system

Abstract

The system according to the embodiment aims to efficiently generate routes and match vegetables between farmers and direct sales outlets. [Solution] The system according to the embodiment includes a route generation unit, a collection unit, and a matching unit. The route generation unit generates routes from farms to each direct sales outlet. The collection unit collects information on the status of vegetables at the direct sales outlets based on the route information generated by the route generation unit. The matching unit analyzes the status of vegetables collected by the collection unit and matches suitable vegetables with direct sales outlets.

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 technology does not adequately provide efficient route generation and vegetable matching between farmers and direct sales outlets, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently generate routes and match vegetables between farmers and direct sales outlets. [Means for solving the problem]

[0006] The system according to the embodiment includes a route generation unit, a collection unit, and a matching unit. The route generation unit generates routes from farms to each direct sales outlet. The collection unit collects information on the status of vegetables at the direct sales outlets based on the route information generated by the route generation unit. The matching unit analyzes the status of vegetables collected by the collection unit and matches suitable vegetables with direct sales outlets. [Effects of the Invention]

[0007] The system according to the embodiment enables efficient route generation and vegetable matching between farmers and direct sales outlets. [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 system according to an embodiment of the present invention generates routes connecting farms with multiple farm shops and matches suitable vegetables with farm shops based on the vegetable availability at the farm shops. In this system, a farm shop is set as the starting point, and multiple farm shops are set as destinations. A generation AI generates an optimal route from the farm shop to each farm shop, calculating the route while taking into account traffic conditions, distance, time, and other factors. Furthermore, the generation AI collects and analyzes the vegetable availability at each farm shop. For example, it collects and analyzes information such as inventory status and demand at each farm shop. This allows the generation AI to identify vegetables suitable for each farm shop and recommend the optimal farm shop to the farmer. For example, if a farm shop has low tomatoes in stock, the generation AI suggests delivering tomatoes to that farm shop. Furthermore, generating routes that take traffic conditions into account is expected to shorten delivery times and reduce costs. This allows the system to enable farmers to efficiently deliver vegetables and farm shops to secure vegetables according to demand.

[0029] A delivery support system according to an embodiment includes a route generation unit, a collection unit, and a matching unit. The route generation unit generates a route from a farmer to each farmer's store. The route generation unit generates the route taking into consideration, for example, traffic conditions, distance, and time. The generation AI calculates the optimal route based on traffic data and map information. The collection unit collects information about the vegetable status at the farmer's store based on the route information generated by the route generation unit. The collection unit collects, for example, inventory status and demand information at each farmer's store. The collection unit can collect information in real time using sensors and databases. The matching unit analyzes the vegetable status collected by the collection unit and matches suitable vegetables with farmer's stores. The matching unit identifies, for example, vegetables suitable for each farmer's store based on the collected data. The matching unit uses the generation AI to analyze data and propose optimal matching. This enables the delivery support system according to an embodiment to efficiently connect farmers and farmer's stores and deliver appropriate vegetables.

[0030] The route generation unit can generate a route based on traffic conditions, distance, and time. The route generation unit, for example, collects real-time traffic data and generates a route taking traffic conditions into consideration. For example, the generation AI can propose a route that avoids traffic congestion. The route generation unit can also generate a route based on distance. For example, the generation AI can propose a route with the shortest distance. Furthermore, the route generation unit can also generate a route based on time. For example, the generation AI can propose a route with the shortest required time. This makes it possible to generate an optimal route by taking traffic conditions, distance, and time into consideration. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input traffic data into the generation AI and have the generation AI calculate the optimal route.

[0031] The collection unit can collect inventory status and demand information for each direct sales outlet. For example, the collection unit measures inventory levels at each direct sales outlet using sensors and stores the data in a database. For example, the collection unit monitors inventory levels in real time and updates the data as needed. The collection unit can also collect demand information for the direct sales outlets. For example, the collection unit can analyze sales data and grasp fluctuations in demand. Furthermore, the collection unit can analyze customer purchasing trends and collect demand information. For example, the collection unit can perform demand forecasts based on customer purchase histories. This enables appropriate matching by collecting inventory status and demand information for the direct sales outlets. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input inventory data into AI and have the AI ​​perform demand forecasts.

[0032] The matching unit can identify vegetables suitable for each farmer's store based on the collected vegetable status. The matching unit, for example, analyzes collected inventory data and identifies vegetables suitable for each farmer's store. For example, the matching unit can suggest necessary vegetables for farmer's stores with low inventory. The matching unit can also identify vegetables suitable for each farmer's store based on demand information. For example, the matching unit can prioritize suggesting vegetables with high demand. Furthermore, the matching unit can also identify vegetables suitable for each farmer's store by taking quality information into consideration. For example, the matching unit can prioritize suggesting vegetables with high quality. This makes it possible to identify appropriate vegetables based on the collected vegetable status. Some or all of the above-described processing in the matching unit may be performed using, or without, a generation AI. For example, the matching unit can input inventory data into the generation AI and have the generation AI identify appropriate vegetables.

[0033] The matching unit can suggest a suitable direct sales outlet to the farmer. The matching unit can suggest the optimal direct sales outlet to the farmer, for example, based on the collected data. For example, the matching unit can prioritize suggesting direct sales outlets with low inventory. The matching unit can also prioritize suggesting direct sales outlets with high demand. Furthermore, the matching unit can suggest the optimal direct sales outlet to the farmer, taking geographical conditions into consideration. For example, the matching unit can prioritize suggesting direct sales outlets close to the farmer. This enables efficient delivery by suggesting the optimal direct sales outlet to the farmer. Some or all of the above-mentioned processing in the matching unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the matching unit can input the collected data into the generation AI and have the generation AI suggest the optimal direct sales outlet.

[0034] The route generation unit can provide the generated route information to the matching unit. The route generation unit, for example, transmits the generated route information to the matching unit. For example, the route generation unit stores the route information in a database so that the matching unit can access it. The route generation unit can also provide the route information to the matching unit in real time. For example, the route generation unit immediately transmits the generated route information to the matching unit. Furthermore, the route generation unit can visually display the route information. For example, the route generation unit displays the route on a map so that the matching unit can confirm it. This enables appropriate matching by providing the route information to the matching unit. Some or all of the above-mentioned processing in the route generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the route generation unit can input the generated route information to a generation AI and provide it to the matching unit.

[0035] When generating a route, the route generation unit can propose an appropriate route by referring to the farmer's past delivery history. For example, the route generation unit allows the generation AI to propose an optimal route based on routes the farmer has used in the past. The route generation unit can also allow the generation AI to propose a route that avoids congestion based on the farmer's past delivery history. Furthermore, the route generation unit can analyze the farmer's past delivery history and allow the generation AI to propose the most efficient route. In this way, the optimal route can be proposed by referring to the past delivery history. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input past delivery history data into the generation AI and have the generation AI propose an optimal route.

[0036] The route generation unit can adjust the route based on weather information when generating the route. For example, when it is raining, the route generation unit causes the generation AI to prioritize and suggest routes with roofs or underground passages. Furthermore, when it is sunny, the route generation unit can also cause the generation AI to suggest routes with good scenery. Furthermore, when it is snowy, the route generation unit can also suggest routes that are less slippery. This allows the optimal route to be selected by taking weather information into consideration. Weather information is collected based on, for example, meteorological data and weather forecasts. Some or all of the above-mentioned processing in the route generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the route generation unit can input weather data into the generation AI and have the generation AI suggest the optimal route.

[0037] When generating a route, the route generation unit can select an appropriate route based on fuel efficiency information of the farmer's vehicle. In the route generation unit, for example, the generation AI proposes a route with good fuel efficiency. The route generation unit can also select an optimal route to avoid routes with poor fuel efficiency. Furthermore, the route generation unit can also propose the most efficient route based on the vehicle's fuel efficiency information. This makes it possible to select an efficient route by taking fuel efficiency information into consideration. Fuel efficiency information is collected, for example, based on the vehicle's fuel efficiency data and mileage. Some or all of the above-mentioned processing in the route generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the route generation unit can input fuel efficiency data into the generation AI and have the generation AI propose an optimal route.

[0038] When generating a route, the route generation unit can propose an appropriate route based on the geographical location information of the farmer. The route generation unit allows the generation AI to propose an optimal route based on, for example, the farmer's current location. The route generation unit can also allow the generation AI to propose the most efficient route by taking into account the farmer's geographical location information. Furthermore, the route generation unit can also allow the generation AI to propose a route that avoids traffic congestion based on the farmer's geographical location information. In this way, the optimal route can be proposed by taking geographical location information into consideration. The geographical location information is collected based on, for example, GPS data or map information. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input geographical location information into the generation AI and have the generation AI propose an optimal route.

[0039] When generating a route, the route generation unit can analyze the farmer's social media activity and suggest a related route. For example, the route generation unit allows the generation AI to suggest an optimal route based on the locations where the farmer has checked in on social media. The route generation unit can also analyze the content of the farmer's social media posts and allow the generation AI to suggest a related route. Furthermore, the route generation unit can also suggest a related route based on the activity of the farmer's friends on social media. In this way, related routes can be suggested by analyzing social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of followers, and the engagement rate. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input social media data into the generation AI and have the generation AI suggest a related route.

[0040] The route generation unit can customize an appropriate route generation method by reflecting the farmer's past feedback when generating a route. For example, the route generation unit allows the generation AI to propose an optimal route generation method based on the farmer's past feedback. The route generation unit can also customize the route generation method by reflecting the farmer's past feedback. Furthermore, the route generation unit can analyze the farmer's past feedback and allow the generation AI to propose the most efficient route generation method. In this way, the optimal route generation method can be proposed by reflecting the past feedback. Feedback is collected, for example, based on questionnaire surveys or reviews. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input feedback data into the generation AI and have the generation AI propose an optimal route generation method.

[0041] The collection unit can optimize an appropriate collection method by referring to the farmer's store's past inventory data during collection. The collection unit, for example, optimizes the collection method based on the farmer's store's past inventory data. The collection unit can also analyze the farmer's store's past inventory data and propose the most efficient collection method. Furthermore, the collection unit can adjust the collection frequency and timing by referring to the farmer's store's past inventory data. In this way, the optimal collection method can be proposed by referring to the past inventory data. Collection of past inventory data is performed based on, for example, inventory volume and sales history. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input past inventory data into the generation AI and have the generation AI propose the optimal collection method.

[0042] The collection unit can adjust the collection timing based on the business hours of the farmer's store when collecting. The collection unit adjusts the collection timing, for example, to match the business hours of the farmer's store. The collection unit can also adjust the collection timing so that collection is not performed outside of the business hours of the farmer's store. Furthermore, the collection unit can propose the most efficient collection timing taking into account the business hours of the farmer's store. This makes it possible to propose the optimal collection timing by taking into account the business hours. Collection of business hours is performed based on, for example, the opening time and closing time of business. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input business hours data into the generation AI and have the generation AI adjust the collection timing.

[0043] During collection, the collection unit can prioritize collection of highly relevant data based on the geographical location information of the farmer's store. For example, the collection unit prioritizes collection of highly relevant data based on the geographical location information of the farmer's store. The collection unit can also propose the most efficient data collection method by taking into account the geographical location information of the farmer's store. Furthermore, the collection unit can determine the priority of data to be collected by referring to the geographical location information of the farmer's store. In this way, highly relevant data can be preferentially collected by taking into account the geographical location information. The collection of geographical location information is performed based on, for example, GPS data or map information. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input geographical location information to the generation AI and have the generation AI collect highly relevant data.

[0044] During collection, the collection unit can analyze the social media activity of the farmer's market and collect related data. For example, the collection unit collects data regarding locations where the farmer's market has checked in on social media. The collection unit can also analyze the content of the farmer's market's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the farmer's market's friends on social media. In this way, related data can be collected by analyzing social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of followers, and the engagement rate. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input social media data into the generation AI and have the generation AI collect related data.

[0045] When matching, the matching unit can perform appropriate matching by referring to the farmer's store's past sales data. In the matching unit, for example, the generation AI proposes optimal matching based on the farmer's store's past sales data. The matching unit can also analyze the farmer's store's past sales data and have the generation AI propose the most efficient matching. Furthermore, the matching unit can also have the generation AI customize the matching method by referring to the farmer's store's past sales data. This makes it possible to propose optimal matching by referring to past sales data. Sales data is collected based on, for example, sales volume, sales period, and customer purchase history. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input sales data into the generation AI and have the generation AI propose optimal matching.

[0046] The matching unit can adjust the matching based on the demand forecast data of the farmer's store during matching. For example, the matching unit allows the generation AI to propose optimal matching based on the demand forecast data of the farmer's store. The matching unit can also analyze the demand forecast data of the farmer's store and allow the generation AI to propose the most efficient matching. Furthermore, the matching unit can allow the generation AI to customize the matching method by referring to the demand forecast data of the farmer's store. This makes it possible to propose optimal matching by taking the demand forecast data into consideration. Demand forecast data is collected based on, for example, past sales data, seasonal fluctuations, and trend analysis. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input demand forecast data into the generation AI and have the generation AI propose optimal matching.

[0047] During matching, the matching unit can select appropriate matching based on the farmer's production volume information. In the matching unit, for example, the generation AI proposes optimal matching based on the farmer's production volume information. The matching unit can also analyze the farmer's production volume information and propose the most efficient matching by the generation AI. Furthermore, the matching unit can also customize the matching method by referring to the farmer's production volume information. In this way, optimal matching can be proposed by taking production volume information into consideration. Production volume information is collected based on, for example, harvest volume, cultivation area, and cropping plan. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input production volume information into the generation AI and have the generation AI propose the optimal matching.

[0048] During matching, the matching unit can propose suitable matches based on the geographical location information of the farmer's store. For example, the matching unit allows the generation AI to propose optimal matches based on the geographical location information of the farmer's store. The matching unit can also allow the generation AI to propose the most efficient matches by taking into account the geographical location information of the farmer's store. Furthermore, the matching unit can allow the generation AI to customize the matching method by referring to the geographical location information of the farmer's store. This makes it possible to propose optimal matches by taking into account the geographical location information. Geographical location information is collected based on, for example, GPS data or map information. Some or all of the above-described processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input geographical location information into the generation AI and have the generation AI propose optimal matches.

[0049] When matching, the matching unit can analyze the social media activity of the farmer's store and suggest relevant matches. For example, the matching unit allows the generation AI to suggest optimal matches based on information about locations where the farmer's store has checked in on social media. The matching unit can also analyze the content of the farmer's social media posts and allow the generation AI to suggest relevant matches. Furthermore, the matching unit can also allow the generation AI to suggest relevant matches based on the activity of the farmer's friends on social media. In this way, relevant matches can be suggested by analyzing social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of followers, and the engagement rate. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input social media data into the generation AI and have the generation AI suggest relevant matches.

[0050] The matching unit can customize an appropriate matching method by reflecting the farmer's past feedback during matching. For example, the matching unit allows the generation AI to propose an optimal matching method based on the farmer's past feedback. The matching unit can also customize the matching method by reflecting the farmer's past feedback. Furthermore, the matching unit can analyze the farmer's past feedback and allow the generation AI to propose the most efficient matching method. This allows the optimal matching method to be proposed by reflecting the past feedback. Feedback is collected, for example, based on questionnaire surveys or reviews. Some or all of the above-mentioned processing in the matching unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the matching unit can input feedback data into the generation AI and have the generation AI propose an optimal matching method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit collects energy consumption data from farm shops and can manage inventory with energy efficiency in mind. For example, it can propose energy-efficient refrigeration equipment to farm shops with high energy consumption. It can also propose increasing inventory to farm shops with low energy consumption. Furthermore, it can provide specific action plans to improve the energy efficiency of farm shops based on the energy consumption data. This makes it possible to manage inventory with energy efficiency in mind.

[0053] The route generation unit can adjust the route based on the farmer's vehicle maintenance information. For example, if the vehicle needs maintenance, the generation AI will suggest a route that includes the nearest service station. Also, if the vehicle maintenance has been completed, the generation AI can suggest the shortest route. Furthermore, based on the vehicle's maintenance history, the generation AI can also suggest preventive maintenance. This makes it possible to generate routes that take vehicle maintenance information into account.

[0054] The matching unit can adjust matching with direct sales outlets based on the farmer's production schedule. For example, if the farmer is in the harvest season, the generation AI will prioritize suggesting direct sales outlets with high demand. Also, if the farmer is in the fallow season, the generation AI can suggest direct sales outlets with low inventory. Furthermore, based on the farmer's production schedule, the generation AI can also suggest the optimal delivery timing. This makes it possible to match products taking production schedules into account.

[0055] The collection unit collects energy consumption data from farm shops and can manage inventory with energy efficiency in mind. For example, it can propose energy-efficient refrigeration equipment to farm shops with high energy consumption. It can also propose increasing inventory to farm shops with low energy consumption. Furthermore, it can provide specific action plans to improve the energy efficiency of farm shops based on the energy consumption data. This makes it possible to manage inventory with energy efficiency in mind.

[0056] The route generation unit can adjust the route based on the farmer's vehicle maintenance information. For example, if the vehicle needs maintenance, the generation AI will suggest a route that includes the nearest service station. Also, if the vehicle maintenance has been completed, the generation AI can suggest the shortest route. Furthermore, based on the vehicle's maintenance history, the generation AI can also suggest preventive maintenance. This makes it possible to generate routes that take vehicle maintenance information into account.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The route generation unit generates routes from the farm to each direct sales outlet. The route generation unit generates routes taking into account traffic conditions, distance, time, etc., and uses generation AI to calculate the optimal route based on traffic data and map information. Step 2: The collection unit collects information on the vegetable status at the farmer's market based on the route information generated by the route generation unit. The collection unit collects information on the inventory status and demand at each farmer's market, and can collect information in real time using sensors and a database. Step 3: The matching department analyzes the vegetable information collected by the collection department and matches suitable vegetables with direct sales outlets. Based on the collected data, the matching department identifies vegetables suitable for each direct sales outlet, analyzes the data using generative AI, and proposes optimal matches.

[0059] (Example 2) A system according to an embodiment of the present invention generates routes connecting farms with multiple farm shops and matches suitable vegetables with farm shops based on the vegetable availability at the farm shops. In this system, a farm shop is set as the starting point, and multiple farm shops are set as destinations. A generation AI generates an optimal route from the farm shop to each farm shop, calculating the route while taking into account traffic conditions, distance, time, and other factors. Furthermore, the generation AI collects and analyzes the vegetable availability at each farm shop. For example, it collects and analyzes information such as inventory status and demand at each farm shop. This allows the generation AI to identify vegetables suitable for each farm shop and recommend the optimal farm shop to the farmer. For example, if a farm shop has low tomatoes in stock, the generation AI suggests delivering tomatoes to that farm shop. Furthermore, generating routes that take traffic conditions into account is expected to shorten delivery times and reduce costs. This allows the system to enable farmers to efficiently deliver vegetables and farm shops to secure vegetables according to demand.

[0060] A delivery support system according to an embodiment includes a route generation unit, a collection unit, and a matching unit. The route generation unit generates a route from a farmer to each farmer's store. The route generation unit generates the route taking into consideration, for example, traffic conditions, distance, and time. The generation AI calculates the optimal route based on traffic data and map information. The collection unit collects information about the vegetable status at the farmer's store based on the route information generated by the route generation unit. The collection unit collects, for example, inventory status and demand information at each farmer's store. The collection unit can collect information in real time using sensors and databases. The matching unit analyzes the vegetable status collected by the collection unit and matches suitable vegetables with farmer's stores. The matching unit identifies, for example, vegetables suitable for each farmer's store based on the collected data. The matching unit uses the generation AI to analyze data and propose optimal matching. This enables the delivery support system according to an embodiment to efficiently connect farmers and farmer's stores and deliver appropriate vegetables.

[0061] The route generation unit can generate a route based on traffic conditions, distance, and time. The route generation unit, for example, collects real-time traffic data and generates a route taking traffic conditions into consideration. For example, the generation AI can propose a route that avoids traffic congestion. The route generation unit can also generate a route based on distance. For example, the generation AI can propose a route with the shortest distance. Furthermore, the route generation unit can also generate a route based on time. For example, the generation AI can propose a route with the shortest required time. This makes it possible to generate an optimal route by taking traffic conditions, distance, and time into consideration. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input traffic data into the generation AI and have the generation AI calculate the optimal route.

[0062] The collection unit can collect inventory status and demand information for each direct sales outlet. For example, the collection unit measures inventory levels at each direct sales outlet using sensors and stores the data in a database. For example, the collection unit monitors inventory levels in real time and updates the data as needed. The collection unit can also collect demand information for the direct sales outlets. For example, the collection unit can analyze sales data and grasp fluctuations in demand. Furthermore, the collection unit can analyze customer purchasing trends and collect demand information. For example, the collection unit can perform demand forecasts based on customer purchase histories. This enables appropriate matching by collecting inventory status and demand information for the direct sales outlets. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input inventory data into AI and have the AI ​​perform demand forecasts.

[0063] The matching unit can identify vegetables suitable for each farmer's store based on the collected vegetable status. The matching unit, for example, analyzes collected inventory data and identifies vegetables suitable for each farmer's store. For example, the matching unit can suggest necessary vegetables for farmer's stores with low inventory. The matching unit can also identify vegetables suitable for each farmer's store based on demand information. For example, the matching unit can prioritize suggesting vegetables with high demand. Furthermore, the matching unit can also identify vegetables suitable for each farmer's store by taking quality information into consideration. For example, the matching unit can prioritize suggesting vegetables with high quality. This makes it possible to identify appropriate vegetables based on the collected vegetable status. Some or all of the above-described processing in the matching unit may be performed using, or without, a generation AI. For example, the matching unit can input inventory data into the generation AI and have the generation AI identify appropriate vegetables.

[0064] The matching unit can suggest a suitable direct sales outlet to the farmer. The matching unit can suggest the optimal direct sales outlet to the farmer, for example, based on the collected data. For example, the matching unit can prioritize suggesting direct sales outlets with low inventory. The matching unit can also prioritize suggesting direct sales outlets with high demand. Furthermore, the matching unit can suggest the optimal direct sales outlet to the farmer, taking geographical conditions into consideration. For example, the matching unit can prioritize suggesting direct sales outlets close to the farmer. This enables efficient delivery by suggesting the optimal direct sales outlet to the farmer. Some or all of the above-mentioned processing in the matching unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the matching unit can input the collected data into the generation AI and have the generation AI suggest the optimal direct sales outlet.

[0065] The route generation unit can provide the generated route information to the matching unit. The route generation unit, for example, transmits the generated route information to the matching unit. For example, the route generation unit stores the route information in a database so that the matching unit can access it. The route generation unit can also provide the route information to the matching unit in real time. For example, the route generation unit immediately transmits the generated route information to the matching unit. Furthermore, the route generation unit can visually display the route information. For example, the route generation unit displays the route on a map so that the matching unit can confirm it. This enables appropriate matching by providing the route information to the matching unit. Some or all of the above-mentioned processing in the route generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the route generation unit can input the generated route information to a generation AI and provide it to the matching unit.

[0066] The route generation unit can estimate the farmer's emotions and adjust the priority of route generation based on the estimated farmer's emotions. For example, if the farmer is feeling stressed, the route generation unit can generate the most efficient route that can reach the destination in the shortest time. Furthermore, if the farmer is feeling relaxed, the route generation unit can suggest a scenic route or a route that includes rest stops. Furthermore, if the farmer is in a hurry, the route generation unit can generate the shortest route that avoids traffic congestion. This adjusts the priority of route generation according to the farmer's emotions, thereby reducing the farmer's stress. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the route generation unit can be performed using, for example, the generation AI. For example, the route generation unit can input the farmer's emotion data into the generation AI and have the generation AI adjust the priority of route generation.

[0067] When generating a route, the route generation unit can propose an appropriate route by referring to the farmer's past delivery history. For example, the route generation unit allows the generation AI to propose an optimal route based on routes the farmer has used in the past. The route generation unit can also allow the generation AI to propose a route that avoids congestion based on the farmer's past delivery history. Furthermore, the route generation unit can analyze the farmer's past delivery history and allow the generation AI to propose the most efficient route. In this way, the optimal route can be proposed by referring to the past delivery history. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input past delivery history data into the generation AI and have the generation AI propose an optimal route.

[0068] The route generation unit can adjust the route based on weather information when generating the route. For example, when it is raining, the route generation unit causes the generation AI to prioritize and suggest routes with roofs or underground passages. Furthermore, when it is sunny, the route generation unit can also cause the generation AI to suggest routes with good scenery. Furthermore, when it is snowy, the route generation unit can also suggest routes that are less slippery. This allows the optimal route to be selected by taking weather information into consideration. Weather information is collected based on, for example, meteorological data and weather forecasts. Some or all of the above-mentioned processing in the route generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the route generation unit can input weather data into the generation AI and have the generation AI suggest the optimal route.

[0069] When generating a route, the route generation unit can select an appropriate route based on fuel efficiency information of the farmer's vehicle. In the route generation unit, for example, the generation AI proposes a route with good fuel efficiency. The route generation unit can also select an optimal route to avoid routes with poor fuel efficiency. Furthermore, the route generation unit can also propose the most efficient route based on the vehicle's fuel efficiency information. This makes it possible to select an efficient route by taking fuel efficiency information into consideration. Fuel efficiency information is collected, for example, based on the vehicle's fuel efficiency data and mileage. Some or all of the above-mentioned processing in the route generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the route generation unit can input fuel efficiency data into the generation AI and have the generation AI propose an optimal route.

[0070] The route generation unit can estimate the farmer's emotions and adjust the timing of route generation based on the estimated farmer's emotions. For example, if the farmer is feeling stressed, the route generation unit generates a route for a time with low traffic, such as early morning or late night. Alternatively, if the farmer is relaxed, the route generation unit can generate a route for a time with beautiful scenery during the day. Furthermore, if the farmer is in a hurry, the route generation unit can instantly generate a route to support rapid delivery. This allows the farmer's stress to be reduced by adjusting the timing of route generation according to the farmer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the route generation unit may be performed using, for example, the generation AI. For example, the route generation unit can input the farmer's emotion data into the generation AI and have the generation AI adjust the timing of route generation.

[0071] When generating a route, the route generation unit can propose an appropriate route based on the geographical location information of the farmer. The route generation unit allows the generation AI to propose an optimal route based on, for example, the farmer's current location. The route generation unit can also allow the generation AI to propose the most efficient route by taking into account the farmer's geographical location information. Furthermore, the route generation unit can also allow the generation AI to propose a route that avoids traffic congestion based on the farmer's geographical location information. In this way, the optimal route can be proposed by taking geographical location information into consideration. The geographical location information is collected based on, for example, GPS data or map information. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input geographical location information into the generation AI and have the generation AI propose an optimal route.

[0072] When generating a route, the route generation unit can analyze the farmer's social media activity and suggest a related route. For example, the route generation unit allows the generation AI to suggest an optimal route based on the locations where the farmer has checked in on social media. The route generation unit can also analyze the content of the farmer's social media posts and allow the generation AI to suggest a related route. Furthermore, the route generation unit can also suggest a related route based on the activity of the farmer's friends on social media. In this way, related routes can be suggested by analyzing social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of followers, and the engagement rate. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input social media data into the generation AI and have the generation AI suggest a related route.

[0073] The route generation unit can customize an appropriate route generation method by reflecting the farmer's past feedback when generating a route. For example, the route generation unit allows the generation AI to propose an optimal route generation method based on the farmer's past feedback. The route generation unit can also customize the route generation method by reflecting the farmer's past feedback. Furthermore, the route generation unit can analyze the farmer's past feedback and allow the generation AI to propose the most efficient route generation method. In this way, the optimal route generation method can be proposed by reflecting the past feedback. Feedback is collected, for example, based on questionnaire surveys or reviews. Some or all of the above-mentioned processing in the route generation unit may be performed using, or without, the generation AI. For example, the route generation unit can input feedback data into the generation AI and have the generation AI propose an optimal route generation method.

[0074] The collection unit can estimate the emotions of the farmer's store and adjust the frequency of inventory status collection based on the estimated emotions of the farmer's store. For example, if the farmer's store is feeling stressed, the collection unit can increase the collection frequency to quickly grasp the inventory status. Furthermore, if the farmer's store is relaxed, the collection unit can decrease the collection frequency to periodically grasp the inventory status. Furthermore, if the farmer's store is in a hurry, the collection unit can increase the collection frequency to immediately grasp the inventory status. Thus, by adjusting the collection frequency according to the emotions of the farmer's store, the inventory status can be quickly grasped. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the emotion data of the farmer's store into the generation AI and have the generation AI adjust the collection frequency.

[0075] The collection unit can optimize an appropriate collection method by referring to the farmer's store's past inventory data during collection. The collection unit, for example, optimizes the collection method based on the farmer's store's past inventory data. The collection unit can also analyze the farmer's store's past inventory data and propose the most efficient collection method. Furthermore, the collection unit can adjust the collection frequency and timing by referring to the farmer's store's past inventory data. In this way, the optimal collection method can be proposed by referring to the past inventory data. Collection of past inventory data is performed based on, for example, inventory volume and sales history. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input past inventory data into the generation AI and have the generation AI propose the optimal collection method.

[0076] The collection unit can adjust the collection timing based on the business hours of the farmer's store when collecting. The collection unit adjusts the collection timing, for example, to match the business hours of the farmer's store. The collection unit can also adjust the collection timing so that collection is not performed outside of the business hours of the farmer's store. Furthermore, the collection unit can propose the most efficient collection timing taking into account the business hours of the farmer's store. This makes it possible to propose the optimal collection timing by taking into account the business hours. Collection of business hours is performed based on, for example, the opening time and closing time of business. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input business hours data into the generation AI and have the generation AI adjust the collection timing.

[0077] The collection unit can estimate the emotions of the farmer's store and determine the priority of data to be collected based on the estimated emotions of the farmer's store. For example, if the farmer's store is feeling stressed, the collection unit can prioritize collecting important data. Furthermore, if the farmer's store is relaxed, the collection unit can also collect overall data in a balanced manner. Furthermore, if the farmer's store is in a hurry, the collection unit can prioritize collecting data that is immediately needed. This allows important data to be collected quickly by determining the priority of data according to the emotions of the farmer's store. Emotion estimation is achieved 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 can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input emotion data of the farmer's store into the generation AI and have the generation AI determine the priority of data to be collected.

[0078] During collection, the collection unit can prioritize collection of highly relevant data based on the geographical location information of the farmer's store. For example, the collection unit prioritizes collection of highly relevant data based on the geographical location information of the farmer's store. The collection unit can also propose the most efficient data collection method by taking into account the geographical location information of the farmer's store. Furthermore, the collection unit can determine the priority of data to be collected by referring to the geographical location information of the farmer's store. In this way, highly relevant data can be preferentially collected by taking into account the geographical location information. The collection of geographical location information is performed based on, for example, GPS data or map information. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input geographical location information to the generation AI and have the generation AI collect highly relevant data.

[0079] During collection, the collection unit can analyze the social media activity of the farmer's market and collect related data. For example, the collection unit collects data regarding locations where the farmer's market has checked in on social media. The collection unit can also analyze the content of the farmer's market's social media posts and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the farmer's market's friends on social media. In this way, related data can be collected by analyzing social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of followers, and the engagement rate. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input social media data into the generation AI and have the generation AI collect related data.

[0080] The matching unit can estimate the farmer's emotions and adjust the matching priority based on the estimated farmer's emotions. For example, if the farmer is stressed, the matching unit can adjust the priority so that matching is completed most efficiently and in the shortest time. Furthermore, if the farmer is relaxed, the matching unit can also prioritize matching according to the farmer's preferences. Furthermore, if the farmer is in a hurry, the matching unit can also adjust the priority so that matching is completed immediately. This adjusts the matching priority according to the farmer's emotions, thereby reducing the farmer's stress. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the matching unit can be performed using, for example, the generation AI, or without the generation AI. For example, the matching unit can input the farmer's emotion data into the generation AI and have the generation AI adjust the matching priority.

[0081] When matching, the matching unit can perform appropriate matching by referring to the farmer's store's past sales data. In the matching unit, for example, the generation AI proposes optimal matching based on the farmer's store's past sales data. The matching unit can also analyze the farmer's store's past sales data and have the generation AI propose the most efficient matching. Furthermore, the matching unit can also have the generation AI customize the matching method by referring to the farmer's store's past sales data. This makes it possible to propose optimal matching by referring to past sales data. Sales data is collected based on, for example, sales volume, sales period, and customer purchase history. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input sales data into the generation AI and have the generation AI propose optimal matching.

[0082] The matching unit can adjust the matching based on the demand forecast data of the farmer's store during matching. For example, the matching unit allows the generation AI to propose optimal matching based on the demand forecast data of the farmer's store. The matching unit can also analyze the demand forecast data of the farmer's store and allow the generation AI to propose the most efficient matching. Furthermore, the matching unit can allow the generation AI to customize the matching method by referring to the demand forecast data of the farmer's store. This makes it possible to propose optimal matching by taking the demand forecast data into consideration. Demand forecast data is collected based on, for example, past sales data, seasonal fluctuations, and trend analysis. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input demand forecast data into the generation AI and have the generation AI propose optimal matching.

[0083] During matching, the matching unit can select appropriate matching based on the farmer's production volume information. In the matching unit, for example, the generation AI proposes optimal matching based on the farmer's production volume information. The matching unit can also analyze the farmer's production volume information and propose the most efficient matching by the generation AI. Furthermore, the matching unit can also customize the matching method by referring to the farmer's production volume information. In this way, optimal matching can be proposed by taking production volume information into consideration. Production volume information is collected based on, for example, harvest volume, cultivation area, and cropping plan. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input production volume information into the generation AI and have the generation AI propose the optimal matching.

[0084] The matching unit can estimate the farmer's emotions and adjust the timing of matching based on the estimated farmer's emotions. For example, if the farmer is feeling stressed, the matching unit can have the generation AI perform matching during times when the farmer is relaxed, such as early morning or late night. Alternatively, if the farmer is relaxed, the matching unit can have the generation AI perform matching during the daytime. Furthermore, if the farmer is in a hurry, the matching unit can perform matching immediately to support rapid delivery. This allows the farmer's stress to be reduced by adjusting the timing of matching according to the farmer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 matching unit can be performed using, for example, the generation AI. For example, the matching unit can input the farmer's emotion data into the generation AI and have the generation AI adjust the timing of matching.

[0085] During matching, the matching unit can propose suitable matches based on the geographical location information of the farmer's store. For example, the matching unit allows the generation AI to propose optimal matches based on the geographical location information of the farmer's store. The matching unit can also allow the generation AI to propose the most efficient matches by taking into account the geographical location information of the farmer's store. Furthermore, the matching unit can allow the generation AI to customize the matching method by referring to the geographical location information of the farmer's store. This makes it possible to propose optimal matches by taking into account the geographical location information. Geographical location information is collected based on, for example, GPS data or map information. Some or all of the above-described processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input geographical location information into the generation AI and have the generation AI propose optimal matches.

[0086] When matching, the matching unit can analyze the social media activity of the farmer's store and suggest relevant matches. For example, the matching unit allows the generation AI to suggest optimal matches based on information about locations where the farmer's store has checked in on social media. The matching unit can also analyze the content of the farmer's social media posts and allow the generation AI to suggest relevant matches. Furthermore, the matching unit can also allow the generation AI to suggest relevant matches based on the activity of the farmer's friends on social media. In this way, relevant matches can be suggested by analyzing social media activity. The analysis of social media activity is performed based on, for example, the content of posts, the number of followers, and the engagement rate. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, the generation AI. For example, the matching unit can input social media data into the generation AI and have the generation AI suggest relevant matches.

[0087] The matching unit can customize an appropriate matching method by reflecting the farmer's past feedback during matching. For example, the matching unit allows the generation AI to propose an optimal matching method based on the farmer's past feedback. The matching unit can also customize the matching method by reflecting the farmer's past feedback. Furthermore, the matching unit can analyze the farmer's past feedback and allow the generation AI to propose the most efficient matching method. This allows the optimal matching method to be proposed by reflecting the past feedback. Feedback is collected, for example, based on questionnaire surveys or reviews. Some or all of the above-mentioned processing in the matching unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the matching unit can input feedback data into the generation AI and have the generation AI propose an optimal matching method. === Hard Collateral 1-1 === Each of the multiple elements including the route generation unit, collection unit, and matching 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 route generation unit is realized by the control unit 46A of the smart device 14 and calculates the optimal route based on traffic data and map information. The collection unit uses sensors and databases of the smart device 14 to collect inventory status and demand information at farmer's stores in real time. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify vegetables suitable for each farmer's store. === Hard Collateral 1-2 === Each of the multiple elements, including the route generation unit, collection unit, and matching 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 route generation unit is realized by the control unit 46A of the smart glasses 214 and calculates the optimal route based on traffic data and map information. The collection unit uses sensors and databases in the smart glasses 214 to collect inventory status and demand information at farmer's stores in real time. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify vegetables suitable for each farmer's store. === Hard Collateral 1-3 === Each of the multiple elements including the route generation unit, collection unit, and matching 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 route generation unit is realized by the control unit 46A of the headset terminal 314 and calculates the optimal route based on traffic data and map information. The collection unit uses the sensors and database of the headset terminal 314 to collect stock status and demand information at farm shops in real time. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify vegetables suitable for each farm shop. === Hard Collateral 1-4 === Each of the multiple elements including the route generation unit, collection unit, and matching unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the route generation unit is realized by the control unit 46A of the robot 414, and calculates the optimal route based on traffic data and map information. The collection unit uses the sensors and database of the robot 414 to collect stock status and demand information at farmer's stores in real time. The matching unit is realized by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to identify vegetables suitable for each farmer's store.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The route generation unit can monitor the farmer's health and adjust the route based on their health condition. For example, if the farmer feels tired, the generation AI will suggest a route that includes rest points. Also, if the farmer is in good health, the generation AI can suggest the shortest route. Furthermore, if the farmer has a specific health problem, the generation AI can suggest a route that passes near a medical facility. This makes it possible to generate routes that are tailored to the farmer's health condition.

[0090] The collection unit collects energy consumption data from farm shops and can manage inventory with energy efficiency in mind. For example, it can propose energy-efficient refrigeration equipment to farm shops with high energy consumption. It can also propose increasing inventory to farm shops with low energy consumption. Furthermore, it can provide specific action plans to improve the energy efficiency of farm shops based on the energy consumption data. This makes it possible to manage inventory with energy efficiency in mind.

[0091] The matching unit can estimate the farmer's emotions and adjust the way it communicates with the farmer's store based on the estimated emotions. For example, if the farmer is feeling stressed, the generation AI can provide concise and clear instructions. If the farmer is relaxed, the generation AI can also provide communication with detailed explanations. Furthermore, if the farmer is in a hurry, the generation AI can prioritize a quick response. This makes it possible to communicate according to the farmer's emotions.

[0092] The route generation unit can adjust the route based on the farmer's vehicle maintenance information. For example, if the vehicle needs maintenance, the generation AI will suggest a route that includes the nearest service station. Also, if the vehicle maintenance has been completed, the generation AI can suggest the shortest route. Furthermore, based on the vehicle's maintenance history, the generation AI can also suggest preventive maintenance. This makes it possible to generate routes that take vehicle maintenance information into account.

[0093] The collection unit can estimate the emotions of the farmer's shop and adjust the inventory management approach based on the estimated emotions. For example, if the farmer's shop is feeling stressed, the generation AI can make suggestions to simplify inventory management. Alternatively, if the farmer's shop is relaxed, the generation AI can suggest detailed inventory management methods. Furthermore, if the farmer's shop is in a hurry, the generation AI can suggest quick inventory management methods. This makes it possible to manage inventory according to the emotions of the farmer's shop.

[0094] The matching unit can adjust matching with direct sales outlets based on the farmer's production schedule. For example, if the farmer is in the harvest season, the generation AI will prioritize suggesting direct sales outlets with high demand. Also, if the farmer is in the fallow season, the generation AI can suggest direct sales outlets with low inventory. Furthermore, based on the farmer's production schedule, the generation AI can also suggest the optimal delivery timing. This makes it possible to match products taking production schedules into account.

[0095] The route generation unit can estimate the farmer's emotions and adjust the difficulty of the route based on the estimated emotions. For example, if the farmer is feeling stressed, the generation AI can suggest an easy and straight route. If the farmer is feeling relaxed, the generation AI can also suggest a scenic route. Furthermore, if the farmer is in a hurry, the generation AI can also suggest the shortest route. This makes it possible to adjust the difficulty of the route according to the farmer's emotions.

[0096] The collection unit collects energy consumption data from farm shops and can manage inventory with energy efficiency in mind. For example, it can propose energy-efficient refrigeration equipment to farm shops with high energy consumption. It can also propose increasing inventory to farm shops with low energy consumption. Furthermore, it can provide specific action plans to improve the energy efficiency of farm shops based on the energy consumption data. This makes it possible to manage inventory with energy efficiency in mind.

[0097] The matching unit can estimate the farmer's emotions and adjust the way it communicates with the farmer's store based on the estimated emotions. For example, if the farmer is feeling stressed, the generation AI can provide concise and clear instructions. If the farmer is relaxed, the generation AI can also provide communication with detailed explanations. Furthermore, if the farmer is in a hurry, the generation AI can prioritize a quick response. This makes it possible to communicate according to the farmer's emotions.

[0098] The route generation unit can adjust the route based on the farmer's vehicle maintenance information. For example, if the vehicle needs maintenance, the generation AI will suggest a route that includes the nearest service station. Also, if the vehicle maintenance has been completed, the generation AI can suggest the shortest route. Furthermore, based on the vehicle's maintenance history, the generation AI can also suggest preventive maintenance. This makes it possible to generate routes that take vehicle maintenance information into account.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The route generation unit generates routes from the farm to each direct sales outlet. The route generation unit generates routes taking into account traffic conditions, distance, time, etc., and uses generation AI to calculate the optimal route based on traffic data and map information. Step 2: The collection unit collects information on the vegetable status at the farmer's market based on the route information generated by the route generation unit. The collection unit collects information on the inventory status and demand at each farmer's market, and can collect information in real time using sensors and a database. Step 3: The matching department analyzes the vegetable information collected by the collection department and matches suitable vegetables with direct sales outlets. Based on the collected data, the matching department identifies vegetables suitable for each direct sales outlet, analyzes the data using generative AI, and proposes optimal matches.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 route generation unit that generates routes from farmers to each direct sales outlet; A collection unit that collects vegetable status information of the direct sales store based on the route information generated by the route generation unit; A matching unit that analyzes the status of the vegetables collected by the collection unit and matches suitable vegetables with direct sales outlets. A system characterized by:

2. The route generation unit Generate routes based on traffic, distance, and time 2. The system of claim 1.

3. The collecting unit Collecting inventory and demand information from each direct sales outlet 2. The system of claim 1.

4. The matching unit Based on the collected vegetable information, identify the vegetables that are suitable for each farm shop.

2. The system of claim 1.

5. The matching unit Proposing suitable direct sales outlets to farmers 2. The system of claim 1.

6. The route generation unit Provide the generated route information to the matching section 2. The system of claim 1.

7. The route generation unit Estimate farmers' sentiment and adjust route generation priorities based on the estimated sentiment 2. The system of claim 1.

8. The route generation unit When generating a route, the system refers to the farmer's past delivery history and suggests an appropriate route.

2. The system of claim 1.

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