System

Abstract

An object of a system according to an embodiment is to perform price comparison on a merchant-by-merchant basis and reduce the shipping charge and the time and effort for shipping and receiving.SOLUTION: A system according to an embodiment includes a price comparison unit, a shipping fee reduction unit, a CO2 reduction unit, a selection assist unit, and a demand-data providing unit. The price comparison unit performs price comparison on a merchant-by-merchant basis. A shipping cost reduction component reduces shipping costs and shipping and receiving efforts based on the purchase from the merchant suggested by the price comparison component. The CO2 reduction unit reduces CO2 emissions based on the bulk shipment proposed by the shipping cost reduction unit. The selection assist unit assists the user's selection. The demand data providing unit provides demand data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, online shopping is dominated by price comparisons for each product, rather than by merchant, which can increase shipping costs and the hassle of shipping and receiving items.

[0005] The system according to the embodiment aims to compare prices on a merchant-by-merchant basis and reduce the hassle of shipping fees and delivery / receiving. [Means for solving the problem]

[0006] The system according to the embodiment includes a price comparison unit, a shipping cost reduction unit, a CO2 reduction unit, a selection assistance unit, and a demand data provision unit. The price comparison unit compares prices on a merchant-by-merchant basis. The shipping cost reduction unit reduces shipping costs and the hassle of shipping and receiving based on purchases from merchants suggested by the price comparison unit. The CO2 reduction unit reduces CO2 emissions based on bulk shipping suggested by the shipping cost reduction unit. The selection assistance unit assists users in making selections. The demand data provision unit provides demand data. [Effects of the Invention]

[0007] The system according to the embodiment performs price comparisons on a merchant-by-merchant basis, reducing the hassle of shipping fees and delivery and receipt. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The online shopping system according to an embodiment of the present invention allows comparisons not only on a product-by-product basis but also on a merchant-by-merchant basis. This system reduces the hassle of shipping fees and delivery and receipt for both users and merchants, and also contributes to reducing CO2 emissions. It also uses AI to assist users in their selections and provides demand data to businesses, thereby supporting merchants' business operations. This allows the online shopping system to improve convenience for users and merchants and contribute to environmental protection.

[0029] An online shopping system according to an embodiment includes a price comparison unit, a shipping fee reduction unit, a CO2 reduction unit, a selection assistance unit, and a demand data provision unit. The price comparison unit compares prices on a merchant-by-merchant basis. For example, AI analyzes a user's purchasing history and preferences and suggests the most suitable merchant. The shipping fee reduction unit reduces shipping fees and the hassle of shipping and receiving items based on purchases from merchants suggested by the price comparison unit. For example, AI analyzes the combination of items a user purchases and suggests the most efficient merchant. The CO2 reduction unit reduces CO2 emissions based on the bulk shipping suggested by the shipping fee reduction unit. For example, AI reduces delivery truck travel distances and CO2 emissions by bulk shipping from the same merchant. The selection assistance unit assists users in making selections. For example, AI analyzes a user's purchasing history and preferences and suggests the most suitable products and merchants. The demand data provision unit provides demand data. For example, AI analyzes a user's selection data and provides the demand data to merchants. As a result, the online shopping system according to the embodiment can improve convenience for users and merchants and contribute to environmental protection. For example, users can reduce shipping costs by bulk shipping, and merchants can operate their businesses efficiently based on demand data. Furthermore, by reducing CO2 emissions, it is expected to contribute to the realization of a sustainable society.

[0030] The price comparison unit analyzes not only a user's purchase history, but also their social media activity or search history, enabling more accurate merchant suggestions. For example, the price comparison unit uses AI to analyze a user's social media posts, likes, shares, and other activities to understand the user's interests. This allows the unit to identify and suggest merchants that the user prefers. The price comparison unit also uses AI to analyze a user's search history to identify products and merchants that the user is interested in. For example, the unit can suggest optimal merchants based on keywords the user has searched for in the past or websites they have visited. This enables the unit to make highly accurate suggestions based on the user's interests.

[0031] The price comparison unit monitors merchant inventory status in real time and can prioritize suggesting merchants with abundant stock. For example, the price comparison unit uses AI to collect merchant inventory data in real time and identify merchants with abundant stock. For example, it avoids merchants with low stock and prioritizes suggesting merchants with ample stock. The price comparison unit also uses AI to monitor inventory status and builds a system that issues alerts when stock decreases. For example, it notifies the merchant when stock falls below a certain threshold. This reduces the risk of users running out of stock and enables smoother purchases.

[0032] The shipping cost reduction unit can analyze the user's address or delivery destination information and propose the most efficient delivery route. For example, the shipping cost reduction unit uses AI to analyze the user's address or delivery destination information and identify the most efficient delivery route. For example, if there are multiple delivery destinations, it calculates and proposes the shortest route. The shipping cost reduction unit also builds a system in which AI takes traffic conditions and weather information into consideration and proposes the optimal delivery route. For example, it proposes a route that avoids traffic congestion and bad weather. This makes delivery more efficient and enables shipping costs to be reduced.

[0033] The shipping cost reduction department can compare multiple delivery options and propose the most cost-effective method. For example, the shipping cost reduction department uses AI to analyze multiple delivery options and identify the most cost-effective method. For example, it compares delivery companies and delivery methods and proposes the cheapest option. The shipping cost reduction department also builds a system where AI compares the cost and delivery time of delivery options and finds the optimal balance. For example, it proposes an option with low cost and short delivery time. This allows you to select a cost-effective delivery method.

[0034] The CO2 Reduction Department can analyze CO2 emission data from delivery companies and suggest the most environmentally friendly delivery company. For example, the CO2 Reduction Department uses AI to collect CO2 emission data from each delivery company and identify the most environmentally friendly delivery company. For example, it may preferentially suggest delivery companies that use electric vehicles. The CO2 Reduction Department will also build a system where AI evaluates the eco-friendly efforts of delivery companies and suggests environmentally friendly delivery companies. For example, it may preferentially suggest delivery companies that use renewable energy. This allows CO2 emissions to be reduced by selecting an environmentally friendly delivery company.

[0035] The CO2 reduction unit can analyze users' purchasing patterns and prioritize suggesting eco-friendly products. For example, the CO2 reduction unit uses AI to analyze users' purchasing patterns and identify eco-friendly products. For example, it may prioritize suggesting products made from renewable materials. The CO2 reduction unit also builds a system in which AI collects evaluation data on eco-friendly products and suggests them to users. For example, it may prioritize suggesting merchants that handle environmentally friendly products. This contributes to environmental protection by prioritizing the suggestion of eco-friendly products.

[0036] The selection assistance unit analyzes not only the user's purchase history, but also their social media activity or search history, enabling more accurate product suggestions. For example, the selection assistance unit uses AI to analyze the user's social media posts, likes, shares, and other activities to understand the user's interests. This allows the unit to identify and suggest products that the user prefers. The selection assistance unit also uses AI to analyze the user's search history to identify products that the user is interested in. For example, the unit can suggest optimal products based on keywords the user has searched for in the past or websites they have visited. This enables the unit to make highly accurate suggestions based on the user's interests.

[0037] The selection assistance unit monitors product inventory status in real time and can prioritize suggesting products with abundant stock. For example, the selection assistance unit uses AI to collect product inventory data in real time and identify products with abundant stock. For example, it avoids products with low stock and prioritizes suggesting products with sufficient stock. The selection assistance unit also builds a system in which AI monitors inventory status and issues an alert if stock decreases. For example, it notifies the merchant if stock falls below a certain threshold. This reduces the risk of users running out of stock and enables smoother purchases.

[0038] The demand data providing unit can analyze merchant inventory data in real time and perform demand forecasts. For example, AI collects merchant inventory data in real time and performs demand forecasts. For example, it analyzes inventory fluctuations and predicts periods of high demand. The demand data providing unit also builds a system in which AI analyzes past sales data and performs demand forecasts. For example, it predicts fluctuations in demand during specific seasons or events and notifies the merchant. This allows for demand forecasts to optimize the merchant's inventory management.

[0039] The demand data provider can analyze the merchant's sales data and propose the optimal sales strategy. For example, the demand data provider uses AI to analyze the merchant's sales data and propose the optimal sales strategy. For example, it identifies and proposes the best time and price range for a particular product to sell well. The demand data provider also builds a system in which AI analyzes competitors' sales data and proposes the optimal sales strategy. For example, it adjusts the strategy based on the competitors' pricing and promotions. This can be expected to increase the merchant's sales by proposing the optimal sales strategy.

[0040] The demand data provider analyzes not only demand data but also competitor data, allowing it to propose competitive strategies to merchants. For example, the demand data provider uses AI to analyze the merchant's demand data and competitor data to propose competitive strategies. For example, it adjusts strategies based on competitor pricing and promotions. The demand data provider also builds a system in which AI analyzes market trend data and proposes competitive strategies. For example, it proposes new products to merchants based on specific trends. This makes it possible to propose strategies that take competitor data into account.

[0041] The demand data provider can analyze the effectiveness of a merchant's advertising and propose optimal advertising strategies. For example, the demand data provider uses AI to analyze the merchant's advertising data and propose optimal advertising strategies. For example, it identifies and proposes the time and target demographic for which a particular advertisement is effective. The demand data provider also builds a system in which AI monitors the effectiveness of advertising campaigns in real time and proposes optimal advertising strategies. For example, it analyzes click-through rates and conversion rates of advertisements and proposes effective advertising strategies. This improves the effectiveness of the merchant's advertising by proposing optimal advertising strategies.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have purchased in the past based on the user's purchase history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0044] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0045] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have previously purchased based on the user's purchase history and search history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0046] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0047] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have previously purchased based on the user's purchase history and search history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0048] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0049] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have previously purchased based on the user's purchase history and search history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0050] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0051] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have previously purchased based on the user's purchase history and search history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0052] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The price comparison section compares prices across merchants. For example, AI can analyze the user's purchasing history and preferences to suggest the most suitable merchant. Step 2: The shipping cost reduction unit reduces shipping costs and the hassle of receiving and shipping based on purchases from merchants suggested by the price comparison unit. For example, AI can analyze the combination of products a user purchases and suggest the most efficient merchant. Step 3: The CO2 Reduction Department reduces CO2 emissions based on the combined shipping proposed by the Shipping Reduction Department. For example, AI can reduce the distance traveled by delivery trucks by combining shipments from the same merchant, thereby reducing CO2 emissions. Step 4: The selection assistance unit assists the user in making a selection. For example, AI can analyze the user's purchase history and preferences to suggest the most suitable products and merchants. Step 5: The demand data providing unit provides demand data. For example, the AI ​​analyzes the user's selection data and provides the demand data to the merchant.

[0055] (Example 2) The online shopping system according to an embodiment of the present invention allows comparisons not only on a product-by-product basis but also on a merchant-by-merchant basis. This system reduces the hassle of shipping fees and delivery and receipt for both users and merchants, and also contributes to reducing CO2 emissions. It also uses AI to assist users in their selections and provides demand data to businesses, thereby supporting merchants' business operations. This allows the online shopping system to improve convenience for users and merchants and contribute to environmental protection.

[0056] An online shopping system according to an embodiment includes a price comparison unit, a shipping fee reduction unit, a CO2 reduction unit, a selection assistance unit, and a demand data provision unit. The price comparison unit compares prices on a merchant-by-merchant basis. For example, AI analyzes a user's purchasing history and preferences and suggests the most suitable merchant. The shipping fee reduction unit reduces shipping fees and the hassle of shipping and receiving items based on purchases from merchants suggested by the price comparison unit. For example, AI analyzes the combination of items a user purchases and suggests the most efficient merchant. The CO2 reduction unit reduces CO2 emissions based on the bulk shipping suggested by the shipping fee reduction unit. For example, AI reduces delivery truck travel distances and CO2 emissions by bulk shipping from the same merchant. The selection assistance unit assists users in making selections. For example, AI analyzes a user's purchasing history and preferences and suggests the most suitable products and merchants. The demand data provision unit provides demand data. For example, AI analyzes a user's selection data and provides the demand data to merchants. As a result, the online shopping system according to the embodiment can improve convenience for users and merchants and contribute to environmental protection. For example, users can reduce shipping costs by bulk shipping, and merchants can operate their businesses efficiently based on demand data. Furthermore, by reducing CO2 emissions, it is expected to contribute to the realization of a sustainable society.

[0057] The price comparison unit analyzes not only a user's purchase history, but also their social media activity or search history, enabling more accurate merchant suggestions. For example, the price comparison unit uses AI to analyze a user's social media posts, likes, shares, and other activities to understand the user's interests. This allows the unit to identify and suggest merchants that the user prefers. The price comparison unit also uses AI to analyze a user's search history to identify products and merchants that the user is interested in. For example, the unit can suggest optimal merchants based on keywords the user has searched for in the past or websites they have visited. This enables the unit to make highly accurate suggestions based on the user's interests.

[0058] The price comparison unit monitors merchant inventory status in real time and can prioritize suggesting merchants with abundant stock. For example, the price comparison unit uses AI to collect merchant inventory data in real time and identify merchants with abundant stock. For example, it avoids merchants with low stock and prioritizes suggesting merchants with ample stock. The price comparison unit also uses AI to monitor inventory status and builds a system that issues alerts when stock decreases. For example, it notifies the merchant when stock falls below a certain threshold. This reduces the risk of users running out of stock and enables smoother purchases.

[0059] The price comparison unit can use the emotion estimation function to preferentially suggest merchants about which the user has had positive feelings in the past. For example, the price comparison unit estimates the emotion at the time of purchase based on the user's past purchase history and identifies merchants about which the user has had positive feelings. For example, the price comparison unit analyzes reviews and ratings after purchases and preferentially suggests merchants with high ratings. The price comparison unit also uses the emotion estimation function to identify merchants about which the user has had positive feelings in the past. For example, if the user has had positive feelings about a specific product, the price comparison unit preferentially suggests merchants that carry that product. This makes it possible to make suggestions that increase user satisfaction.

[0060] The shipping cost reduction unit can analyze the user's address or delivery destination information and propose the most efficient delivery route. For example, the shipping cost reduction unit uses AI to analyze the user's address or delivery destination information and identify the most efficient delivery route. For example, if there are multiple delivery destinations, it calculates and proposes the shortest route. The shipping cost reduction unit also builds a system in which AI takes traffic conditions and weather information into consideration and proposes the optimal delivery route. For example, it proposes a route that avoids traffic congestion and bad weather. This makes delivery more efficient and enables shipping costs to be reduced.

[0061] The shipping cost reduction department can compare multiple delivery options and propose the most cost-effective method. For example, the shipping cost reduction department uses AI to analyze multiple delivery options and identify the most cost-effective method. For example, it compares delivery companies and delivery methods and proposes the cheapest option. The shipping cost reduction department also builds a system where AI compares the cost and delivery time of delivery options and finds the optimal balance. For example, it proposes an option with low cost and short delivery time. This allows you to select a cost-effective delivery method.

[0062] The shipping cost reduction unit can use the emotion estimation function to suggest the delivery method that causes the least stress to the user. For example, the shipping cost reduction unit uses the emotion estimation function to identify the delivery method that causes the least stress to the user. For example, it analyzes past delivery history and feedback to suggest a method that causes the least stress. The shipping cost reduction unit also builds a system in which AI collects data on users' emotions regarding delivery and suggests the optimal delivery method. For example, it prioritizes suggesting delivery methods that users have previously expressed positive emotions about. This makes it possible to select a delivery method that reduces stress for the user.

[0063] The CO2 Reduction Department can analyze CO2 emission data from delivery companies and suggest the most environmentally friendly delivery company. For example, the CO2 Reduction Department uses AI to collect CO2 emission data from each delivery company and identify the most environmentally friendly delivery company. For example, it may preferentially suggest delivery companies that use electric vehicles. The CO2 Reduction Department will also build a system where AI evaluates the eco-friendly efforts of delivery companies and suggests environmentally friendly delivery companies. For example, it may preferentially suggest delivery companies that use renewable energy. This allows CO2 emissions to be reduced by selecting an environmentally friendly delivery company.

[0064] The CO2 reduction unit can analyze users' purchasing patterns and prioritize suggesting eco-friendly products. For example, the CO2 reduction unit uses AI to analyze users' purchasing patterns and identify eco-friendly products. For example, it may prioritize suggesting products made from renewable materials. The CO2 reduction unit also builds a system in which AI collects evaluation data on eco-friendly products and suggests them to users. For example, it may prioritize suggesting merchants that handle environmentally friendly products. This contributes to environmental protection by prioritizing the suggestion of eco-friendly products.

[0065] The CO2 reduction unit can use the emotion estimation function to make suggestions that will make the user feel positive about environmental protection. For example, the CO2 reduction unit uses the emotion estimation function to suggest products and merchants that will make the user feel positive about environmental protection. For example, to a user who showed positive emotions when purchasing an eco-friendly product, the CO2 reduction unit can suggest similar products. The CO2 reduction unit also builds a system in which AI analyzes the user's emotion data and makes suggestions that will make the user feel positive about environmental protection. For example, it can provide information about environmental protection to raise the user's awareness. This makes it possible to make suggestions that will make the user feel positive about environmental protection.

[0066] The selection assistance unit analyzes not only the user's purchase history, but also their social media activity or search history, enabling more accurate product suggestions. For example, the selection assistance unit uses AI to analyze the user's social media posts, likes, shares, and other activities to understand the user's interests. This allows the unit to identify and suggest products that the user prefers. The selection assistance unit also uses AI to analyze the user's search history to identify products that the user is interested in. For example, the unit can suggest optimal products based on keywords the user has searched for in the past or websites they have visited. This enables the unit to make highly accurate suggestions based on the user's interests.

[0067] The selection assistance unit monitors product inventory status in real time and can prioritize suggesting products with abundant stock. For example, the selection assistance unit uses AI to collect product inventory data in real time and identify products with abundant stock. For example, it avoids products with low stock and prioritizes suggesting products with sufficient stock. The selection assistance unit also builds a system in which AI monitors inventory status and issues an alert if stock decreases. For example, it notifies the merchant if stock falls below a certain threshold. This reduces the risk of users running out of stock and enables smoother purchases.

[0068] The selection assist unit can use the emotion estimation function to preferentially suggest products that the user has felt positive about in the past. The selection assist unit, for example, estimates the emotion at the time of purchase based on the user's past purchasing history and identifies products that the user felt positive about. For example, the selection assist unit analyzes reviews and ratings after purchase and preferentially suggests products that have received high ratings. The selection assist unit also uses the emotion estimation function to suggest products related to products that the user has felt positive about in the past. For example, if the user has felt positive about a specific product, the selection assist unit preferentially suggests products related to that product. This makes it possible to make suggestions that increase user satisfaction.

[0069] The demand data providing unit can analyze merchant inventory data in real time and perform demand forecasts. For example, AI collects merchant inventory data in real time and performs demand forecasts. For example, it analyzes inventory fluctuations and predicts periods of high demand. The demand data providing unit also builds a system in which AI analyzes past sales data and performs demand forecasts. For example, it predicts fluctuations in demand during specific seasons or events and notifies the merchant. This allows for demand forecasts to optimize the merchant's inventory management.

[0070] The demand data provider can analyze the merchant's sales data and propose the optimal sales strategy. For example, the demand data provider uses AI to analyze the merchant's sales data and propose the optimal sales strategy. For example, it identifies and proposes the best time and price range for a particular product to sell well. The demand data provider also builds a system in which AI analyzes competitors' sales data and proposes the optimal sales strategy. For example, it adjusts the strategy based on the competitors' pricing and promotions. This can be expected to increase the merchant's sales by proposing the optimal sales strategy.

[0071] The demand data providing unit can use the emotion estimation function to identify products for which users feel the most positive emotions and provide the demand data to merchants. The demand data providing unit, for example, uses the emotion estimation function to identify products for which users feel the most positive emotions and provides the demand data to merchants. For example, it analyzes facial expressions and voice data at the time of purchase to identify products for which users expressed positive emotions. The demand data providing unit also builds a system in which AI analyzes users' emotion data and provides demand data for products for which users expressed positive emotions. For example, if a user expressed positive emotions toward a particular product, it provides the demand data for that product to the merchant. This allows the merchant's sales strategy to be optimized by providing demand data based on user emotions.

[0072] The demand data provider analyzes not only demand data but also competitor data, allowing it to propose competitive strategies to merchants. For example, the demand data provider uses AI to analyze the merchant's demand data and competitor data to propose competitive strategies. For example, it adjusts strategies based on competitor pricing and promotions. The demand data provider also builds a system in which AI analyzes market trend data and proposes competitive strategies. For example, it proposes new products to merchants based on specific trends. This makes it possible to propose strategies that take competitor data into account.

[0073] The demand data provider can analyze the effectiveness of a merchant's advertising and propose optimal advertising strategies. For example, the demand data provider uses AI to analyze the merchant's advertising data and propose optimal advertising strategies. For example, it identifies and proposes the time and target demographic for which a particular advertisement is effective. The demand data provider also builds a system in which AI monitors the effectiveness of advertising campaigns in real time and proposes optimal advertising strategies. For example, it analyzes click-through rates and conversion rates of advertisements and proposes effective advertising strategies. This improves the effectiveness of the merchant's advertising by proposing optimal advertising strategies.

[0074] The demand data providing unit can use the emotion estimation function to propose a marketing strategy that the user is most likely to empathize with. The demand data providing unit, for example, uses the emotion estimation function to identify a marketing strategy that the user is most likely to empathize with and proposes it to the merchant. For example, it analyzes emotion data on past marketing campaigns to identify strategies that have a high degree of empathy. The demand data providing unit also builds a system in which AI analyzes user emotion data and proposes marketing strategies that have a high degree of empathy. For example, if a user expresses positive emotions toward a particular campaign, it proposes a strategy similar to that campaign. This improves the marketing effectiveness of the merchant by proposing marketing strategies based on the user's emotions.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have purchased in the past based on the user's purchase history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0077] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0078] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have previously purchased based on the user's purchase history and search history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0079] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0080] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have previously purchased based on the user's purchase history and search history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0081] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0082] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have previously purchased based on the user's purchase history and search history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0083] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0084] The online shopping system can also have a reminder function that encourages repeat purchases of products that users have previously purchased based on the user's purchase history and search history. For example, a reminder can be sent for consumables or products purchased regularly when a certain period of time has passed since purchase. The reminder function can also send reminders for products that the user has previously given high ratings. This improves user convenience and encourages repeat purchases.

[0085] The online shopping system can further include a new product suggestion unit that suggests new products that a user might be interested in based on the user's purchase history and search history. For example, AI can analyze a user's past purchase history and search history to identify related new products. The new product suggestion unit can also suggest products related to products that the user has previously given high ratings. This makes it possible to suggest new products based on the user's interests and concerns.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The price comparison section compares prices across merchants. For example, AI can analyze the user's purchasing history and preferences to suggest the most suitable merchant. Step 2: The shipping cost reduction unit reduces shipping costs and the hassle of receiving and shipping based on purchases from merchants suggested by the price comparison unit. For example, AI can analyze the combination of products a user purchases and suggest the most efficient merchant. Step 3: The CO2 Reduction Department reduces CO2 emissions based on the combined shipping proposed by the Shipping Reduction Department. For example, AI can reduce the distance traveled by delivery trucks by combining shipments from the same merchant, thereby reducing CO2 emissions. Step 4: The selection assistance unit assists the user in making a selection. For example, AI can analyze the user's purchase history and preferences to suggest the most suitable products and merchants. Step 5: The demand data providing unit provides demand data. For example, the AI ​​analyzes the user's selection data and provides the demand data to the merchant.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[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 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.

[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 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).

[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] 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.

[0129] 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.

[0130] 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.

[0131] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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. [Explanation of symbols]

[0155] 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 price comparison unit that performs price comparisons on a merchant basis; a shipping cost reduction unit that reduces shipping costs and shipping and receiving hassles based on purchases from merchants suggested by the price comparison unit; a CO2 reduction unit that reduces CO2 emissions based on the bulk shipping proposed by the shipping fee reduction unit; a selection assist unit that assists a user in making a selection; a demand data providing unit that provides demand data; A system characterized by:

2. The price comparison unit Analyze not only the user's purchase history but also their social media activity or search history to make more accurate merchant suggestions 2. The system of claim 1.

3. The shipping fee reduction unit Analyzing the user's address or delivery destination information and proposing the most efficient delivery route 2. The system of claim 1.

4. The CO2 reduction unit is Analyze the CO2 emissions data of delivery companies and suggest the most environmentally friendly delivery company 2. The system of claim 1.

5. The selection assist unit Analyze not only the user's purchase history but also their social media activity or search history to make more accurate product suggestions 2. The system of claim 1.

6. The demand data providing unit Analyze the merchant's inventory data in real time and make demand forecasts 2. The system of claim 1.

7. The price comparison unit Using an emotion estimation function, the merchants with whom the user has had positive emotions in the past are preferentially suggested.

2. The system of claim 1.

8. The demand data providing unit Using a sentiment estimation function, the product for which the user has the most positive sentiment is identified and the demand data is provided to the merchant.

2. The system of claim 1.

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