs and learns information about producers and end users into AI. This distribution system inputs producer information (e.g., product type, inventory status, production capacity, etc.) and end user information (e.g., purchase history, preferences, address, etc.) into AI, which then learns from this information to identify the optimal product for each user and delivers it directly from the producer to the end user. This reduces the producer's risk of waste and allows end users to directly deliver the optimal product. For example, the distribution system collects producer information, uses AI to learn from that information, and determines supply capacity. Next, it collects end user information, and uses AI to learn from that information to determine demand. The AI ​​compares supply capacity with demand to identify the optimal product for each user and delivers the product directly from the producer to the end user. This allows the distribution system to achieve efficient distribution without stores or inventory, reducing the risk of waste for producers and allowing end users to directly receive the optimal product.

[0080] The distribution system according to the embodiment includes a collection unit, an analysis unit, and a delivery unit. The collection unit collects information about producers. The producer information includes, but is not limited to, product types, inventory status, and production capacity. The collection unit acquires information from, for example, a database provided by the producer. The collection unit can also monitor inventory status in real time using sensors and IoT devices. The collection unit can also analyze past production data to evaluate the producer's production capacity. For example, the collection unit acquires product types and inventory status from the producer's database. The collection unit can also monitor inventory status in real time using sensors. The collection unit can also evaluate production capacity by analyzing past production data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input information acquired from the producer's database into AI, which may analyze and collect the information. The analysis unit identifies the optimal product for each user based on the information collected by the collection unit. The analysis unit may, for example, use AI to compare the producer's supply capacity with end-user demand. For example, the analysis unit uses AI to analyze the producer's supply capacity and end user demand and identify the optimal product. The analysis unit can also use AI to build a demand forecasting model and predict future demand. For example, the analysis unit uses AI to build a demand forecasting model and predict future demand. The analysis unit can also use AI to analyze end user preferences and identify the optimal product. For example, the analysis unit uses AI to analyze end user preferences and identify the optimal product. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit inputs information collected by the collection unit into AI, which analyzes the information and identifies the optimal product. The delivery unit delivers the product identified by the analysis unit from the producer to the end user. The delivery unit delivers the product in cooperation with, for example, a logistics company. For example, the delivery unit delivers the product to the end user's address in cooperation with a logistics company.The delivery unit can also use AI to calculate the optimal delivery route, thereby achieving efficient delivery. For example, the delivery unit can calculate the optimal delivery route using AI, thereby achieving efficient delivery. The delivery unit can also monitor the delivery status in real time and notify the end user. For example, the delivery unit monitors the delivery status in real time and notifies the end user. Some or all of the above-mentioned processing in the delivery unit may be performed using AI, for example, or may be performed without using AI. For example, the delivery unit can deliver products in cooperation with a logistics company, and AI can calculate the optimal delivery route. As a result, the distribution system according to the embodiment can collect information on producers and end users, identify optimal products, and deliver the products from the producers to the end users.

[0081] The collection unit can collect information about the producer, such as product type, inventory status, and production capacity. The collection unit, for example, acquires the product type, inventory status, and production capacity from a database provided by the producer. The collection unit can also monitor the inventory status in real time using sensors and IoT devices. For example, the collection unit monitors the inventory status in real time using sensors. The collection unit can also analyze past production data to evaluate production capacity. For example, the collection unit analyzes past production data to evaluate production capacity. In this way, by collecting detailed information about the producer, it is possible to accurately grasp the supply capacity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information acquired from the producer's database into AI, which then analyzes and collects the information.

[0082] The collection unit can collect end user information such as purchase history, preferences, and addresses. For example, the collection unit acquires purchase history, preferences, and addresses from a database provided by the end user. The collection unit can also collect end user preferences using a questionnaire survey. For example, the collection unit collects end user preferences using a questionnaire survey. The collection unit can also understand end user preferences by analyzing the end user's past purchase history. For example, the collection unit analyzes the end user's past purchase history to understand preferences. This allows detailed information about end users to be collected, thereby accurately understanding demand. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information acquired from the end user's database into AI, which then analyzes and collects the information.

[0083] The analysis unit can compare the producer's supply capacity with the end user's demand and identify appropriate products for each user. The analysis unit, for example, uses AI to compare the producer's supply capacity with the end user's demand. For example, the analysis unit uses AI to analyze the producer's supply capacity and the end user's demand and identify the optimal product. The analysis unit can also use AI to build a demand forecasting model and predict future demand. For example, the analysis unit uses AI to build a demand forecasting model and predict future demand. The analysis unit can also use AI to analyze the end user's preferences and identify the optimal product. For example, the analysis unit uses AI to analyze the end user's preferences and identify the optimal product. In this way, the optimal product can be identified by comparing the supply capacity with the demand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the information collected by the collection unit into AI, and the AI ​​can analyze the information and identify the optimal product.

[0084] The delivery unit can deliver the identified products directly from the producer to the end user. The delivery unit, for example, works with a logistics company to deliver the products. For example, the delivery unit works with a logistics company to deliver the products to the end user's address. The delivery unit can also use AI to calculate the optimal delivery route to achieve efficient delivery. For example, the delivery unit uses AI to calculate the optimal delivery route to achieve efficient delivery. The delivery unit can also monitor the delivery status in real time and notify the end user. For example, the delivery unit monitors the delivery status in real time and notifies the end user. This allows direct delivery from the producer to the end user, thereby realizing a distribution system without stores or inventory. Some or all of the above-mentioned processing in the delivery unit may be performed using AI, for example, or may be performed without AI. For example, the delivery unit can deliver the products in cooperation with a logistics company, and AI can calculate the optimal delivery route.

[0085] The collection unit can estimate the producer's emotions and adjust the timing of information collection based on the estimated emotions of the producer. For example, if the producer is feeling stressed, the collection unit reduces the frequency of information collection and collects information when the producer is relaxed. Furthermore, if the producer is relaxed, the collection unit can collect detailed information and obtain more accurate data. Furthermore, if the producer is busy, the collection unit can quickly collect information by using simplified questions. For example, if the producer is feeling stressed, the collection unit reduces the frequency of information collection and collects information when the producer is relaxed. Furthermore, if the producer is relaxed, the collection unit can collect detailed information and obtain more accurate data. Furthermore, if the producer is busy, the collection unit can quickly collect information by using simplified questions. In this way, by adjusting the timing of information collection according to the producer's emotions, more appropriate information can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the producer's emotion data into the generation AI, which may infer the emotion, and adjust the timing of information collection based on the result.

[0086] The collection unit can analyze the producer's past production history and select an appropriate information collection method. For example, the collection unit selects the most efficient information collection method based on information provided by the producer in the past. The collection unit can also predict and collect information needed at a specific time from the producer's past production history. For example, the collection unit analyzes the producer's past production history and optimizes the frequency and timing of information collection. In this way, the analysis of past production history can select the optimal information collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's past production history data into AI, which analyzes the information and selects the optimal information collection method.

[0087] When collecting information, the collection unit can filter the information based on the producer's current production status and seasonal factors. For example, the collection unit grasps the producer's current production status in real time and collects only necessary information. The collection unit can also take seasonal factors into consideration and prioritize collection of information necessary for a specific season. For example, the collection unit adjusts the content and method of information collection depending on the producer's production status. This makes it possible to collect only necessary information by taking the current production status and seasonal factors into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's current production status data into AI, which then analyzes the information and performs filtering.

[0088] When collecting information, the collection unit can select an appropriate collection means depending on the producer's input method. For example, if the producer prefers voice input, the collection unit can collect information using voice recognition technology. Alternatively, if the producer prefers text input, the collection unit can provide a simple form to collect information. For example, if the producer prefers image input, the collection unit can collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the producer's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's input data into AI, which then analyzes the information and selects the optimal collection means.

[0089] The collection unit can estimate the producer's emotions and determine the priority of information to be collected based on the estimated emotions of the producer. For example, if the producer is stressed, the collection unit can prioritize collecting only important information. Furthermore, if the producer is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the producer is busy, the collection unit can also prioritize collecting information that can be collected quickly. For example, if the producer is stressed, the collection unit can prioritize collecting only important information. Furthermore, if the producer is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the producer is busy, the collection unit can also prioritize collecting information that can be collected quickly. In this way, by determining the priority of information according to the producer's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the producer's emotional data into the generation AI, which can then infer the emotion and prioritize the information based on the results.

[0090] When collecting information, the collection unit can prioritize collecting highly relevant information based on the producer's geographical location information. For example, the collection unit prioritizes collecting region-specific information based on the producer's geographical location information. The collection unit can also prioritize collecting information related to climate and the environment, taking into account the producer's geographical location information. For example, the collection unit prioritizes collecting information related to logistics and delivery based on the producer's geographical location information. This allows highly relevant information to be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's geographical location information into AI, which analyzes the information and preferentially collects highly relevant information.

[0091] When collecting information, the collection unit can analyze the social media activities of producers and collect related information. For example, the collection unit can analyze the social media posts of producers and collect the latest production status and trends. The collection unit can also collect information useful for demand forecasting from the social media activities of producers. For example, the collection unit can collect improvements and new ideas based on feedback from producers on social media. In this way, the latest production status and trends can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the social media data of producers into AI, which can analyze the information and collect related information.

[0092] The collection unit can customize the collection method by reflecting the producer's past feedback when collecting information. For example, the collection unit improves the content of information collection questions based on the producer's past feedback. The collection unit can also customize the information collection interface by reflecting the producer's past feedback. For example, the collection unit adjusts the frequency and timing of information collection by referring to the producer's past feedback. In this way, the content of information collection questions and the interface can be improved by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the producer's past feedback data into AI, which analyzes the information and customizes the collection method.

[0093] The collection unit can estimate the end user's emotions and adjust the timing of information collection based on the estimated end user's emotions. For example, when the end user is feeling stressed, the collection unit reduces the frequency of information collection and collects information when the end user is relaxed. Furthermore, when the end user is relaxed, the collection unit collects detailed information to obtain more accurate data. Furthermore, when the end user is busy, the collection unit can quickly collect information by using simplified questions. For example, when the end user is feeling stressed, the collection unit reduces the frequency of information collection and collects information when the end user is relaxed. Furthermore, when the end user is relaxed, the collection unit collects detailed information to obtain more accurate data. Furthermore, when the end user is busy, the collection unit can quickly collect information by using simplified questions. In this way, by adjusting the timing of information collection according to the end user's emotions, more appropriate information can be collected. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the end user's emotional data into the generation AI, which may infer the emotion, and adjust the timing of information collection based on the result.

[0094] The collection unit can analyze the end user's past purchase history and select an appropriate information collection method. For example, the collection unit selects the most efficient information collection method based on products the end user has purchased in the past. The collection unit can also predict and collect information needed at a specific time from the end user's past purchase history. For example, the collection unit analyzes the end user's past purchase history and optimizes the frequency and timing of information collection. In this way, the analysis of the past purchase history can select the optimal information collection method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's past purchase history data into AI, which analyzes the information and selects the optimal information collection method.

[0095] When collecting information, the collection unit can perform filtering based on the end user's current living situation and areas of interest. For example, the collection unit grasps the end user's current living situation in real time and collects only necessary information. The collection unit can also take into account the end user's areas of interest and prioritize collecting related information. For example, the collection unit adjusts the content and method of information collection according to the end user's living situation. This makes it possible to collect only necessary information by taking into account the end user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's current living situation data into AI, which then analyzes the information and performs filtering.

[0096] When collecting information, the collection unit can select an appropriate collection means depending on the end user's input method. For example, if the end user prefers voice input, the collection unit can collect information using voice recognition technology. Alternatively, if the end user prefers text input, the collection unit can provide a simple form to collect information. For example, if the end user prefers image input, the collection unit can collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the end user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's input data into AI, which then analyzes the information and selects the optimal collection means.

[0097] The collection unit can estimate the end user's emotions and determine the priority of information to be collected based on the estimated end user's emotions. For example, when the end user is stressed, the collection unit prioritizes collecting only important information. Furthermore, when the end user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the end user is busy, the collection unit can also prioritize collecting information that can be collected quickly. For example, when the end user is stressed, the collection unit prioritizes collecting only important information. Furthermore, when the end user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the end user is busy, the collection unit can also prioritize collecting information that can be collected quickly. In this way, by determining the priority of information according to the end user's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the end user's emotional data into the generation AI, which can then infer the emotion and prioritize the information based on the results.

[0098] When collecting information, the collection unit can prioritize collecting highly relevant information based on the end user's geographical location information. For example, the collection unit prioritizes collecting region-specific information based on the end user's geographical location information. The collection unit can also prioritize collecting information related to climate and the environment, taking the end user's geographical location information into consideration. For example, the collection unit prioritizes collecting information related to logistics and delivery based on the end user's geographical location information. This allows highly relevant information to be collected preferentially by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's geographical location information into AI, which analyzes the information and preferentially collects highly relevant information.

[0099] When collecting information, the collection unit can analyze the end user's social media activities and collect related information. For example, the collection unit can analyze the end user's social media posts and collect the latest areas of interest and trends. The collection unit can also collect information useful for demand forecasting from the end user's social media activities. For example, the collection unit can collect improvements and new ideas based on the end user's feedback on social media. In this way, the latest areas of interest and trends can be collected by analyzing the social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the end user's social media data into AI, which can analyze the information and collect related information.

[0100] The collection unit can customize the collection method by reflecting the end user's past feedback when collecting information. For example, the collection unit improves the content of questions for information collection based on the end user's past feedback. The collection unit can also customize the information collection interface by reflecting the end user's past feedback. For example, the collection unit adjusts the frequency and timing of information collection by referring to the end user's past feedback. In this way, the content of questions for information collection and the interface can be improved by reflecting the past feedback. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the end user's past feedback data into AI, which analyzes the information and customizes the collection method.

[0101] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis to identify the most suitable product. Furthermore, when the user is in a hurry, the analysis unit can quickly perform an analysis to identify the most suitable product. Furthermore, when the user is excited, the analysis unit can prioritize visually stimulating products. For example, when the user is relaxed, the analysis unit performs a detailed analysis to identify the most suitable product. Furthermore, when the user is in a hurry, the analysis unit can quickly perform an analysis to identify the most suitable product. Furthermore, when the user is excited, the analysis unit can prioritize visually stimulating products. Thus, by adjusting the analysis criteria according to the user's emotions, more suitable products can be identified. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the analysis criteria based on the results.

[0102] During analysis, the analysis unit can compare the producer's supply capacity with the end user's demand to improve the accuracy of the analysis. For example, the analysis unit compares the producer's supply capacity with the end user's demand to identify the optimal product. The analysis unit can also consider the producer's supply capacity and identify products that meet the end user's demand. For example, the analysis unit optimizes the producer's supply capacity based on the end user's demand. This improves the accuracy of the analysis by comparing the supply capacity with the demand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the producer's supply capacity data and the end user's demand data into AI, which can analyze the information and identify the optimal product.

[0103] During analysis, the analysis unit can perform analysis based on the end user's attribute information. The analysis unit can identify the most suitable product, for example, by taking into account the end user's age and gender. The analysis unit can also identify the most suitable product based on the end user's purchase history. For example, the analysis unit can identify the most suitable product by taking into account the end user's preferences and areas of interest. This makes it possible to identify a more suitable product by taking into account the end user's attribute information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the end user's attribute information data into AI, which can analyze the information and identify the most suitable product.

[0104] During analysis, the analysis unit can weight the analysis based on the end user's purchase frequency. For example, the analysis unit prioritizes identifying products that are frequently purchased by the end user. The analysis unit can also weight the importance of products based on the end user's purchase frequency. For example, the analysis unit takes into account the end user's purchase frequency to identify optimal products. By weighting the analysis based on purchase frequency, more important products can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input end user purchase frequency data into AI, which can analyze the information to identify optimal products.

[0105] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is excited, the analysis unit can prioritize displaying visually stimulating analysis results. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is excited, the analysis unit can prioritize displaying visually stimulating analysis results. In this way, by adjusting the display order of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the display order of the analysis results based on that result.

[0106] During analysis, the analysis unit can perform analysis based on the geographic distribution of end users. For example, the analysis unit identifies region-specific products based on the geographic distribution of end users. The analysis unit can also propose optimal delivery routes taking the geographic distribution of end users into consideration. For example, the analysis unit performs demand forecasting based on the geographic distribution of end users and identifies optimal products. This makes it possible to identify region-specific products by taking the geographic distribution into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographic distribution data of end users into AI, which then analyzes the information to identify optimal products.

[0107] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user's purchasing history. The analysis unit can also identify optimal products by referring to literature related to the end user's preferences. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user's attribute information. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the end user's relevant literature data into AI, which can analyze the information and identify optimal products.

[0108] During analysis, the analysis unit can perform analysis based on the market value of the end user. For example, the analysis unit identifies the most suitable product based on the market value of the end user. The analysis unit can also weight the importance of the product by taking the market value of the end user into consideration. For example, the analysis unit identifies the most suitable product based on the market value of the end user. In this way, by taking market value into consideration, more important products can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input end user market value data into AI, which analyzes the information to identify the most suitable product.

[0109] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. For example, the delivery unit selects a normal delivery method when the user is relaxed. Furthermore, the delivery unit can select a rapid delivery method when the user is in a hurry. Furthermore, the delivery unit can select a special delivery method when the user is excited. For example, the delivery unit selects the normal delivery method when the user is relaxed. Furthermore, the delivery unit can select a rapid delivery method when the user is in a hurry. Furthermore, the delivery unit can select a special delivery method when the user is excited. This enables more appropriate delivery by adjusting the delivery method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery department can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the delivery method based on the results.

[0110] At the time of delivery, the delivery unit can select the optimal delivery method by analyzing the end user's past delivery history. For example, the delivery unit selects the optimal delivery method based on the end user's past delivery history. The delivery unit can also predict the optimal delivery method for a specific period based on the end user's past delivery history. For example, the delivery unit analyzes the end user's past delivery history to optimize the frequency and timing of deliveries. In this way, the optimal delivery method can be selected by analyzing the past delivery history. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's past delivery history data into AI, which then analyzes the information and selects the optimal delivery method.

[0111] The delivery unit can customize the delivery method based on the end user's current living situation at the time of delivery. For example, the delivery unit grasps the end user's current living situation in real time and selects the optimal delivery method. The delivery unit can also adjust the content and method of delivery according to the end user's living situation. For example, the delivery unit optimizes the timing of delivery by taking the end user's living situation into consideration. This enables more appropriate delivery by customizing the delivery method based on the current living situation. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input data on the end user's current living situation into AI, which analyzes the information and selects the optimal delivery method.

[0112] The delivery unit can improve the delivery method by reflecting end user feedback during delivery. The delivery unit can improve the delivery method, for example, based on end user feedback. The delivery unit can also customize the delivery interface by reflecting end user feedback. For example, the delivery unit can adjust the frequency and timing of delivery based on end user feedback. In this way, the delivery method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the delivery unit can be performed using, for example, AI, or can be performed without using AI. For example, the delivery unit can input end user feedback data into AI, which can analyze the information and improve the delivery method.

[0113] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. For example, if the user is relaxed, the delivery unit can set a normal delivery priority. Furthermore, if the user is in a hurry, the delivery unit can prioritize quick delivery. Furthermore, if the user is excited, the delivery unit can also prioritize special delivery. For example, if the user is relaxed, the delivery unit can set a normal delivery priority. Furthermore, if the user is in a hurry, the delivery unit can also prioritize quick delivery. Furthermore, if the user is excited, the delivery unit can also prioritize special delivery. This enables more appropriate delivery by determining delivery priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery department can input the user's emotional data into the generation AI, which then infers the emotion and determines delivery priorities based on the results.

[0114] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the end user's geographical location information. The delivery unit, for example, selects the optimal delivery method based on the end user's geographical location information. The delivery unit can also propose the optimal delivery route, taking into account the end user's geographical location information. For example, the delivery unit optimizes the timing of delivery based on the end user's geographical location information. This allows the optimal delivery method to be selected by taking into account the geographical location information. Some or all of the above-described processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI. For example, the delivery unit can input the end user's geographical location information into AI, which analyzes the information and selects the optimal delivery method.

[0115] At the time of delivery, the delivery unit can analyze the end user's social media activity and suggest a delivery method. For example, the delivery unit can analyze the end user's social media posts and suggest the optimal delivery method. The delivery unit can also suggest a delivery method that is useful for demand forecasting based on the end user's social media activity. For example, the delivery unit improves the delivery method based on the end user's feedback on social media. In this way, the optimal delivery method can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's social media data into AI, which then analyzes the information and suggests the optimal delivery method.

[0116] The delivery unit can customize the delivery method by reflecting the end user's past feedback at the time of delivery. The delivery unit can improve the delivery method based on the end user's past feedback, for example. The delivery unit can also customize the delivery interface by reflecting the end user's past feedback. For example, the delivery unit can adjust the frequency and timing of delivery by referring to the end user's past feedback. In this way, the delivery method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's past feedback data into AI, which analyzes the information and customizes the delivery method.

[0117] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis to identify the most suitable product. Furthermore, when the user is in a hurry, the analysis unit can quickly perform an analysis to identify the most suitable product. Furthermore, when the user is excited, the analysis unit can prioritize visually stimulating products. For example, when the user is relaxed, the analysis unit performs a detailed analysis to identify the most suitable product. Furthermore, when the user is in a hurry, the analysis unit can quickly perform an analysis to identify the most suitable product. Furthermore, when the user is excited, the analysis unit can prioritize visually stimulating products. Thus, by adjusting the analysis criteria according to the user's emotions, more suitable products can be identified. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the analysis criteria based on the results.

[0118] During analysis, the analysis unit can compare the producer's supply capacity with the end user's demand to improve the accuracy of the analysis. For example, the analysis unit compares the producer's supply capacity with the end user's demand to identify the optimal product. The analysis unit can also consider the producer's supply capacity and identify products that meet the end user's demand. For example, the analysis unit optimizes the producer's supply capacity based on the end user's demand. This improves the accuracy of the analysis by comparing the supply capacity with the demand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the producer's supply capacity data and the end user's demand data into AI, which can analyze the information and identify the optimal product.

[0119] During analysis, the analysis unit can perform analysis based on the end user's attribute information. The analysis unit can identify the most suitable product, for example, by taking into account the end user's age and gender. The analysis unit can also identify the most suitable product based on the end user's purchase history. For example, the analysis unit can identify the most suitable product by taking into account the end user's preferences and areas of interest. This makes it possible to identify a more suitable product by taking into account the end user's attribute information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the end user's attribute information data into AI, which can analyze the information and identify the most suitable product.

[0120] During analysis, the analysis unit can weight the analysis based on the end user's purchase frequency. For example, the analysis unit prioritizes identifying products that are frequently purchased by the end user. The analysis unit can also weight the importance of products based on the end user's purchase frequency. For example, the analysis unit takes into account the end user's purchase frequency to identify optimal products. By weighting the analysis based on purchase frequency, more important products can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input end user purchase frequency data into AI, which can analyze the information to identify optimal products.

[0121] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is excited, the analysis unit can prioritize displaying visually stimulating analysis results. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is excited, the analysis unit can prioritize displaying visually stimulating analysis results. In this way, by adjusting the display order of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the display order of the analysis results based on that result.

[0122] During analysis, the analysis unit can perform analysis based on the geographic distribution of end users. For example, the analysis unit identifies region-specific products based on the geographic distribution of end users. The analysis unit can also propose optimal delivery routes taking the geographic distribution of end users into consideration. For example, the analysis unit performs demand forecasting based on the geographic distribution of end users and identifies optimal products. This makes it possible to identify region-specific products by taking the geographic distribution into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographic distribution data of end users into AI, which then analyzes the information to identify optimal products.

[0123] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user's purchasing history. The analysis unit can also identify optimal products by referring to literature related to the end user's preferences. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the end user's attribute information. In this way, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the end user's relevant literature data into AI, which can analyze the information and identify optimal products.

[0124] During analysis, the analysis unit can perform analysis based on the market value of the end user. For example, the analysis unit identifies the most suitable product based on the market value of the end user. The analysis unit can also weight the importance of the product by taking the market value of the end user into consideration. For example, the analysis unit identifies the most suitable product based on the market value of the end user. In this way, by taking market value into consideration, more important products can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input end user market value data into AI, which analyzes the information to identify the most suitable product.

[0125] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user's emotions. For example, the delivery unit selects a normal delivery method when the user is relaxed. Furthermore, the delivery unit can select a rapid delivery method when the user is in a hurry. Furthermore, the delivery unit can select a special delivery method when the user is excited. For example, the delivery unit selects the normal delivery method when the user is relaxed. Furthermore, the delivery unit can select a rapid delivery method when the user is in a hurry. Furthermore, the delivery unit can select a special delivery method when the user is excited. This enables more appropriate delivery by adjusting the delivery method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery department can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the delivery method based on the results.

[0126] At the time of delivery, the delivery unit can select the optimal delivery method by analyzing the end user's past delivery history. For example, the delivery unit selects the optimal delivery method based on the end user's past delivery history. The delivery unit can also predict the optimal delivery method for a specific period based on the end user's past delivery history. For example, the delivery unit analyzes the end user's past delivery history to optimize the frequency and timing of deliveries. In this way, the optimal delivery method can be selected by analyzing the past delivery history. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's past delivery history data into AI, which then analyzes the information and selects the optimal delivery method.

[0127] The delivery unit can customize the delivery method based on the end user's current living situation at the time of delivery. For example, the delivery unit grasps the end user's current living situation in real time and selects the optimal delivery method. The delivery unit can also adjust the content and method of delivery according to the end user's living situation. For example, the delivery unit optimizes the timing of delivery by taking the end user's living situation into consideration. This enables more appropriate delivery by customizing the delivery method based on the current living situation. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input data on the end user's current living situation into AI, which analyzes the information and selects the optimal delivery method.

[0128] The delivery unit can improve the delivery method by reflecting end user feedback during delivery. The delivery unit can improve the delivery method, for example, based on end user feedback. The delivery unit can also customize the delivery interface by reflecting end user feedback. For example, the delivery unit can adjust the frequency and timing of delivery based on end user feedback. In this way, the delivery method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the delivery unit can be performed using, for example, AI, or can be performed without using AI. For example, the delivery unit can input end user feedback data into AI, which can analyze the information and improve the delivery method.

[0129] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. For example, if the user is relaxed, the delivery unit can set a normal delivery priority. Furthermore, if the user is in a hurry, the delivery unit can prioritize quick delivery. Furthermore, if the user is excited, the delivery unit can also prioritize special delivery. For example, if the user is relaxed, the delivery unit can set a normal delivery priority. Furthermore, if the user is in a hurry, the delivery unit can also prioritize quick delivery. Furthermore, if the user is excited, the delivery unit can also prioritize special delivery. This enables more appropriate delivery by determining delivery priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery department can input the user's emotional data into the generation AI, which then infers the emotion and determines delivery priorities based on the results.

[0130] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the end user's geographical location information. The delivery unit, for example, selects the optimal delivery method based on the end user's geographical location information. The delivery unit can also propose the optimal delivery route, taking into account the end user's geographical location information. For example, the delivery unit optimizes the timing of delivery based on the end user's geographical location information. This allows the optimal delivery method to be selected by taking into account the geographical location information. Some or all of the above-described processing in the delivery unit may be performed, for example, using AI, or may be performed without using AI. For example, the delivery unit can input the end user's geographical location information into AI, which analyzes the information and selects the optimal delivery method.

[0131] At the time of delivery, the delivery unit can analyze the end user's social media activity and suggest a delivery method. For example, the delivery unit can analyze the end user's social media posts and suggest the optimal delivery method. The delivery unit can also suggest a delivery method that is useful for demand forecasting based on the end user's social media activity. For example, the delivery unit improves the delivery method based on the end user's feedback on social media. In this way, the optimal delivery method can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's social media data into AI, which then analyzes the information and suggests the optimal delivery method.

[0132] The delivery unit can customize the delivery method by reflecting the end user's past feedback at the time of delivery. The delivery unit can improve the delivery method based on the end user's past feedback, for example. The delivery unit can also customize the delivery interface by reflecting the end user's past feedback. For example, the delivery unit can adjust the frequency and timing of delivery by referring to the end user's past feedback. In this way, the delivery method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the end user's past feedback data into AI, which analyzes the information and customizes the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and delivery unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can monitor inventory status in real time using sensors or IoT devices of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to compare the producer's supply capacity with the end user's demand. The delivery unit is realized, 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 delivers products to the end user's address in cooperation with a logistics company. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and delivery unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit 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, the collection unit can monitor inventory status in real time using sensors or IoT devices of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to compare the producer's supply capacity with the end user's demand. The delivery 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 delivers products to the end user's address in cooperation with a logistics company. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and delivery unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can monitor inventory status in real time using sensors or IoT devices of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to compare the supply capacity of producers with the demand of end users. The delivery unit is realized, 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 delivers products to end users' addresses in cooperation with logistics companies. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and delivery unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can monitor inventory status in real time using sensors or IoT devices of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to compare the producer's supply capacity with the end user's demand. The delivery unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and delivers products to the end user's address in cooperation with a logistics company.

[0133] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0134] The analysis unit can predict future purchasing patterns based on the end user's purchasing history. For example, the analysis unit analyzes data on products the end user has purchased in the past and identifies products that the end user is likely to purchase next. The analysis unit can also predict products that will be in high demand at specific times, taking into account the end user's purchasing frequency and seasonal purchasing trends. Furthermore, the analysis unit can suggest related products based on the end user's purchasing history. This makes it possible to predict future purchasing patterns and suggest more appropriate products by utilizing the end user's purchasing history.

[0135] The collection unit can analyze the end user's social media activities to understand the end user's areas of interest and trends. For example, the collection unit can analyze the posts and comments shared by the end user on social media to identify the end user's current areas of interest. The collection unit can also analyze the accounts the end user follows and groups the end user joins to understand the topics in which the end user is interested. Furthermore, the collection unit can evaluate the strength of the end user's interests based on the frequency of the end user's social media activity and engagement. This makes it possible to understand the end user's areas of interest and trends by utilizing the end user's social media activities, and to make more appropriate product suggestions.

[0136] The delivery unit can propose the optimal delivery route based on the end user's geographical location information. For example, the delivery unit calculates the shortest delivery route based on the end user's address information. The delivery unit can also propose the optimal delivery time by taking into account traffic conditions and weather information. Furthermore, the delivery unit can also optimize the route to make deliveries to multiple end users more efficient. This makes it possible to utilize the end user's geographical location information to achieve efficient deliveries.

[0137] The analysis unit can estimate the end user's emotions and adjust the content of product suggestions based on the estimated emotions. For example, if the end user is relaxed, the analysis unit can suggest products that have a relaxing effect. Also, if the end user is feeling stressed, the analysis unit can suggest products that are useful for relieving stress. Furthermore, if the end user is excited, the analysis unit can suggest exciting products. This makes it possible to suggest products according to the end user's emotions, and to provide a service with higher satisfaction.

[0138] The collection unit can customize the information collection method based on the end user's past feedback. For example, the collection unit can analyze feedback provided by the end user in the past and improve the content of the information collection questions. The collection unit can also reflect the end user's feedback and make the information collection interface easier to use. Furthermore, the collection unit can adjust the frequency and timing of information collection based on the end user's feedback. In this way, the end user's feedback can be utilized to customize the information collection method and enable more efficient information collection.

[0139] The delivery unit can estimate the end user's emotions and adjust the timing of delivery based on the estimated emotions. For example, if the end user is relaxed, the delivery unit can select a normal delivery timing. If the end user is in a hurry, the delivery unit can prioritize quick delivery. Furthermore, if the end user is excited, the delivery unit can set a special delivery timing. This makes it possible to adjust the delivery timing according to the end user's emotions, resulting in the provision of a service with higher satisfaction.

[0140] The analysis unit can make bundle suggestions for related products based on the end user's purchase history. For example, the analysis unit can suggest products that are highly related to products purchased in the past by the end user. The analysis unit can also analyze the end user's purchase history and suggest product bundles based on specific themes. Furthermore, the analysis unit can also make seasonal bundle suggestions by taking into account the end user's purchasing patterns. This makes it possible to make bundle suggestions for related products by utilizing the end user's purchase history, resulting in more attractive product suggestions.

[0141] The collection unit can estimate the end user's emotions and adjust the method of collecting information based on the estimated emotions. For example, if the end user is relaxed, the collection unit can collect detailed information. If the end user is feeling stressed, the collection unit can quickly collect information using simplified questions. Furthermore, if the end user is excited, the collection unit can also use interesting questions to collect information. In this way, the method of collecting information can be adjusted according to the end user's emotions, and more appropriate information can be collected.

[0142] The delivery department can select the optimal delivery method based on the end user's past delivery history. For example, the delivery department can analyze the delivery methods used by the end user in the past and select the method that provides the highest level of satisfaction. The delivery department can also predict the optimal delivery method for a specific period based on the end user's past delivery history. Furthermore, the delivery department can improve the delivery method by reflecting past feedback from the end user. In this way, the optimal delivery method can be selected by utilizing the end user's past delivery history, allowing for the provision of a service with a higher level of satisfaction.

[0143] The analysis unit can estimate the end user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the end user is relaxed, the analysis unit can display detailed analysis results. If the end user is in a hurry, the analysis unit can prioritize displaying important analysis results. Furthermore, if the end user is excited, the analysis unit can display visually stimulating analysis results. This allows the display method of the analysis results to be adjusted according to the end user's emotions, making it possible to provide more appropriate information.

[0144] The processing flow of the second embodiment will be briefly explained below.

[0145] Step 1: The collection unit collects information from producers. This information includes product types, inventory status, production capacity, etc. The collection unit obtains information from databases provided by producers and can also monitor inventory status in real time using sensors and IoT devices. It can also analyze past production data to evaluate production capacity. These processes can sometimes be performed using AI. Step 2: The collection unit collects end-user information. End-user information includes purchase history, preferences, and demand forecasts. The collection unit obtains information from a database provided by the end user and can also monitor demand in real time using sensors and IoT devices. It can also analyze past purchase data to make demand forecasts. These processes can sometimes be performed using AI. Step 3: The analysis unit identifies the best product for each user based on the information collected by the collection unit. The analysis unit uses AI to compare the producer's supply capacity with the end user's demand and identify the best product. It can also build a demand forecasting model to predict future demand. It can also analyze the end user's preferences and identify the best product. Step 4: The delivery unit delivers the products identified by the analysis unit from the producer to the end user. The delivery unit works in cooperation with logistics companies to deliver the products and uses AI to calculate the optimal delivery route, ensuring efficient delivery. It can also monitor delivery status in real time and notify the end user.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0150] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0151] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0166] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0182] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0216] 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.

[0217] [Explanation of symbols]

[0218] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects information from producers; a collection unit that collects end user information; an analysis unit that identifies a product appropriate for each user based on the information collected by the collection unit; a delivery unit that delivers the product identified by the analysis unit from the manufacturer to the end user. A system characterized by:

2. The collecting unit Collect information on producers such as product type, inventory status, production capacity, etc. The system of claim 1 .

3. The collecting unit Collect end user information such as purchase history, preferences, address, etc. The system of claim 1 .

4. The analysis unit Matching producer supply capacity with end-user demand to identify the right products for each user The system of claim 1 .

5. The delivery unit includes: Delivering identified products directly from producers to end users The system of claim 1 .

6. The collecting unit Estimate the sentiment of producers and adjust the timing of information collection based on the estimated sentiment of producers. The system of claim 1 .

7. The collecting unit Analyze the producer's past production history and select the appropriate information collection method The system of claim 1 .

8. The collecting unit When collecting information, filter it based on the producer's current production status and seasonal factors. The system of claim 1 .

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