system

Abstract

The system according to this embodiment aims to efficiently match people who want to purchase goods with people who want to deliver them. [Solution] The system according to the embodiment comprises a selection unit, a matching unit, a notification unit, a delivery unit, and a payment unit. The selection unit selects a product. The matching unit matches the purchaser and the delivery person based on the product selected by the selection unit. The notification unit notifies the delivery person matched by the matching unit. The delivery unit has the delivery person notified by the notification unit leave the product at the delivery location. The payment unit makes a payment based on the product selected by the selection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for anyone to easily perform the delivery service of an online supermarket, and efficient matching is required.

[0005] The system according to the embodiment aims to efficiently match a person who wants to purchase a product with a person who wants to perform the delivery.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a selection unit, a matching unit, a notification unit, a delivery unit, and a payment unit. The selection unit selects a product. The matching unit matches the purchaser and the delivery person based on the product selected by the selection unit. The notification unit notifies the delivery person matched by the matching unit. The delivery unit has the delivery person notified by the notification unit leave the product at the delivery location. The payment unit makes a payment based on the product selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently match people who want to purchase goods with people who want to deliver them. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The online supermarket delivery system according to an embodiment of the present invention is designed to allow anyone to participate as if they were helping out. This system uses AI to match people who want products with people who want to deliver them and earn a profit, thereby realizing an easy-to-use delivery service that does not require large-scale equipment. First, let's explain the process on the buyer's side. The buyer selects products and stores from the app and adds the products to their cart, at which point the AI ​​begins matching. The AI ​​displays 1 to 3 users close to the buyer's current location and the store, and selects one delivery person based on their messages and estimated arrival time. Payment is made using an electronic payment system, and the buyer specifies a location for leaving the package, so all they have to do is wait for the delivery. Next, let's explain the process on the delivery person's side. The delivery person receives a notification on the app, reviews the content, and decides whether or not to accept the delivery. If they accept, they enter an available time for delivery and visit the store at the scheduled time. They select the products, make payment through the app, place the products in the designated location, and take a photo with their camera to complete the process. By limiting delivery to unattended locations, deliveries can be made without face-to-face interaction. This system allows people who are busy working from home, people who have difficulty going out due to childcare, and people who are too tired to go shopping to easily receive their products. Furthermore, it allows people who want to earn extra money for lunch as a side job, or those who want to make good use of a new bicycle, to easily participate in deliveries. In addition, when the AI ​​matches buyers with delivery personnel, it selects the most suitable delivery person based on information such as current location, gender, past ratings, and age. This improves the accuracy of the matching and enables efficient deliveries. In this way, by utilizing AI, it is possible to make online supermarket delivery services easy to use, revitalize local communities, and provide opportunities for side jobs. This makes the online supermarket delivery system a mechanism that anyone can participate in as a kind of helper.

[0029] The online supermarket delivery system according to this embodiment comprises a selection unit, a matching unit, a notification unit, a delivery unit, and a payment unit. The selection unit selects products. The selection unit can select products based on, for example, product category, price range, user preferences, etc. When, for example, a user selects products and stores from the app and adds the products to their cart, the AI ​​starts matching. The matching unit matches the buyer with a delivery person based on the products selected by the selection unit. The matching unit selects the most suitable delivery person based on information such as current location, gender, past ratings, and age. For example, the matching unit displays 1 to 3 users close to the buyer's current location or store, and selects one delivery person based on their messages and estimated arrival time. The notification unit notifies the delivery person matched by the matching unit. For example, the notification unit notifies the delivery person, and the delivery person can choose whether or not to accept the notification. For example, the delivery person receives the notification on the app, reviews the content, and chooses whether or not to accept it. The delivery unit has the delivery person notified by the notification unit leave the products at the delivery location. The contactless delivery unit, for example, involves the delivery person placing the goods in a designated location and taking a photo with a camera to complete the process. The contactless delivery unit, for example, involves the delivery person selecting the goods, making payment via an app, placing the goods in a designated location, and taking a photo with a camera to complete the process. The payment unit processes payment based on the goods selected by the selection unit. The payment unit, for example, uses an electronic payment system to process payments. The payment unit, for example, uses an electronic payment system to process payments and allows the customer to specify a contactless delivery location, so the customer only needs to wait for arrival. This allows the online supermarket delivery system to efficiently handle the entire process from product selection to payment and delivery.

[0030] The selection section allows users to choose products. For example, it can select products based on product category, price range, and user preferences. Specifically, when a user opens the app and selects a product category, the selection section displays a list of products within that category. Users can further refine their search by applying filters such as price range, brand, and specific ingredients. The selection section also features a function to display personalized recommendations based on the user's past purchase history and ratings. For example, it analyzes products the user has previously purchased and rated to suggest similar or related products. Furthermore, the selection section provides special feature pages tailored to seasons and events, making it easy for users to find the best products for that time of year. For example, it might feature cold drinks and ice cream in the summer, and hot drinks and hot pot ingredients in the winter. When a user adds an item to their cart, the selection section uses that information to initiate the next step: matching. This allows the selection section to enable users to easily and quickly select products and proceed smoothly through the purchase process.

[0031] The matching unit matches buyers with delivery personnel based on the products selected by the selection unit. Specifically, the matching unit collects information such as the buyer's current location, gender, past ratings, and age, and selects the most suitable delivery person. For example, if the product selected by the buyer is fresh food, the matching unit will prioritize selecting a nearby delivery person who can deliver quickly. It also prioritizes delivery personnel with high past ratings to maintain service quality. The matching unit displays 1 to 3 users close to the buyer's current location or the store, and selects one delivery person based on their messages and estimated arrival time. Furthermore, it uses AI to analyze the delivery person's schedule and current delivery status in real time and calculate the optimal delivery route. This allows for shorter delivery times and increased efficiency. The matching unit also has a function that allows buyers and delivery personnel to exchange messages, enabling detailed instructions and questions regarding delivery. This allows the matching unit to facilitate smooth communication between buyers and delivery personnel and ensure a smooth delivery process.

[0032] The notification unit notifies delivery personnel matched by the matching unit. Specifically, the notification unit sends notifications to delivery personnel via the app, and upon receiving the notification, the delivery personnel can choose whether or not to accept the delivery. The notification unit allows delivery personnel to receive notifications via the app, review the content, and decide whether or not to accept the delivery. For example, the notification unit displays detailed product information, delivery address, and estimated arrival time to the delivery personnel, allowing them to review the information and decide whether or not to accept the delivery. Once the delivery personnel accept the delivery, the notification unit also notifies the buyer, enabling real-time tracking of the delivery progress. Furthermore, the notification unit also has a function to send a delivery completion notification to the buyer when the delivery personnel have completed the delivery, and to request a delivery rating. In this way, the notification unit enables smooth information sharing between buyers and delivery personnel throughout the entire delivery process, improving the efficiency and quality of deliveries.

[0033] The unattended delivery service allows delivery personnel, notified by the notification service, to leave the goods at the recipient's door. Specifically, the unattended delivery service ensures that the delivery person places the goods in a designated location and takes a photo with a camera to complete the process. For example, a delivery person might select an item, make payment via the app, place the item in the designated location, and take a photo with a camera to complete the process. The unattended delivery service provides guidelines to ensure that delivery personnel accurately place the goods in the location specified by the buyer. For example, it allows delivery personnel to place the goods in the location desired by the buyer, such as the front door, a delivery box, or a designated hiding place. Furthermore, after the delivery person places the goods, the unattended delivery service has a function to take a photo of the location with a camera and send the image to the buyer as proof of delivery completion. This allows the buyer to confirm that the goods have been delivered correctly and to use the service with peace of mind. The unattended delivery service ensures the safety and efficiency of delivery personnel when leaving goods at the door, and provides high-quality service to buyers.

[0034] The payment department processes payments based on the products selected by the selection department. Specifically, the payment department uses an electronic payment system. For example, by using an electronic payment system and specifying a delivery location, the buyer only needs to wait for the delivery to arrive. The payment department offers multiple electronic payment options, allowing users to pay in the most convenient way. For example, it supports various payment methods such as credit cards, debit cards, e-money, and QR code (registered trademark) payments. Furthermore, after the buyer completes the payment, the payment department immediately sends a confirmation email or notification so that they can verify the purchase details and payment amount. The payment department also strengthens security measures, employing encryption technology to securely protect users' personal and payment information. This allows the payment department to provide a secure environment for users to make payments and improve the reliability of the entire online supermarket delivery system.

[0035] The matching system can select the most suitable delivery personnel based on information such as current location, gender, past ratings, and age. For example, the matching system can select the nearest delivery personnel based on the current location. It can also select delivery personnel considering gender. Furthermore, the matching system can select highly reliable delivery personnel based on past ratings. For example, the matching system can select the nearest delivery personnel based on the current location. It can also select delivery personnel considering gender. It can also select highly reliable delivery personnel based on past ratings. This improves the accuracy of delivery personnel selection and enables more efficient deliveries.

[0036] The notification unit notifies the delivery person, who can choose whether or not to accept the delivery. For example, the notification unit notifies the delivery person, who can choose whether or not to accept the delivery. For example, the notification unit allows the delivery person to receive the notification via an app, review the content, and choose whether or not to accept the delivery. For example, if the delivery person accepts the delivery, they can enter their available delivery time and visit the store at the scheduled time. This allows for flexible delivery because the delivery person can choose whether or not to accept the delivery.

[0037] The contactless delivery system allows delivery personnel to place items in a designated location, take a photo with a camera, and complete the delivery. For example, the contactless delivery system allows delivery personnel to select items, pay via an app, place items in a designated location, and take a photo with a camera to complete the delivery. This enables delivery to be completed without face-to-face interaction.

[0038] The payment department can process payments using an electronic payment system. For example, the payment department can process payments using an electronic payment system, and by specifying a delivery location, the buyer only needs to wait for the package to arrive. This enables fast and secure payment through electronic payment.

[0039] The online supermarket delivery system according to this embodiment includes an evaluation unit. The evaluation unit can record the past evaluations of delivery personnel. The evaluation unit, for example, records the past evaluations of delivery personnel. By recording the past evaluations of delivery personnel, the evaluation unit can select highly reliable delivery personnel. By recording the evaluations of delivery personnel, the evaluation unit can select highly reliable delivery personnel.

[0040] The selection function can analyze past purchase history and automatically select products based on user preferences. For example, it can suggest similar products based on items the user has purchased in the past. For example, it can analyze the user's purchase frequency and automatically add regularly purchased items to the cart. For example, it can analyze the user's past purchase history to identify seasonal preferences and suggest products appropriate for the season. This enables product selection based on user preferences.

[0041] The selection function can filter products based on the user's current health status and dietary restrictions. For example, if the user is on a diet, the selection function will prioritize suggesting low-calorie products. If the user has allergies, the selection function will suggest products that do not contain allergens. If the user needs a specific nutrient, the selection function will suggest products that are rich in that nutrient. This allows users to select products that are tailored to their health status and dietary restrictions.

[0042] The selection function allows users to choose products from nearby stores, taking into account their geographical location. For example, the selection function may prioritize displaying products from the store closest to the user's current location. For example, the selection function may suggest products from stores with short delivery times based on the user's geographical location. For example, the selection function may suggest products from stores with low shipping costs based on the user's geographical location. This enables product selection based on the user's geographical location.

[0043] The selection unit can analyze a user's social media activity and suggest relevant products. For example, it can suggest products based on products the user has "liked" on social media. For example, it can suggest products from brands the user follows on social media. For example, it can analyze the content of a user's social media posts and suggest products they might be interested in. This makes it possible to suggest products based on the user's social media activity.

[0044] The matching unit can analyze the delivery history of delivery personnel and select the most suitable delivery person. For example, the matching unit can select a delivery person capable of fast delivery based on their past delivery history. For example, the matching unit can select a delivery person with a high rating based on their past delivery history. For example, the matching unit can select a delivery person who is familiar with a specific area based on their past delivery history. This makes it possible to select the most suitable delivery person based on their past delivery history.

[0045] The matching system can perform matching based on the delivery person's current schedule and traffic conditions. For example, the matching system considers the delivery person's current schedule and matches them during off-peak hours. For example, the matching system considers the delivery person's current traffic conditions and suggests routes that avoid congestion. For example, the matching system suggests the most efficient route based on the delivery person's current location information. This enables efficient matching based on the delivery person's current situation.

[0046] The matching unit can select the most suitable delivery person by considering the delivery person's geographical location. For example, the matching unit can select the delivery person closest to the delivery person's current location. For example, the matching unit can select the delivery person with the shortest delivery time based on the delivery person's geographical location. For example, the matching unit can select the delivery person with the lowest shipping cost based on the delivery person's geographical location. This makes it possible to select the most suitable delivery person based on the delivery person's geographical location.

[0047] The matching unit can analyze the social media activity of delivery personnel and select highly reliable delivery personnel. For example, the matching unit can select highly reliable delivery personnel based on their social media ratings. Alternatively, it can analyze the content of their social media activities to select highly reliable personnel. Or, for example, it can select highly reliable personnel based on the number of their social media followers. This makes it possible to select highly reliable delivery personnel based on their social media activity.

[0048] The notification unit can analyze the delivery person's past response history and select the most suitable notification method. For example, the notification unit may prioritize using notification methods that the delivery person has responded to quickly in the past. For example, the notification unit may prioritize using notification methods that the delivery person has received high ratings for in the past. For example, the notification unit may select the most effective notification method based on the delivery person's past response history. This makes it possible to select the most suitable notification method based on the delivery person's past response history.

[0049] The notification unit can adjust the timing of notifications based on the delivery person's current status. For example, the notification unit can send notifications during times when the delivery person is free. For example, the notification unit can send notifications during times when the delivery person is not on the move. For example, the notification unit can send notifications at the optimal time based on the delivery person's current location information. This makes it possible to adjust the optimal notification timing according to the delivery person's current status.

[0050] The notification unit can select the optimal notification method by considering the delivery person's geographical location. For example, the notification unit can send a notification to the location closest to the delivery person's current location. For example, the notification unit can send a notification to the location with the shortest delivery time based on the delivery person's geographical location. For example, the notification unit can send a notification to the location with the lowest shipping cost based on the delivery person's geographical location. This makes it possible to select the optimal notification method based on the delivery person's geographical location.

[0051] The notification unit can analyze the delivery person's social media activity and select a reliable notification method. For example, the notification unit can select a reliable notification method based on the delivery person's social media reputation. For example, the notification unit can analyze the content of the delivery person's social media activity and select a reliable notification method. For example, the notification unit can select a reliable notification method based on the delivery person's number of social media followers. This makes it possible to select a reliable notification method based on the delivery person's social media activity.

[0052] The delivery unit can analyze the delivery person's past delivery history and select the optimal delivery method. For example, the delivery unit can select a method that allows for quick delivery based on the delivery person's past delivery history. For example, the delivery unit can select a delivery method with a high rating based on the delivery person's past delivery history. For example, the delivery unit can select a delivery method suitable for a specific area based on the delivery person's past delivery history. This makes it possible to select the optimal delivery method based on the delivery person's past delivery history.

[0053] The delivery unit can adjust the timing of delivery based on the delivery person's current status. For example, the delivery unit can deliver packages during times when the delivery person is free. For example, the delivery unit can deliver packages during times when the delivery person is not on the move. For example, the delivery unit can deliver packages at the optimal time based on the delivery person's current location information. This makes it possible to adjust the optimal delivery timing according to the delivery person's current status.

[0054] The delivery unit can select the optimal delivery method by considering the delivery person's geographical location. For example, the delivery unit may leave the package at the location closest to the delivery person's current location. For example, the delivery unit may leave the package at a location with a shorter delivery time based on the delivery person's geographical location. For example, the delivery unit may leave the package at a location with lower shipping costs based on the delivery person's geographical location. This makes it possible to select the optimal delivery method based on the delivery person's geographical location.

[0055] The delivery department can analyze the social media activity of delivery personnel and select a reliable delivery method. For example, the delivery department can select a reliable delivery method based on the delivery personnel's social media reputation. For example, the delivery department can analyze the content of the delivery personnel's social media activities and select a reliable delivery method. For example, the delivery department can select a reliable delivery method based on the number of followers the delivery personnel have on social media. This makes it possible to select a reliable delivery method based on the delivery personnel's social media activity.

[0056] The payment processing unit can analyze past payment history and select the optimal payment method. For example, the payment processing unit can suggest the most frequently used payment method based on the user's past payment history. For example, the payment processing unit can suggest the highest-rated payment method based on the user's past payment history. For example, the payment processing unit can suggest a payment method to use during a specific time period based on the user's past payment history. This makes it possible to select the optimal payment method based on past payment history.

[0057] The payment unit can adjust the timing of payment based on the user's current financial situation. For example, the payment unit can consider the user's current financial situation and make the payment at the optimal time. For example, the payment unit can consider the user's current financial situation and propose installment payments. For example, the payment unit can consider the user's current financial situation and propose deferred payment. This makes it possible to adjust the optimal payment timing according to the user's financial situation.

[0058] The payment processing unit can select the optimal payment method by considering the user's geographical location. For example, the payment processing unit can suggest a payment method available in the location closest to the user's current location. For example, the payment processing unit can suggest a payment method available in a location with a short delivery time based on the user's geographical location. For example, the payment processing unit can suggest a payment method available in a location with low shipping costs based on the user's geographical location. This makes it possible to select the optimal payment method based on the user's geographical location.

[0059] The payment department can analyze users' social media activity and select reliable payment methods. For example, the payment department can propose reliable payment methods based on users' social media ratings. For example, the payment department can analyze the content of users' social media activities and propose reliable payment methods. For example, the payment department can propose reliable payment methods based on the number of followers users have on social media. This makes it possible to select reliable payment methods based on users' social media activity.

[0060] The evaluation unit can analyze the delivery person's past evaluation history and select the optimal evaluation method. For example, the evaluation unit can select a method that allows for rapid evaluation based on the delivery person's past evaluation history. For example, the evaluation unit can select a method that has received high ratings based on the delivery person's past evaluation history. For example, the evaluation unit can select an evaluation method suitable for a specific region based on the delivery person's past evaluation history. This makes it possible to select the optimal evaluation method based on the delivery person's past evaluation history.

[0061] The evaluation unit can select the optimal evaluation method by considering the delivery person's geographical location information. For example, the evaluation unit may perform the evaluation at the location closest to the delivery person's current location. For example, the evaluation unit may perform the evaluation at a location with a short evaluation time based on the delivery person's geographical location information. For example, the evaluation unit may perform the evaluation at a location with low shipping costs based on the delivery person's geographical location information. This makes it possible to select the optimal evaluation method based on the delivery person's geographical location information.

[0062] The evaluation department can analyze the delivery person's social media activity and select a reliable evaluation method. For example, the evaluation department can select a reliable evaluation method based on the delivery person's social media reputation. Alternatively, the evaluation department can analyze the content of the delivery person's social media activity and select a reliable evaluation method. For example, the evaluation department can select a reliable evaluation method based on the delivery person's social media follower count. This makes it possible to select a reliable evaluation method based on the delivery person's social media activity.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] Online supermarket delivery systems can further acquire users' health data and suggest products based on their health status. For example, if a user is using a wearable device linked to the app, the system can suggest health-related products based on data acquired from that device. Specifically, it can analyze the user's heart rate and sleep data, and suggest products with relaxing effects if the user is experiencing high stress levels. It can also suggest highly nutritious foods and supplements if the user is not getting enough exercise. This enables product suggestions tailored to the user's health condition, providing a more personalized service.

[0065] Online supermarket delivery systems can further analyze users' purchase history and suggest products tailored to specific events and seasons. For example, if a user has previously purchased certain items during Christmas or Halloween, related products can be suggested for those times of the year. Furthermore, by analyzing seasonal preferences, the system can suggest cold drinks and ice cream in the summer, and hot drinks and ingredients for hot pot dishes in the winter. This enables product suggestions that are tailored to the season and events based on the user's purchase history.

[0066] Online supermarket delivery systems can further analyze users' social media activity and suggest relevant products. For example, they can suggest products based on items users have "liked" or brands they follow on social media. They can also analyze users' posts and suggest products they might be interested in. This enables personalized product recommendations based on users' social media activity.

[0067] Online supermarket delivery systems can further analyze the delivery history of their drivers to select the most suitable drivers. For example, they can select drivers capable of fast delivery based on their past delivery history. They can also prioritize drivers with high ratings. Furthermore, they can select drivers who are familiar with specific areas. This enables the selection of the most suitable drivers based on their past delivery history, resulting in more efficient deliveries.

[0068] Online supermarket delivery systems can further optimize matching by considering the delivery person's current schedule and traffic conditions. For example, they can match delivery to available time slots based on the delivery person's current schedule. They can also suggest routes that avoid congestion based on the delivery person's current traffic conditions. Furthermore, they can suggest the most efficient route based on the delivery person's current location. This enables efficient matching based on the delivery person's current situation.

[0069] Online supermarket delivery systems can further consider the user's geographical location to select products from nearby stores. For example, they can prioritize displaying products from the store closest to the user's current location. They can also suggest products from stores with shorter delivery times based on the user's geographical location. Furthermore, they can suggest products from stores with lower shipping costs based on the user's geographical location. This enables product selection based on the user's geographical location, resulting in more efficient delivery.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The selection section chooses products. The selection section can choose products based on, for example, product category, price range, and user preferences. Once the user selects products and stores from the app and adds them to their cart, the AI ​​begins matching. Step 2: The matching unit matches buyers with delivery personnel based on the products selected by the selection unit. The matching unit selects the most suitable delivery person based on information such as current location, gender, past ratings, and age. It displays 1 to 3 users who are close to the buyer's current location or the store, and the buyer selects one delivery person after reviewing their messages and estimated arrival time. Step 3: The notification unit notifies the delivery person matched by the matching unit. For example, the notification unit notifies the delivery person, and the delivery person can choose whether or not to accept the notification. The delivery person receives the notification in the app, reviews the content, and chooses whether or not to accept it. Step 4: The delivery unit notifies the delivery person, who has been notified by the notification unit, to leave the goods. The delivery unit completes the process when, for example, the delivery person places the goods in the designated location and takes a picture with the camera. Alternatively, the delivery person selects the goods, makes payment via the app, places the goods in the designated location, takes a picture with the camera, and completes the process. Step 5: The payment unit processes the payment based on the items selected by the selection unit. The payment unit uses, for example, an electronic payment system. By using an electronic payment system and specifying a delivery location, the buyer only needs to wait for the package to arrive.

[0072] (Example of form 2) The online supermarket delivery system according to an embodiment of the present invention is designed to allow anyone to participate as if they were helping out. This system uses AI to match people who want products with people who want to deliver them and earn a profit, thereby realizing an easy-to-use delivery service that does not require large-scale equipment. First, let's explain the process on the buyer's side. The buyer selects products and stores from the app and adds the products to their cart, at which point the AI ​​begins matching. The AI ​​displays 1 to 3 users close to the buyer's current location and the store, and selects one delivery person based on their messages and estimated arrival time. Payment is made using an electronic payment system, and the buyer specifies a location for leaving the package, so all they have to do is wait for the delivery. Next, let's explain the process on the delivery person's side. The delivery person receives a notification on the app, reviews the content, and decides whether or not to accept the delivery. If they accept, they enter an available time for delivery and visit the store at the scheduled time. They select the products, make payment through the app, place the products in the designated location, and take a photo with their camera to complete the process. By limiting delivery to unattended locations, deliveries can be made without face-to-face interaction. This system allows people who are busy working from home, people who have difficulty going out due to childcare, and people who are too tired to go shopping to easily receive their products. Furthermore, it allows people who want to earn extra money for lunch as a side job, or those who want to make good use of a new bicycle, to easily participate in deliveries. In addition, when the AI ​​matches buyers with delivery personnel, it selects the most suitable delivery person based on information such as current location, gender, past ratings, and age. This improves the accuracy of the matching and enables efficient deliveries. In this way, by utilizing AI, it is possible to make online supermarket delivery services easy to use, revitalize local communities, and provide opportunities for side jobs. This makes the online supermarket delivery system a mechanism that anyone can participate in as a kind of helper.

[0073] The online supermarket delivery system according to this embodiment comprises a selection unit, a matching unit, a notification unit, a delivery unit, and a payment unit. The selection unit selects products. The selection unit can select products based on, for example, product category, price range, user preferences, etc. When, for example, a user selects products and stores from the app and adds the products to their cart, the AI ​​starts matching. The matching unit matches the buyer with a delivery person based on the products selected by the selection unit. The matching unit selects the most suitable delivery person based on information such as current location, gender, past ratings, and age. For example, the matching unit displays 1 to 3 users close to the buyer's current location or store, and selects one delivery person based on their messages and estimated arrival time. The notification unit notifies the delivery person matched by the matching unit. For example, the notification unit notifies the delivery person, and the delivery person can choose whether or not to accept the notification. For example, the delivery person receives the notification on the app, reviews the content, and chooses whether or not to accept it. The delivery unit has the delivery person notified by the notification unit leave the products at the delivery location. The contactless delivery unit, for example, involves the delivery person placing the goods in a designated location and taking a photo with a camera to complete the process. The contactless delivery unit, for example, involves the delivery person selecting the goods, making payment via an app, placing the goods in a designated location, and taking a photo with a camera to complete the process. The payment unit processes payment based on the goods selected by the selection unit. The payment unit, for example, uses an electronic payment system to process payments. The payment unit, for example, uses an electronic payment system to process payments and allows the customer to specify a contactless delivery location, so the customer only needs to wait for arrival. This allows the online supermarket delivery system to efficiently handle the entire process from product selection to payment and delivery.

[0074] The selection section allows users to choose products. For example, it can select products based on product category, price range, and user preferences. Specifically, when a user opens the app and selects a product category, the selection section displays a list of products within that category. Users can further refine their search by applying filters such as price range, brand, and specific ingredients. The selection section also features a function to display personalized recommendations based on the user's past purchase history and ratings. For example, it analyzes products the user has previously purchased and rated to suggest similar or related products. Furthermore, the selection section provides special feature pages tailored to seasons and events, making it easy for users to find the best products for that time of year. For example, it might feature cold drinks and ice cream in the summer, and hot drinks and hot pot ingredients in the winter. When a user adds an item to their cart, the selection section uses that information to initiate the next step: matching. This allows the selection section to enable users to easily and quickly select products and proceed smoothly through the purchase process.

[0075] The matching unit matches buyers with delivery personnel based on the products selected by the selection unit. Specifically, the matching unit collects information such as the buyer's current location, gender, past ratings, and age, and selects the most suitable delivery person. For example, if the product selected by the buyer is fresh food, the matching unit will prioritize selecting a nearby delivery person who can deliver quickly. It also prioritizes delivery personnel with high past ratings to maintain service quality. The matching unit displays 1 to 3 users close to the buyer's current location or the store, and selects one delivery person based on their messages and estimated arrival time. Furthermore, it uses AI to analyze the delivery person's schedule and current delivery status in real time and calculate the optimal delivery route. This allows for shorter delivery times and increased efficiency. The matching unit also has a function that allows buyers and delivery personnel to exchange messages, enabling detailed instructions and questions regarding delivery. This allows the matching unit to facilitate smooth communication between buyers and delivery personnel and ensure a smooth delivery process.

[0076] The notification unit notifies delivery personnel matched by the matching unit. Specifically, the notification unit sends notifications to delivery personnel via the app, and upon receiving the notification, the delivery personnel can choose whether or not to accept the delivery. The notification unit allows delivery personnel to receive notifications via the app, review the content, and decide whether or not to accept the delivery. For example, the notification unit displays detailed product information, delivery address, and estimated arrival time to the delivery personnel, allowing them to review the information and decide whether or not to accept the delivery. Once the delivery personnel accept the delivery, the notification unit also notifies the buyer, enabling real-time tracking of the delivery progress. Furthermore, the notification unit also has a function to send a delivery completion notification to the buyer when the delivery personnel have completed the delivery, and to request a delivery rating. In this way, the notification unit enables smooth information sharing between buyers and delivery personnel throughout the entire delivery process, improving the efficiency and quality of deliveries.

[0077] The unattended delivery service allows delivery personnel, notified by the notification service, to leave the goods at the recipient's door. Specifically, the unattended delivery service ensures that the delivery person places the goods in a designated location and takes a photo with a camera to complete the process. For example, a delivery person might select an item, make payment via the app, place the item in the designated location, and take a photo with a camera to complete the process. The unattended delivery service provides guidelines to ensure that delivery personnel accurately place the goods in the location specified by the buyer. For example, it allows delivery personnel to place the goods in the location desired by the buyer, such as the front door, a delivery box, or a designated hiding place. Furthermore, after the delivery person places the goods, the unattended delivery service has a function to take a photo of the location with a camera and send the image to the buyer as proof of delivery completion. This allows the buyer to confirm that the goods have been delivered correctly and to use the service with peace of mind. The unattended delivery service ensures the safety and efficiency of delivery personnel when leaving goods at the door, and provides high-quality service to buyers.

[0078] The payment department processes payments based on the products selected by the selection department. Specifically, the payment department uses an electronic payment system. For example, by using an electronic payment system and specifying a delivery location, the buyer only needs to wait for the delivery to arrive. The payment department offers multiple electronic payment options, allowing users to pay in the most convenient way. For example, it supports various payment methods such as credit cards, debit cards, e-money, and QR code payments. Furthermore, after the buyer completes the payment, the payment department immediately sends a confirmation email or notification so that they can verify the purchase details and payment amount. The payment department also strengthens security measures, employing encryption technology to securely protect users' personal and payment information. This allows the payment department to provide a secure environment for users to make payments and improve the reliability of the entire online supermarket delivery system.

[0079] The matching system can select the most suitable delivery personnel based on information such as current location, gender, past ratings, and age. For example, the matching system can select the nearest delivery personnel based on the current location. It can also select delivery personnel considering gender. Furthermore, the matching system can select highly reliable delivery personnel based on past ratings. For example, the matching system can select the nearest delivery personnel based on the current location. It can also select delivery personnel considering gender. It can also select highly reliable delivery personnel based on past ratings. This improves the accuracy of delivery personnel selection and enables more efficient deliveries.

[0080] The notification unit notifies the delivery person, who can choose whether or not to accept the delivery. For example, the notification unit notifies the delivery person, who can choose whether or not to accept the delivery. For example, the notification unit allows the delivery person to receive the notification via an app, review the content, and choose whether or not to accept the delivery. For example, if the delivery person accepts the delivery, they can enter their available delivery time and visit the store at the scheduled time. This allows for flexible delivery because the delivery person can choose whether or not to accept the delivery.

[0081] The contactless delivery system allows delivery personnel to place items in a designated location, take a photo with a camera, and complete the delivery. For example, the contactless delivery system allows delivery personnel to select items, pay via an app, place items in a designated location, and take a photo with a camera to complete the delivery. This enables delivery to be completed without face-to-face interaction.

[0082] The payment department can process payments using an electronic payment system. For example, the payment department can process payments using an electronic payment system, and by specifying a delivery location, the buyer only needs to wait for the package to arrive. This enables fast and secure payment through electronic payment.

[0083] The online supermarket delivery system according to this embodiment includes an evaluation unit. The evaluation unit can record the past evaluations of delivery personnel. The evaluation unit, for example, records the past evaluations of delivery personnel. By recording the past evaluations of delivery personnel, the evaluation unit can select highly reliable delivery personnel. By recording the evaluations of delivery personnel, the evaluation unit can select highly reliable delivery personnel.

[0084] The selection unit can estimate the user's emotions and suggest product selections based on those emotions. For example, if the user is feeling stressed, the selection unit will prioritize suggesting products with relaxing effects. For example, if the user is having fun, the selection unit will suggest entertainment-related products. For example, if the user is tired, the selection unit will suggest health foods or refreshing items. This makes it possible to suggest products that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The selection function can analyze past purchase history and automatically select products based on user preferences. For example, it can suggest similar products based on items the user has purchased in the past. For example, it can analyze the user's purchase frequency and automatically add regularly purchased items to the cart. For example, it can analyze the user's past purchase history to identify seasonal preferences and suggest products appropriate for the season. This enables product selection based on user preferences.

[0086] The selection function can filter products based on the user's current health status and dietary restrictions. For example, if the user is on a diet, the selection function will prioritize suggesting low-calorie products. If the user has allergies, the selection function will suggest products that do not contain allergens. If the user needs a specific nutrient, the selection function will suggest products that are rich in that nutrient. This allows users to select products that are tailored to their health status and dietary restrictions.

[0087] The selection unit can estimate the user's emotions and determine the priority of product selection based on the estimated emotions. For example, if the user is feeling stressed, the selection unit will prioritize displaying products with relaxing effects. For example, if the user is having fun, the selection unit will prioritize displaying entertainment-related products. For example, if the user is tired, the selection unit will prioritize displaying health foods and refreshing items. This allows for the determination of product selection priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The selection function allows users to choose products from nearby stores, taking into account their geographical location. For example, the selection function may prioritize displaying products from the store closest to the user's current location. For example, the selection function may suggest products from stores with short delivery times based on the user's geographical location. For example, the selection function may suggest products from stores with low shipping costs based on the user's geographical location. This enables product selection based on the user's geographical location.

[0089] The selection unit can analyze a user's social media activity and suggest relevant products. For example, it can suggest products based on products the user has "liked" on social media. For example, it can suggest products from brands the user follows on social media. For example, it can analyze the content of a user's social media posts and suggest products they might be interested in. This makes it possible to suggest products based on the user's social media activity.

[0090] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is stressed, the matching unit will prioritize matching them with a delivery person who can deliver quickly. For example, if the user is relaxed, the matching unit will prioritize matching them with a highly-rated delivery person. For example, if the user is in a hurry, the matching unit will prioritize matching them with the nearest delivery person. This makes it possible to adjust the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The matching unit can analyze the delivery history of delivery personnel and select the most suitable delivery person. For example, the matching unit can select a delivery person capable of fast delivery based on their past delivery history. For example, the matching unit can select a delivery person with a high rating based on their past delivery history. For example, the matching unit can select a delivery person who is familiar with a specific area based on their past delivery history. This makes it possible to select the most suitable delivery person based on their past delivery history.

[0092] The matching system can perform matching based on the delivery person's current schedule and traffic conditions. For example, the matching system considers the delivery person's current schedule and matches them during off-peak hours. For example, the matching system considers the delivery person's current traffic conditions and suggests routes that avoid congestion. For example, the matching system suggests the most efficient route based on the delivery person's current location information. This enables efficient matching based on the delivery person's current situation.

[0093] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated emotions. For example, if the user is stressed, the matching unit will prioritize matching with a delivery person who can deliver quickly. For example, if the user is relaxed, the matching unit will prioritize matching with a highly-rated delivery person. For example, if the user is in a hurry, the matching unit will prioritize matching with the nearest delivery person. This allows for the determination of matching priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The matching unit can select the most suitable delivery person by considering the delivery person's geographical location. For example, the matching unit can select the delivery person closest to the delivery person's current location. For example, the matching unit can select the delivery person with the shortest delivery time based on the delivery person's geographical location. For example, the matching unit can select the delivery person with the lowest shipping cost based on the delivery person's geographical location. This makes it possible to select the most suitable delivery person based on the delivery person's geographical location.

[0095] The matching unit can analyze the social media activity of delivery personnel and select highly reliable delivery personnel. For example, the matching unit can select highly reliable delivery personnel based on their social media ratings. Alternatively, it can analyze the content of their social media activities to select highly reliable personnel. Or, for example, it can select highly reliable personnel based on the number of their social media followers. This makes it possible to select highly reliable delivery personnel based on their social media activity.

[0096] The notification unit can estimate the user's emotions and adjust the content of notifications based on those emotions. For example, if the user is stressed, the notification unit will send a concise and easy-to-understand notification. If the user is relaxed, the notification unit will send a notification containing detailed information. If the user is in a hurry, the notification unit will send a notification that allows for a quick response. This makes it possible to adjust notification content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The notification unit can analyze the delivery person's past response history and select the most suitable notification method. For example, the notification unit may prioritize using notification methods that the delivery person has responded to quickly in the past. For example, the notification unit may prioritize using notification methods that the delivery person has received high ratings for in the past. For example, the notification unit may select the most effective notification method based on the delivery person's past response history. This makes it possible to select the most suitable notification method based on the delivery person's past response history.

[0098] The notification unit can adjust the timing of notifications based on the delivery person's current status. For example, the notification unit can send notifications during times when the delivery person is free. For example, the notification unit can send notifications during times when the delivery person is not on the move. For example, the notification unit can send notifications at the optimal time based on the delivery person's current location information. This makes it possible to adjust the optimal notification timing according to the delivery person's current status.

[0099] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. If the user is relaxed, the notification unit will prioritize notifications containing detailed information. If the user is in a hurry, the notification unit will prioritize notifications that require a quick response. This allows for notification priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The notification unit can select the optimal notification method by considering the delivery person's geographical location. For example, the notification unit can send a notification to the location closest to the delivery person's current location. For example, the notification unit can send a notification to the location with the shortest delivery time based on the delivery person's geographical location. For example, the notification unit can send a notification to the location with the lowest shipping cost based on the delivery person's geographical location. This makes it possible to select the optimal notification method based on the delivery person's geographical location.

[0101] The notification unit can analyze the delivery person's social media activity and select a reliable notification method. For example, the notification unit can select a reliable notification method based on the delivery person's social media reputation. For example, the notification unit can analyze the content of the delivery person's social media activity and select a reliable notification method. For example, the notification unit can select a reliable notification method based on the delivery person's number of social media followers. This makes it possible to select a reliable notification method based on the delivery person's social media activity.

[0102] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is stressed, the delivery unit will deliver quickly. For example, if the user is relaxed, the delivery unit will deliver carefully. For example, if the user is in a hurry, the delivery unit will deliver in the shortest possible time. This makes it possible to adjust the delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The delivery unit can analyze the delivery person's past delivery history and select the optimal delivery method. For example, the delivery unit can select a method that allows for quick delivery based on the delivery person's past delivery history. For example, the delivery unit can select a delivery method with a high rating based on the delivery person's past delivery history. For example, the delivery unit can select a delivery method suitable for a specific area based on the delivery person's past delivery history. This makes it possible to select the optimal delivery method based on the delivery person's past delivery history.

[0104] The delivery unit can adjust the timing of delivery based on the delivery person's current status. For example, the delivery unit can deliver packages during times when the delivery person is free. For example, the delivery unit can deliver packages during times when the delivery person is not on the move. For example, the delivery unit can deliver packages at the optimal time based on the delivery person's current location information. This makes it possible to adjust the optimal delivery timing according to the delivery person's current status.

[0105] The delivery unit can estimate the user's emotions and determine the priority of delivery based on the estimated emotions. For example, if the user is stressed, the delivery unit will deliver quickly. For example, if the user is relaxed, the delivery unit will deliver carefully. For example, if the user is in a hurry, the delivery unit will deliver in the shortest possible time. This allows for the determination of delivery priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The delivery unit can select the optimal delivery method by considering the delivery person's geographical location. For example, the delivery unit may leave the package at the location closest to the delivery person's current location. For example, the delivery unit may leave the package at a location with a shorter delivery time based on the delivery person's geographical location. For example, the delivery unit may leave the package at a location with lower shipping costs based on the delivery person's geographical location. This makes it possible to select the optimal delivery method based on the delivery person's geographical location.

[0107] The delivery department can analyze the social media activity of delivery personnel and select a reliable delivery method. For example, the delivery department can select a reliable delivery method based on the delivery personnel's social media reputation. For example, the delivery department can analyze the content of the delivery personnel's social media activities and select a reliable delivery method. For example, the delivery department can select a reliable delivery method based on the number of followers the delivery personnel have on social media. This makes it possible to select a reliable delivery method based on the delivery personnel's social media activity.

[0108] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated emotions. For example, if the user is stressed, the payment unit provides a simple and easy-to-understand payment method. For example, if the user is relaxed, the payment unit provides a payment method that includes detailed information. For example, if the user is in a hurry, the payment unit provides a payment method that can be handled quickly. This makes it possible to adjust the payment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The payment processing unit can analyze past payment history and select the optimal payment method. For example, the payment processing unit can suggest the most frequently used payment method based on the user's past payment history. For example, the payment processing unit can suggest the highest-rated payment method based on the user's past payment history. For example, the payment processing unit can suggest a payment method to use during a specific time period based on the user's past payment history. This makes it possible to select the optimal payment method based on past payment history.

[0110] The payment unit can adjust the timing of payment based on the user's current financial situation. For example, the payment unit can consider the user's current financial situation and make the payment at the optimal time. For example, the payment unit can consider the user's current financial situation and propose installment payments. For example, the payment unit can consider the user's current financial situation and propose deferred payment. This makes it possible to adjust the optimal payment timing according to the user's financial situation.

[0111] The payment unit can estimate the user's emotions and determine payment priorities based on those emotions. For example, if the user is stressed, the payment unit will prioritize providing a payment method that allows for quick processing. For example, if the user is relaxed, the payment unit will prioritize providing a payment method that includes detailed information. For example, if the user is in a hurry, the payment unit will prioritize providing a concise and quick payment method. This allows for the determination of payment priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The payment processing unit can select the optimal payment method by considering the user's geographical location. For example, the payment processing unit can suggest a payment method available in the location closest to the user's current location. For example, the payment processing unit can suggest a payment method available in a location with a short delivery time based on the user's geographical location. For example, the payment processing unit can suggest a payment method available in a location with low shipping costs based on the user's geographical location. This makes it possible to select the optimal payment method based on the user's geographical location.

[0113] The payment department can analyze users' social media activity and select reliable payment methods. For example, the payment department can propose reliable payment methods based on users' social media ratings. For example, the payment department can analyze the content of users' social media activities and propose reliable payment methods. For example, the payment department can propose reliable payment methods based on the number of followers users have on social media. This makes it possible to select reliable payment methods based on users' social media activity.

[0114] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated emotions. For example, if the user is stressed, the evaluation unit provides a concise and easy-to-understand evaluation method. For example, if the user is relaxed, the evaluation unit provides an evaluation method that includes detailed information. For example, if the user is in a hurry, the evaluation unit provides an evaluation method that allows for quick responses. This makes it possible to adjust the evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The evaluation unit can analyze the delivery person's past evaluation history and select the optimal evaluation method. For example, the evaluation unit can select a method that allows for rapid evaluation based on the delivery person's past evaluation history. For example, the evaluation unit can select a method that has received high ratings based on the delivery person's past evaluation history. For example, the evaluation unit can select an evaluation method suitable for a specific region based on the delivery person's past evaluation history. This makes it possible to select the optimal evaluation method based on the delivery person's past evaluation history.

[0116] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is stressed, the evaluation unit will prioritize providing evaluation methods that allow for quick responses. For example, if the user is relaxed, the evaluation unit will prioritize providing evaluation methods that include detailed information. For example, if the user is in a hurry, the evaluation unit will prioritize providing concise and quick evaluation methods. This allows for the determination of evaluation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The evaluation unit can select the optimal evaluation method by considering the delivery person's geographical location information. For example, the evaluation unit may perform the evaluation at the location closest to the delivery person's current location. For example, the evaluation unit may perform the evaluation at a location with a short evaluation time based on the delivery person's geographical location information. For example, the evaluation unit may perform the evaluation at a location with low shipping costs based on the delivery person's geographical location information. This makes it possible to select the optimal evaluation method based on the delivery person's geographical location information.

[0118] The evaluation department can analyze the delivery person's social media activity and select a reliable evaluation method. For example, the evaluation department can select a reliable evaluation method based on the delivery person's social media reputation. Alternatively, the evaluation department can analyze the content of the delivery person's social media activity and select a reliable evaluation method. For example, the evaluation department can select a reliable evaluation method based on the delivery person's social media follower count. This makes it possible to select a reliable evaluation method based on the delivery person's social media activity.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] Online supermarket delivery systems can further acquire users' health data and suggest products based on their health status. For example, if a user is using a wearable device linked to the app, the system can suggest health-related products based on data acquired from that device. Specifically, it can analyze the user's heart rate and sleep data, and suggest products with relaxing effects if the user is experiencing high stress levels. It can also suggest highly nutritious foods and supplements if the user is not getting enough exercise. This enables product suggestions tailored to the user's health condition, providing a more personalized service.

[0121] Online supermarket delivery systems can further estimate user emotions and adjust delivery timing based on those emotions. For example, if a user is stressed, the delivery person can be instructed to deliver quickly. Conversely, if a user is relaxed, the delivery time can be made more flexible. Furthermore, if a user is in a hurry, the nearest delivery person can be prioritized to ensure a quick delivery. This enables flexible delivery tailored to the user's emotions, which is expected to improve user satisfaction.

[0122] Online supermarket delivery systems can further analyze users' purchase history and suggest products tailored to specific events and seasons. For example, if a user has previously purchased certain items during Christmas or Halloween, related products can be suggested for those times of the year. Furthermore, by analyzing seasonal preferences, the system can suggest cold drinks and ice cream in the summer, and hot drinks and ingredients for hot pot dishes in the winter. This enables product suggestions that are tailored to the season and events based on the user's purchase history.

[0123] Online supermarket delivery systems can further analyze users' social media activity and suggest relevant products. For example, they can suggest products based on items users have "liked" or brands they follow on social media. They can also analyze users' posts and suggest products they might be interested in. This enables personalized product recommendations based on users' social media activity.

[0124] Online supermarket delivery systems can further estimate the user's emotions and adjust notification content based on those estimates. For example, if a user is stressed, a concise and easy-to-understand notification can be sent. If the user is relaxed, a notification with more detailed information can be sent. Furthermore, if the user is in a hurry, a notification that allows for quick action can be sent. This allows for notification content to be adjusted according to the user's emotions, improving user convenience.

[0125] Online supermarket delivery systems can further analyze the delivery history of their drivers to select the most suitable drivers. For example, they can select drivers capable of fast delivery based on their past delivery history. They can also prioritize drivers with high ratings. Furthermore, they can select drivers who are familiar with specific areas. This enables the selection of the most suitable drivers based on their past delivery history, resulting in more efficient deliveries.

[0126] Online supermarket delivery systems can further estimate user emotions and adjust their evaluation methods based on those estimates. For example, if a user is stressed, they can be provided with a concise and easy-to-understand evaluation method. If a user is relaxed, they can be provided with an evaluation method that includes detailed information. Furthermore, if a user is in a hurry, they can be provided with an evaluation method that allows for quick responses. This enables the evaluation method to be adjusted according to the user's emotions, improving user convenience.

[0127] Online supermarket delivery systems can further optimize matching by considering the delivery person's current schedule and traffic conditions. For example, they can match delivery to available time slots based on the delivery person's current schedule. They can also suggest routes that avoid congestion based on the delivery person's current traffic conditions. Furthermore, they can suggest the most efficient route based on the delivery person's current location. This enables efficient matching based on the delivery person's current situation.

[0128] Online supermarket delivery systems can further estimate user emotions and adjust payment methods based on those estimates. For example, if a user is stressed, a simple and easy-to-understand payment method can be offered. If a user is relaxed, a payment method with more detailed information can be offered. Furthermore, if a user is in a hurry, a payment method that allows for quick processing can be offered. This enables adjustments to payment methods according to user emotions, improving user convenience.

[0129] Online supermarket delivery systems can further consider the user's geographical location to select products from nearby stores. For example, they can prioritize displaying products from the store closest to the user's current location. They can also suggest products from stores with shorter delivery times based on the user's geographical location. Furthermore, they can suggest products from stores with lower shipping costs based on the user's geographical location. This enables product selection based on the user's geographical location, resulting in more efficient delivery.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The selection section chooses products. The selection section can choose products based on, for example, product category, price range, and user preferences. Once the user selects products and stores from the app and adds them to their cart, the AI ​​begins matching. Step 2: The matching unit matches buyers with delivery personnel based on the products selected by the selection unit. The matching unit selects the most suitable delivery person based on information such as current location, gender, past ratings, and age. It displays 1 to 3 users who are close to the buyer's current location or the store, and the buyer selects one delivery person after reviewing their messages and estimated arrival time. Step 3: The notification unit notifies the delivery person matched by the matching unit. For example, the notification unit notifies the delivery person, and the delivery person can choose whether or not to accept the notification. The delivery person receives the notification in the app, reviews the content, and chooses whether or not to accept it. Step 4: The delivery unit notifies the delivery person, who has been notified by the notification unit, to leave the goods. The delivery unit completes the process when, for example, the delivery person places the goods in the designated location and takes a picture with the camera. Alternatively, the delivery person selects the goods, makes payment via the app, places the goods in the designated location, takes a picture with the camera, and completes the process. Step 5: The payment unit processes the payment based on the items selected by the selection unit. The payment unit uses, for example, an electronic payment system. By using an electronic payment system and specifying a delivery location, the buyer only needs to wait for the package to arrive.

[0132] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0133] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0134] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0135] Each of the multiple elements described above, including the selection unit, matching unit, notification unit, delivery unit, payment unit, and evaluation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14, and when the user selects products and stores from the app and adds products to the cart, the AI ​​starts matching. The matching unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and selects the most suitable delivery person based on information such as current location, gender, past evaluation, and age. The notification unit is implemented, for example, by the control unit 46A of the smart device 14, and notifies the delivery person, who then chooses whether or not to accept the notification. The delivery unit is implemented, for example, by the control unit 46A of the smart device 14, and the delivery person places the product in the designated location and takes a picture with the camera to complete the process. The payment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and makes the payment using an electronic payment system. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and records the delivery person's past evaluations. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] As shown in Figure 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the selection unit, matching unit, notification unit, delivery unit, payment unit, and evaluation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214, and when the user selects a product or store from the app and adds the product to the cart, the AI ​​starts matching. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12, and selects the most suitable delivery person based on information such as current location, gender, past evaluation, and age. The notification unit is implemented by the control unit 46A of the smart glasses 214, and notifies the delivery person, who then chooses whether or not to accept the notification. The delivery unit is implemented by the control unit 46A of the smart glasses 214, and the delivery person places the product in the designated location and takes a picture with the camera to complete the process. The payment unit is implemented by the identification processing unit 290 of the data processing unit 12, and makes the payment using an electronic payment system. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and records the delivery person's past evaluations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the selection unit, matching unit, notification unit, delivery unit, payment unit, and evaluation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314, and when the user selects products and stores from the app and adds products to the cart, the AI ​​starts matching. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12, and selects the most suitable delivery person based on information such as current location, gender, past evaluation, and age. The notification unit is implemented by the control unit 46A of the headset terminal 314, and notifies the delivery person, who then chooses whether or not to accept the notification. The delivery unit is implemented by the control unit 46A of the headset terminal 314, and the delivery person places the product in the designated location and takes a picture with the camera to complete the process. The payment unit is implemented by the identification processing unit 290 of the data processing unit 12, and makes the payment using an electronic payment system. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and records the delivery person's past evaluations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] As shown in Figure 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.

[0170] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0172] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0174] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0175] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0176] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0177] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the selection unit, matching unit, notification unit, delivery unit, payment unit, and evaluation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414, and when the user selects products and stores from the app and adds the products to the cart, the AI ​​starts matching. The matching unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and selects the most suitable delivery person based on information such as current location, gender, past evaluation, and age. The notification unit is implemented by, for example, the control unit 46A of the robot 414, and notifies the delivery person, who then chooses whether or not to accept the notification. The delivery unit is implemented by, for example, the control unit 46A of the robot 414, and the delivery person places the products in the designated location and takes a picture with the camera to complete the process. The payment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and makes the payment using an electronic payment system. The evaluation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and records the delivery person's past evaluations. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0185] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0187] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0188] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0189] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0193] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0194] 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.

[0195] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0196] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0197] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0198] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0200] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0201] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0202] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0203] (Note 1) The selection section for choosing products, A matching unit that matches buyers and delivery personnel based on the products selected by the selection unit, A notification unit that notifies the delivery person matched by the matching unit, A delivery unit where the delivery person notified by the aforementioned notification unit leaves the goods, The system includes a settlement unit that performs settlement based on the product selected by the selection unit. A system characterized by the following features. (Note 2) The matching unit is We select the most suitable delivery person based on information such as current location, gender, past ratings, and age. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, The delivery person will be notified and can choose whether or not to accept the delivery. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned mounting section is The delivery person places the product in the designated location, takes a picture with a camera, and the transaction is complete. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned settlement unit, Payments are made using an electronic payment system. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes an evaluation unit that records the past performance of delivery personnel. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is It estimates the user's emotions and makes product selection suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection unit is By analyzing past purchase history, the system automatically selects products based on the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is Filter products based on the user's current health status and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is It estimates the user's emotions and determines the priority of product selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is The system selects products from nearby stores, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is Analyze users' social media activity and suggest relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 14) The matching unit is We analyze the delivery person's past delivery history to select the most suitable delivery person. The system described in Appendix 2, characterized by the features described herein. (Note 15) The matching unit is Matching is done considering the delivery person's current schedule and traffic conditions. The system described in Appendix 2, characterized by the features described herein. (Note 16) The matching unit is The system estimates the user's emotions and determines matching priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 17) The matching unit is The system selects the most suitable delivery person by considering their geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 18) The matching unit is Analyze the social media activity of delivery personnel to select reliable delivery personnel. The system described in Appendix 2, characterized by the features described herein. (Note 19) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 20) The aforementioned notification unit, Analyze the delivery person's past response history to select the optimal notification method. The system described in Appendix 3, characterized by the features described herein. (Note 21) The aforementioned notification unit, We adjust the timing of notifications based on the delivery person's current status. The system described in Appendix 3, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 23) The aforementioned notification unit, The optimal notification method is selected, taking into account the delivery person's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 24) The aforementioned notification unit, Analyze the social media activity of delivery personnel to select the most reliable notification method. The system described in Appendix 3, characterized by the features described herein. (Note 25) The aforementioned mounting section is It estimates the user's emotions and adjusts the placement method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 26) The aforementioned mounting section is The system analyzes the delivery person's past delivery history to select the optimal delivery method. The system described in Appendix 4, characterized by the features described herein. (Note 27) The aforementioned mounting section is The timing of contactless delivery will be adjusted based on the delivery person's current status. The system described in Appendix 4, characterized by the features described herein. (Note 28) The aforementioned mounting section is The system estimates the user's emotions and determines the priority of delivery based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 29) The aforementioned mounting section is The optimal delivery method is selected, taking into account the delivery person's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 30) The aforementioned mounting section is Analyze the social media activity of delivery personnel to select a reliable contactless delivery method. The system described in Appendix 4, characterized by the features described herein. (Note 31) The aforementioned settlement unit, It estimates the user's emotions and adjusts the payment method based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 32) The aforementioned settlement unit, Analyze past payment history to select the optimal payment method. The system described in Appendix 5, characterized by the features described herein. (Note 33) The aforementioned settlement unit, Adjust payment timing based on the user's current financial situation. The system described in Appendix 5, characterized by the features described herein. (Note 34) The aforementioned settlement unit, It estimates the user's emotions and determines payment priorities based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 35) The aforementioned settlement unit, The optimal payment method is selected based on the user's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 36) The aforementioned settlement unit, Analyze users' social media activity to select reliable payment methods. The system described in Appendix 5, characterized by the features described herein. (Note 37) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation method based on the estimated user emotions. The system described in Appendix 6, characterized by the features described herein. (Note 38) The evaluation unit described above, Analyze the delivery person's past rating history and select the optimal rating method. The system described in Appendix 6, characterized by the features described herein. (Note 39) The evaluation unit described above, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 6, characterized by the features described herein. (Note 40) The evaluation unit described above, The optimal evaluation method will be selected, taking into account the geographical location information of the delivery person. The system described in Appendix 6, characterized by the features described herein. (Note 41) The evaluation unit described above, Analyze the social media activity of delivery personnel and select a reliable evaluation method. The system described in Appendix 6, characterized by the features described herein. [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The selection section for choosing products, A matching unit that matches buyers and delivery personnel based on the products selected by the selection unit, A notification unit that notifies the delivery person matched by the matching unit, A delivery unit where the delivery person notified by the aforementioned notification unit leaves the goods, The system includes a settlement unit that performs settlement based on the product selected by the selection unit. A system characterized by the following features.

2. The matching unit is We select the most suitable delivery person based on information such as current location, gender, past ratings, and age. The system according to feature 1.

3. The aforementioned notification unit, The delivery person will be notified and can choose whether or not to accept the delivery. The system according to feature 1.

4. The aforementioned mounting section is The delivery person places the product in the designated location, takes a picture with a camera, and the transaction is complete. The system according to feature 1.

5. The aforementioned settlement unit, Payments are made using an electronic payment system. The system according to feature 1.

6. It includes an evaluation unit that records the past performance of delivery personnel. The system according to feature 1.

7. The aforementioned selection unit is It estimates the user's emotions and makes product selection suggestions based on those estimated emotions. The system according to feature 1.

8. The aforementioned selection unit is By analyzing past purchase history, the system automatically selects products based on the user's preferences. The system according to feature 1.

9. The aforementioned selection unit is Filter products based on the user's current health status and dietary restrictions. The system according to feature 1.

10. The aforementioned selection unit is It estimates the user's emotions and determines the priority of product selection based on those estimated emotions. The system according to feature 1.

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