system

Abstract

The system according to this embodiment aims to improve the online shopping experience. [Solution] The system according to the embodiment comprises a reception unit, a mobile unit, a manipulator unit, a camera unit, a storage unit, a payment unit, a delivery unit, and a detection unit. The reception unit receives online operations from the purchaser. The mobile unit moves within the supermarket based on the information received by the reception unit. The manipulator unit picks up the product at the location moved by the mobile unit. The camera unit checks the condition and expiration date of the product picked up by the manipulator unit. The storage unit places the product in the basket based on the product information checked by the camera unit. The payment unit automatically pays for the product placed in the basket by the storage unit. The delivery unit delivers the product paid for by the payment unit to the customer's home. The detection unit monitors the operations of the mobile unit and the manipulator unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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 the chatbot's 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, there is a problem that the online shopping experience is inferior to shopping in a physical store.

[0005] The system according to the embodiment aims to improve the online shopping experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a mobile unit, a manipulator unit, a camera unit, a storage unit, a payment unit, a delivery unit, and a detection unit. The reception unit receives online orders from the customer. The mobile unit moves around the supermarket based on the information received by the reception unit. The manipulator unit picks up the product at the location reached by the mobile unit. The camera unit checks the condition and expiration date of the product picked up by the manipulator unit. The storage unit places the product in the basket based on the product information confirmed by the camera unit. The payment unit automatically pays for the product placed in the basket by the storage unit. The delivery unit delivers the product paid for by the payment unit to the customer's home. The detection unit monitors the operation of the mobile unit and the manipulator unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the online shopping experience. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires 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 system providing a new shopping experience according to an embodiment of the present invention is a system in which a shopper accesses a supermarket online and operates a robot to automatically select, purchase, and deliver products within the supermarket. The system provides a new shopping experience in which the shopper accesses a supermarket online and operates a robot. The robot is autonomous and can move freely within the supermarket. The robot is equipped with a manipulator for picking up products and a camera for checking the condition and expiration date of the products. The shopper can check the condition of the products through the camera and decide whether to purchase them. Next, the robot places the products selected by the shopper into a basket. The products in the basket are automatically paid for. Payment is made using the shopper's credit card information or an electronic payment system. Furthermore, the products selected by the shopper are delivered to their home by an autonomous truck. The autonomous truck automatically calculates the route from the supermarket to the shopper's home and delivers safely. This eliminates the need for the shopper to go to the supermarket. In addition, a detection system is constantly operating to prevent the robot from hitting people or obstacles while moving around the supermarket or operating the manipulator. The detection system monitors the robot to prevent contact with people or obstacles, ensuring safe operation. This system allows shoppers to enjoy grocery shopping from the comfort of their homes. It is particularly convenient for busy people and those with mobility issues, offering a new shopping experience. This system allows shoppers to operate online, select items within the supermarket, purchase them, and have them delivered automatically.

[0029] The system providing a new shopping experience according to this embodiment comprises a reception unit, a mobile unit, a manipulator unit, a camera unit, a storage unit, a payment unit, a delivery unit, and a detection unit. The reception unit receives online operations from the purchaser. The reception unit can receive operations through, for example, a web browser, a mobile app, or voice commands. The mobile unit moves around the supermarket based on the information received by the reception unit. The mobile unit can move by, for example, wheels, caterpillar tracks, or by flight. The manipulator unit picks up products at the location moved by the mobile unit. The manipulator unit can pick up products using, for example, a gripper or suction pad. The camera unit checks the condition and expiration date of the products picked up by the manipulator unit. The camera unit can check the condition and expiration date of products by, for example, image analysis or barcode scanning. The storage unit places products into the basket based on the product information confirmed by the camera unit. The storage unit can place products into the basket by, for example, alignment or force adjustment. The payment unit automatically processes payment for items placed in a basket by the storage unit. The payment unit can automatically process payment for items using methods such as credit cards, electronic money, and QR codes (registered trademarks). The delivery unit delivers the items that have been paid for by the payment unit to the customer's home. The delivery unit can deliver items to the customer's home using methods such as drones, robotic cars, and courier services. The detection unit monitors the operation of the mobile unit and the manipulator unit. The detection unit can monitor the operation of the mobile unit and the manipulator unit using methods such as cameras, sensors, and log analysis. As a result, the system, which provides a new shopping experience according to the embodiment, allows the customer to operate online, select items in the supermarket, purchase them, and have them delivered automatically.

[0030] The reception desk receives online requests from customers. These requests can be received via various methods, such as web browsers, mobile apps, and voice commands. Specifically, customers can browse product lists and select desired items through web browsers or mobile apps. When using voice commands, customers can specify product names and quantities using voice recognition technology. The reception desk receives these requests in real time and relays customer requests throughout the system. Furthermore, the reception desk manages customer account information and past purchase history, enabling it to provide personalized product suggestions and promotions. For example, it can suggest related and new products based on past and frequently purchased items. The reception desk also handles customer inquiries and support requests, responding quickly through chatbots and customer support. This allows the reception desk to provide customers with a smooth and personalized shopping experience.

[0031] The mobile unit moves within the supermarket based on information received by the reception unit. The mobile unit can move using methods such as wheels, tracks, or flight. Specifically, a wheeled mobile robot can smoothly move through the aisles of the supermarket and reach the location of designated products. Using tracks improves the ability to overcome steps and obstacles, enabling movement in a wider variety of environments. When using flight, a drone can move through the air and quickly access product shelves. The mobile unit calculates the optimal route based on the supermarket's map information and product placement information, and moves efficiently. Furthermore, the mobile unit can use sensors and cameras to understand its surroundings in real time and move safely while avoiding obstacles. For example, ultrasonic sensors and LiDAR can be used to detect surrounding obstacles and people, and avoid collisions. The mobile unit is also equipped with a battery management system and can return to a charging station at the appropriate time to enable long operating hours. As a result, the mobile unit can move efficiently and safely within the supermarket and support the quick retrieval of products.

[0032] The manipulator unit picks up the product at a position moved by the mobile unit. The manipulator unit can pick up products using, for example, a gripper or suction pad. Specifically, the gripper can grasp the product with appropriate force according to its shape and size. The suction pad uses vacuum suction to firmly lift products with smooth surfaces. The manipulator unit has arms with multiple degrees of freedom, allowing it to accurately pick up products while adjusting their position and orientation. Furthermore, the manipulator unit is equipped with force sensors and tactile sensors to sense the weight and hardness of the product and handle it with appropriate force. For example, when handling fragile products or containers containing liquids, it adjusts the force to lift them safely. In addition, the manipulator unit can use AI-based image recognition technology to identify the shape and label of products and accurately select them. As a result, the manipulator unit can efficiently and safely pick up products of various shapes and sizes.

[0033] The camera unit checks the condition and expiration date of the product picked up by the manipulator unit. The camera unit can check the condition and expiration date of the product using methods such as image analysis and barcode scanning. Specifically, the camera unit uses a high-resolution camera to photograph the surface of the product and uses image analysis technology to detect the presence of scratches or stains. When using barcode scanning, it reads the product's barcode and obtains information such as the expiration date and manufacturing date. Furthermore, the camera unit can utilize AI-based image recognition technology to identify the product's label and packaging design and obtain accurate product information. For example, the AI ​​recognizes the letters and numbers written on the product label and extracts the expiration date and manufacturing date. The camera unit can also analyze the color and shape of the product and evaluate its quality. As a result, the camera unit can accurately check the condition and expiration date of the product and provide high-quality products.

[0034] The storage unit places items into the basket based on information about the items confirmed by the camera unit. The storage unit can place items into the basket using methods such as positioning and force adjustment. Specifically, the storage unit positions items appropriately according to their shape and size, and adjusts the force applied to place them into the basket. For example, when handling fragile items or containers containing liquids, it adjusts the force to safely place them into the basket. The storage unit is equipped with arms that have multiple degrees of freedom, allowing it to accurately place items into the basket while adjusting their position and orientation. Furthermore, the storage unit is equipped with force sensors and tactile sensors, which sense the weight and hardness of items, enabling it to handle items with the appropriate force. As a result, the storage unit can efficiently and safely place items of various shapes and sizes into the basket.

[0035] The payment unit automatically processes payments for items placed in the shopping cart by the storage unit. The payment unit can automatically process payments using methods such as credit cards, electronic money, and QR codes. Specifically, it retrieves credit card and electronic money information based on the buyer's account information, calculates the total amount of goods, and processes the payment. When using QR codes, the buyer can complete the payment by scanning the QR code with their smartphone. The payment unit has enhanced security measures to safely manage buyers' personal and payment information. For example, it uses encryption technology to protect data and prevent unauthorized access and information leaks. Furthermore, the payment unit can confirm payments, issue receipts, and manage purchase history. This allows the payment unit to process payments quickly and securely, providing buyers with a smooth shopping experience.

[0036] The delivery department delivers goods that have been paid for by the payment department to the customer's home. The delivery department can deliver goods to the customer's home using methods such as drones, robotic cars, and courier services. Specifically, when using drones, the appropriate drone is selected according to the weight and size of the goods, and it automatically flies to the customer's address using GPS. When using robotic cars, the delivery route is calculated and safe delivery is carried out using autonomous driving technology. When using courier services, goods are delivered quickly in cooperation with the delivery company. The delivery department can track the delivery status in real time and notify the customer of the delivery progress. For example, the customer can check the current location and estimated arrival time of the goods through a smartphone app. The delivery department also provides options for safely storing goods when the customer is absent. For example, goods can be stored in a delivery box or a designated pick-up location, allowing the customer to pick them up at their convenience. In this way, the delivery department can deliver goods quickly and reliably and provide a convenient delivery service to the customer.

[0037] The detection unit monitors the operation of the mobile unit and the manipulator unit. The detection unit can monitor the operation of the mobile unit and the manipulator unit using methods such as cameras, sensors, and log analysis. Specifically, it uses cameras to monitor the direction of movement and surrounding conditions of the mobile unit in real time to prevent collisions with obstacles and people. When using sensors, ultrasonic sensors or LiDAR are used to detect surrounding obstacles and support safe movement. When using log analysis, the operation history of the mobile unit and the manipulator unit is analyzed to detect abnormal behavior and errors. Based on this information, the detection unit controls the operation of the mobile unit and the manipulator unit in real time to achieve safe and efficient operation. For example, if the mobile unit approaches an obstacle, the detection unit automatically stops movement, calculates an avoidance route, and safely resumes movement. Also, if abnormal force is applied when the manipulator unit picks up a product, the detection unit immediately interrupts the operation to prevent damage to the product. In this way, the detection unit can safely and efficiently monitor the operation of the mobile unit and the manipulator unit, improving the reliability of the entire system.

[0038] The reception desk can analyze the buyer's past operation history and suggest the optimal operation method. For example, the reception desk can prioritize suggesting operation methods (voice, text, etc.) that the buyer has frequently used in the past. The reception desk can also display relevant operation options based on the product categories the buyer has previously selected. Furthermore, the reception desk can predict and suggest operation methods to be used at specific times based on the buyer's past operation history. This improves operational efficiency by suggesting the optimal operation method based on past operation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0039] The reception desk can automatically adjust the priority of operations based on the buyer's current purchase intent. For example, if the buyer shows high purchase intent, the reception desk will prioritize displaying key operation options. Conversely, if the buyer shows low purchase intent, the reception desk can simplify the operation procedure and display only basic options. Furthermore, the reception desk can dynamically change the order of operations according to the buyer's purchase intent to provide an optimal user experience. This improves customer satisfaction by adjusting the priority of operations according to purchase intent. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0040] The reception desk can suggest the most suitable supermarket based on the customer's geographical location. For example, the reception desk can automatically suggest the supermarket closest to the customer's current location. It can also prioritize displaying frequently used supermarkets based on the customer's past visit history. Furthermore, the reception desk can suggest the most suitable supermarket considering the inventory status of specific products based on the customer's geographical location. This improves customer convenience by suggesting the most suitable supermarket based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0041] The reception desk can analyze the buyer's social media activity and suggest relevant products. For example, the reception desk can suggest relevant products based on products the buyer has mentioned on social media. It can also analyze the buyer's interests and preferences on social media and suggest products based on that. Furthermore, the reception desk can suggest products related to specific events or seasons based on the buyer's social media activity. This allows the reception desk to provide products that match the buyer's interests by suggesting relevant products based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0042] The mobile unit can analyze congestion levels within the supermarket in real time and select the optimal route. For example, it can suggest a route that avoids crowded areas within the supermarket in real time. The mobile unit can also dynamically change the shortest route according to congestion levels to achieve efficient travel. Furthermore, the mobile unit can suggest the optimal travel time considering the expected congestion times. This enables efficient travel by selecting the optimal route according to congestion levels. Some or all of the above processing in the mobile unit may be performed using AI, for example, or without AI.

[0043] The mobile unit can move efficiently by referring to product placement information. For example, the mobile unit calculates the shortest route based on product placement information and moves efficiently. The mobile unit can also update product placement information in real time and suggest the optimal route. Furthermore, the mobile unit can efficiently select necessary products during movement, taking product placement information into consideration. As a result, travel time is reduced by moving efficiently based on product placement information. Some or all of the above processing in the mobile unit may be performed using AI, for example, or without using AI.

[0044] The mobile unit can select the optimal route to maintain product quality by considering the temperature and humidity within the supermarket. For example, the mobile unit can avoid moving through high-temperature areas to maintain product quality. It can also avoid moving through high-humidity areas to prevent product deterioration. Furthermore, the mobile unit can monitor changes in temperature and humidity in real time and dynamically adjust the optimal route. This ensures that product quality is maintained by selecting the optimal route based on temperature and humidity. Some or all of the above processes in the mobile unit may be performed using AI, for example, or without AI.

[0045] The mobile unit can perform coordinated movements with other robots to achieve efficient movement. For example, the mobile unit can acquire the position information of other robots in real time and select a route to avoid collisions. It can also work with other robots to plan a movement route for efficiently selecting products. Furthermore, the mobile unit can communicate with other robots and move in coordination to achieve efficient work. This enables efficient movement through coordinated movements with other robots. Some or all of the above-described processes in the mobile unit may be performed using AI, for example, or without AI.

[0046] The manipulator unit can automatically select the optimal gripping method according to the shape and weight of the product. For example, the manipulator unit can analyze the shape of the product with a camera and select the optimal gripping method. The manipulator unit can also measure the weight of the product with a sensor and grip it with the appropriate force. Furthermore, the manipulator unit can automatically adjust the position of its fingers according to the shape and weight of the product. This prevents damage to the product by selecting a gripping method appropriate to the shape and weight of the product. Some or all of the above processes in the manipulator unit may be performed using AI, for example, or without using AI.

[0047] The manipulator unit can adjust the amount of force applied to prevent damage to the product during handling. For example, the manipulator unit can analyze the material of the product using a camera and grasp it with the appropriate amount of force. The manipulator unit can also dynamically adjust the amount of force according to the shape and weight of the product. Furthermore, the manipulator unit can monitor the amount of force applied in real time using sensors during handling to prevent damage. In this way, the quality of the product is maintained by adjusting the amount of force applied to prevent damage. Some or all of the above processes in the manipulator unit may be performed using AI, for example, or without using AI.

[0048] The manipulator unit can sense the temperature and humidity of the product and select the optimal handling method. For example, the manipulator unit can measure the temperature of the product with a sensor and select an appropriate handling method. It can also measure the humidity of the product with a sensor and select an appropriate handling method. Furthermore, the manipulator unit can dynamically adjust its operation according to the temperature and humidity of the product. This ensures that the quality of the product is maintained by selecting the optimal handling method based on temperature and humidity. Some or all of the above processing in the manipulator unit may be performed using AI, for example, or without AI.

[0049] The manipulator unit can perform coordinated actions with other robots to achieve efficient work. For example, the manipulator unit can acquire the position information of other robots in real time and pick up products in cooperation with them. The manipulator unit can also plan actions to efficiently select products in cooperation with other robots. Furthermore, the manipulator unit can achieve efficient work by communicating with other robots and working in coordination with them. This enables efficient work through coordinated actions with other robots. Some or all of the above-described processes in the manipulator unit may be performed using AI, for example, or without using AI.

[0050] The camera unit can automatically analyze the condition and expiration date of a product and notify the buyer. For example, the camera unit can take a picture of the product's condition, and the AI ​​can perform image analysis to evaluate the product's freshness. The camera unit can also read the product's expiration date, and the AI ​​can automatically notify the buyer. Furthermore, the camera unit can take a picture of the product's exterior, and the AI ​​can detect scratches or stains and notify the buyer. This allows the buyer to make an appropriate decision by automatically analyzing the product's condition and expiration date. Some or all of the above processes in the camera unit may be performed using AI, for example, or without AI.

[0051] The camera unit can adjust its field of view to capture the overall image of the product. For example, the camera unit can widen its field of view to display the entire product at once. Alternatively, it can narrow its field of view to display a specific part of the product in detail. Furthermore, the camera unit can dynamically adjust its field of view to alternately display the overall image and details of the product. This allows for an appropriate understanding of both the overall image and details of the product by adjusting the field of view. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI.

[0052] The camera unit can analyze the color and shape of a product and evaluate its quality. For example, the camera unit can capture the color of a product with its camera, and the AI ​​can evaluate the vividness of the color. The camera unit can also capture the shape of a product with its camera, and the AI ​​can evaluate the consistency of the shape. Furthermore, the camera unit can capture the appearance of a product with its camera, and the AI ​​can detect abnormalities in color and shape to evaluate its quality. This makes it possible to evaluate the quality of a product by analyzing its color and shape. Some or all of the above processing in the camera unit may be performed using AI, for example, or without using AI.

[0053] The camera unit can stream camera footage in real time and provide it to the buyer. For example, the camera unit can stream camera footage in real time so that the buyer can check the condition of the product. The camera unit can also stream camera footage in real time so that the buyer can remotely select a product. Furthermore, the camera unit can stream camera footage in real time so that the buyer can check the details of the product. This allows the buyer to check the condition of the product by providing video in real time. Some or all of the above processing in the camera unit may be performed using AI, for example, or without using AI.

[0054] The storage unit can automatically select the optimal storage method according to the shape and weight of the product. For example, the storage unit can analyze the shape of the product with a camera and select the optimal storage method. It can also measure the weight of the product with a sensor and store it with the appropriate amount of force. Furthermore, the storage unit can automatically adjust the storage space according to the shape and weight of the product. This prevents damage to the product by selecting a storage method that is appropriate for the shape and weight of the product. Some or all of the above processes in the storage unit may be performed using AI, for example, or without using AI.

[0055] The storage unit can adjust the amount of force applied during storage to prevent damage to the products. For example, the storage unit can analyze the material of the product with a camera and store it with the appropriate amount of force. The storage unit can also dynamically adjust the amount of force according to the shape and weight of the product. Furthermore, the storage unit can monitor the amount of force applied in real time with sensors during storage to prevent damage. In this way, the quality of the products is maintained by adjusting the amount of force applied to prevent damage. Some or all of the above processes in the storage unit may be performed using AI, for example, or without using AI.

[0056] The storage unit can sense the temperature and humidity of the products and select the optimal storage method. For example, the storage unit can measure the temperature of the products with a sensor and select an appropriate storage method. It can also measure the humidity of the products with a sensor and select an appropriate storage method. Furthermore, the storage unit can dynamically adjust the storage space according to the temperature and humidity of the products. This ensures that the quality of the products is maintained by selecting the optimal storage method based on temperature and humidity. Some or all of the above processes in the storage unit may be performed using AI, for example, or without using AI.

[0057] The storage unit can perform coordinated operations with other robots during storage to achieve efficient storage. For example, the storage unit can acquire the location information of other robots in real time and store products in coordination. The storage unit can also plan operations to efficiently store products in cooperation with other robots. Furthermore, the storage unit can achieve efficient storage by communicating with other robots and operating in coordination. This enables efficient storage through coordinated operations with other robots. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without using AI.

[0058] The payment department can analyze past purchase history and select the optimal payment method. For example, the payment department may prioritize suggesting payment methods that the buyer has used in the past. The payment department can also select the optimal payment method based on the buyer's past purchase history. Furthermore, the payment department can predict and suggest payment methods to be used during specific time periods based on the buyer's past purchase history. This improves payment efficiency by selecting the optimal payment method based on past purchase history. Some or all of the above processes in the payment department may be performed using AI, for example, or without AI.

[0059] The payment unit can automatically select the buyer's credit card information and electronic payment system at the time of payment. For example, the payment unit can automatically acquire the buyer's credit card information and process the payment. The payment unit can also automatically select the electronic payment system registered by the buyer and process the payment. Furthermore, the payment unit can automatically select the optimal payment method based on the buyer's past payment history. This allows for smooth payment processing by automatically selecting credit card information and electronic payment systems. Some or all of the above-described processes in the payment unit may be performed using AI, for example, or without the use of AI.

[0060] The payment unit can propose the most suitable payment method based on the buyer's geographical location information at the time of payment. For example, the payment unit can propose the payment method closest to the buyer's current location. The payment unit can also propose payment methods available in a specific region based on the buyer's geographical location information. Furthermore, the payment unit can select the most suitable payment method considering the buyer's geographical location information. This improves the convenience for the buyer by proposing the most suitable payment method based on geographical location information. Some or all of the above processing in the payment unit may be performed using AI, for example, or without using AI.

[0061] The payment department can analyze the buyer's social media activity at the time of payment and suggest relevant payment methods. For example, the payment department can suggest relevant payment methods based on payment methods mentioned by the buyer on social media. The payment department can also analyze the buyer's interests and preferences on social media and suggest payment methods based on that. Furthermore, the payment department can suggest payment methods related to specific events or seasons based on the buyer's social media activity. In this way, by suggesting relevant payment methods based on social media activity, it is possible to provide payment methods that match the buyer's interests. Some or all of the above processing in the payment department may be performed using AI, for example, or not using AI.

[0062] The delivery unit can select the optimal delivery method based on the shape and weight of the product during delivery. For example, the delivery unit can analyze the shape of the product with a camera and select the optimal delivery method. It can also measure the weight of the product with a sensor and deliver it with appropriate force. Furthermore, the delivery unit can automatically adjust the delivery route and delivery method according to the shape and weight of the product. This prevents damage to the product by selecting a delivery method appropriate to the shape and weight of the product. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI.

[0063] The delivery unit can adjust the amount of force applied during delivery to prevent damage to the goods. For example, the delivery unit can analyze the material of the goods using a camera and deliver them with the appropriate amount of force. The delivery unit can also dynamically adjust the amount of force according to the shape and weight of the goods. Furthermore, the delivery unit can monitor the amount of force applied in real time using sensors during delivery to prevent damage. In this way, the quality of the goods is maintained by adjusting the amount of force applied to prevent damage. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without using AI.

[0064] The delivery department can select the optimal delivery route based on the buyer's geographical location information during delivery. For example, the delivery department can select the route closest to the buyer's current location. The delivery department can also select delivery routes available in a specific area based on the buyer's geographical location information. Furthermore, the delivery department can select the optimal delivery route by taking the buyer's geographical location information into consideration. This improves delivery efficiency by selecting the optimal delivery route based on geographical location information. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI.

[0065] The delivery unit can achieve efficient delivery by coordinating with other delivery robots during delivery. For example, the delivery unit can acquire the location information of other delivery robots in real time and perform deliveries in coordination. The delivery unit can also plan routes for efficient delivery in cooperation with other delivery robots. Furthermore, the delivery unit can achieve efficient delivery by communicating with other delivery robots and coordinating their operations. This enables efficient delivery through coordinated operation with other delivery robots. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without using AI.

[0066] The detection unit can analyze the congestion situation inside the supermarket in real time and select the optimal detection method. For example, the detection unit can propose a detection method in real time to avoid crowded areas inside the supermarket. The detection unit can also dynamically change the optimal detection method according to the congestion situation. Furthermore, the detection unit can select the optimal detection method considering the time of day when congestion is expected. This improves safety by selecting the optimal detection method according to the congestion situation. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0067] The detection unit can perform efficient detection by referring to the product placement information when detection occurs. For example, the detection unit can select the optimal detection method based on the product placement information. The detection unit can also update the product placement information in real time to perform efficient detection. Furthermore, the detection unit can dynamically adjust the detection accuracy by taking the product placement information into consideration. This improves safety by performing efficient detection based on product placement information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0068] The detection unit can select the optimal detection method considering the temperature and humidity inside the supermarket. For example, the detection unit can select a detection method to avoid areas with high temperatures. It can also select a detection method to avoid areas with high humidity. Furthermore, the detection unit can monitor changes in temperature and humidity in real time and dynamically adjust the optimal detection method. This improves safety by selecting the optimal detection method based on temperature and humidity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0069] The detection unit can perform coordinated operations with other robots during detection to achieve efficient detection. For example, the detection unit can acquire the position information of other robots in real time and perform coordinated detection. The detection unit can also plan methods for efficient detection in cooperation with other robots. Furthermore, the detection unit can achieve efficient detection by communicating with other robots and operating in coordination. This enables efficient detection through coordinated operations with other robots. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without using AI.

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

[0071] The manipulator unit can automatically select the optimal gripping method according to the shape and weight of the product. For example, it can analyze the shape of the product with a camera and select the optimal gripping method. It can also measure the weight of the product with a sensor and grip it with the appropriate force. Furthermore, it can automatically adjust the position of the manipulator's fingers according to the shape and weight of the product. By selecting a gripping method according to the shape and weight of the product, damage to the product can be prevented. Some or all of the above processes in the manipulator unit may be performed using AI, for example, or without the use of AI.

[0072] The camera unit can automatically analyze the condition and expiration date of a product and notify the buyer. For example, the camera can photograph the condition of the product, and the AI ​​can perform image analysis to evaluate the product's freshness. The camera can also read the product's expiration date, and the AI ​​can automatically notify the buyer. Furthermore, the camera can photograph the product's exterior, and the AI ​​can detect scratches or stains and notify the buyer. This allows the buyer to make an appropriate decision by automatically analyzing the condition and expiration date of the product. Some or all of the above-described processes in the camera unit may be performed using AI, for example, or without AI.

[0073] The detection unit can analyze the congestion situation within the supermarket in real time and select the optimal detection method. For example, it can propose a detection method in real time to avoid crowded areas within the supermarket. It can also dynamically change the optimal detection method according to the congestion situation. Furthermore, it can select the optimal detection method considering the time of day when congestion is expected. By selecting the optimal detection method according to the congestion situation, safety is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0074] The mobile unit can select the optimal route to maintain product quality by considering the temperature and humidity within the supermarket. For example, it can avoid moving through high-temperature areas to maintain product quality. It can also avoid moving through high-humidity areas to prevent product deterioration. Furthermore, it can monitor changes in temperature and humidity in real time and dynamically adjust the optimal route. This ensures that product quality is maintained by selecting the optimal route based on temperature and humidity. Some or all of the above processing in the mobile unit may be performed using AI, for example, or without AI.

[0075] The manipulator unit can sense the temperature and humidity of the product and select the optimal handling method. For example, it can measure the product's temperature with a sensor and select an appropriate handling method. It can also measure the product's humidity with a sensor and select an appropriate handling method. Furthermore, it can dynamically adjust the manipulator's operation according to the product's temperature and humidity. This ensures that the product's quality is maintained by selecting the optimal handling method based on temperature and humidity. Some or all of the above-described processes in the manipulator unit may be performed using AI, for example, or without AI.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The reception desk accepts online requests from the purchaser. The reception desk can accept requests via, for example, a web browser, a mobile app, or voice commands. Step 2: The mobile unit moves around the supermarket based on the information received by the reception unit. The mobile unit can move by means of, for example, wheels, tracks, or by flight. Step 3: The manipulator unit picks up the product at the position moved by the moving unit. The manipulator unit can pick up the product using, for example, a gripper, a suction pad, etc. Step 4: The camera unit checks the condition and expiration date of the product picked up by the manipulator unit. The camera unit can check the condition and expiration date of the product using methods such as image analysis or barcode scanning. Step 5: The storage unit places the items into the basket based on the product information confirmed by the camera unit. The storage unit can place items into the basket using methods such as alignment and pressure. Step 6: The payment unit automatically processes payment for the items placed in the basket by the storage unit. The payment unit can automatically process payment for items using methods such as credit cards, electronic money, or QR codes. Step 7: The delivery department delivers the goods, which have been paid for by the payment department, to the customer's home. The delivery department can deliver the goods to the customer's home using methods such as drones, robotic cars, or courier services. Step 8: The detection unit monitors the operation of the mobile unit and the manipulator unit. The detection unit can monitor the operation of the mobile unit and the manipulator unit by methods such as cameras, sensors, and log analysis.

[0078] (Example of form 2) The system providing a new shopping experience according to an embodiment of the present invention is a system in which a shopper accesses a supermarket online and operates a robot to automatically select, purchase, and deliver products within the supermarket. The system provides a new shopping experience in which the shopper accesses a supermarket online and operates a robot. The robot is autonomous and can move freely within the supermarket. The robot is equipped with a manipulator for picking up products and a camera for checking the condition and expiration date of the products. The shopper can check the condition of the products through the camera and decide whether to purchase them. Next, the robot places the products selected by the shopper into a basket. The products in the basket are automatically paid for. Payment is made using the shopper's credit card information or an electronic payment system. Furthermore, the products selected by the shopper are delivered to their home by an autonomous truck. The autonomous truck automatically calculates the route from the supermarket to the shopper's home and delivers safely. This eliminates the need for the shopper to go to the supermarket. In addition, a detection system is constantly operating to prevent the robot from hitting people or obstacles while moving around the supermarket or operating the manipulator. The detection system monitors the robot to prevent contact with people or obstacles, ensuring safe operation. This system allows shoppers to enjoy grocery shopping from the comfort of their homes. It is particularly convenient for busy people and those with mobility issues, offering a new shopping experience. This system allows shoppers to operate online, select items within the supermarket, purchase them, and have them delivered automatically.

[0079] The system providing a new shopping experience according to this embodiment comprises a reception unit, a mobile unit, a manipulator unit, a camera unit, a storage unit, a payment unit, a delivery unit, and a detection unit. The reception unit receives online operations from the purchaser. The reception unit can receive operations through, for example, a web browser, a mobile app, or voice commands. The mobile unit moves around the supermarket based on the information received by the reception unit. The mobile unit can move by, for example, wheels, caterpillar tracks, or by flight. The manipulator unit picks up products at the location moved by the mobile unit. The manipulator unit can pick up products using, for example, a gripper or suction pad. The camera unit checks the condition and expiration date of the products picked up by the manipulator unit. The camera unit can check the condition and expiration date of products by, for example, image analysis or barcode scanning. The storage unit places products into the basket based on the product information confirmed by the camera unit. The storage unit can place products into the basket by, for example, alignment or force adjustment. The payment unit automatically processes payment for items placed in a basket by the storage unit. The payment unit can automatically process payment for items using methods such as credit cards, electronic money, or QR codes. The delivery unit delivers the items paid for by the payment unit to the customer's home. The delivery unit can deliver items to the customer's home using methods such as drones, robotic cars, or courier services. The detection unit monitors the operation of the mobile unit and the manipulator unit. The detection unit can monitor the operation of the mobile unit and the manipulator unit using methods such as cameras, sensors, or log analysis. As a result, the system, which provides a new shopping experience according to the embodiment, allows the customer to operate online, select items in the supermarket, purchase them, and have them delivered automatically.

[0080] The reception desk receives online requests from customers. These requests can be received via various methods, such as web browsers, mobile apps, and voice commands. Specifically, customers can browse product lists and select desired items through web browsers or mobile apps. When using voice commands, customers can specify product names and quantities using voice recognition technology. The reception desk receives these requests in real time and relays customer requests throughout the system. Furthermore, the reception desk manages customer account information and past purchase history, enabling it to provide personalized product suggestions and promotions. For example, it can suggest related and new products based on past and frequently purchased items. The reception desk also handles customer inquiries and support requests, responding quickly through chatbots and customer support. This allows the reception desk to provide customers with a smooth and personalized shopping experience.

[0081] The mobile unit moves within the supermarket based on information received by the reception unit. The mobile unit can move using methods such as wheels, tracks, or flight. Specifically, a wheeled mobile robot can smoothly move through the aisles of the supermarket and reach the location of designated products. Using tracks improves the ability to overcome steps and obstacles, enabling movement in a wider variety of environments. When using flight, a drone can move through the air and quickly access product shelves. The mobile unit calculates the optimal route based on the supermarket's map information and product placement information, and moves efficiently. Furthermore, the mobile unit can use sensors and cameras to understand its surroundings in real time and move safely while avoiding obstacles. For example, ultrasonic sensors and LiDAR can be used to detect surrounding obstacles and people, and avoid collisions. The mobile unit is also equipped with a battery management system and can return to a charging station at the appropriate time to enable long operating hours. As a result, the mobile unit can move efficiently and safely within the supermarket and support the quick retrieval of products.

[0082] The manipulator unit picks up the product at a position moved by the mobile unit. The manipulator unit can pick up products using, for example, a gripper or suction pad. Specifically, the gripper can grasp the product with appropriate force according to its shape and size. The suction pad uses vacuum suction to firmly lift products with smooth surfaces. The manipulator unit has arms with multiple degrees of freedom, allowing it to accurately pick up products while adjusting their position and orientation. Furthermore, the manipulator unit is equipped with force sensors and tactile sensors to sense the weight and hardness of the product and handle it with appropriate force. For example, when handling fragile products or containers containing liquids, it adjusts the force to lift them safely. In addition, the manipulator unit can use AI-based image recognition technology to identify the shape and label of products and accurately select them. As a result, the manipulator unit can efficiently and safely pick up products of various shapes and sizes.

[0083] The camera unit checks the condition and expiration date of the product picked up by the manipulator unit. The camera unit can check the condition and expiration date of the product using methods such as image analysis and barcode scanning. Specifically, the camera unit uses a high-resolution camera to photograph the surface of the product and uses image analysis technology to detect the presence of scratches or stains. When using barcode scanning, it reads the product's barcode and obtains information such as the expiration date and manufacturing date. Furthermore, the camera unit can utilize AI-based image recognition technology to identify the product's label and packaging design and obtain accurate product information. For example, the AI ​​recognizes the letters and numbers written on the product label and extracts the expiration date and manufacturing date. The camera unit can also analyze the color and shape of the product and evaluate its quality. As a result, the camera unit can accurately check the condition and expiration date of the product and provide high-quality products.

[0084] The storage unit places items into the basket based on information about the items confirmed by the camera unit. The storage unit can place items into the basket using methods such as positioning and force adjustment. Specifically, the storage unit positions items appropriately according to their shape and size, and adjusts the force applied to place them into the basket. For example, when handling fragile items or containers containing liquids, it adjusts the force to safely place them into the basket. The storage unit is equipped with arms that have multiple degrees of freedom, allowing it to accurately place items into the basket while adjusting their position and orientation. Furthermore, the storage unit is equipped with force sensors and tactile sensors, which sense the weight and hardness of items, enabling it to handle items with the appropriate force. As a result, the storage unit can efficiently and safely place items of various shapes and sizes into the basket.

[0085] The payment unit automatically processes payments for items placed in the shopping cart by the storage unit. The payment unit can automatically process payments using methods such as credit cards, electronic money, and QR codes. Specifically, it retrieves credit card and electronic money information based on the buyer's account information, calculates the total amount of goods, and processes the payment. When using QR codes, the buyer can complete the payment by scanning the QR code with their smartphone. The payment unit has enhanced security measures to safely manage buyers' personal and payment information. For example, it uses encryption technology to protect data and prevent unauthorized access and information leaks. Furthermore, the payment unit can confirm payments, issue receipts, and manage purchase history. This allows the payment unit to process payments quickly and securely, providing buyers with a smooth shopping experience.

[0086] The delivery department delivers goods that have been paid for by the payment department to the customer's home. The delivery department can deliver goods to the customer's home using methods such as drones, robotic cars, and courier services. Specifically, when using drones, the appropriate drone is selected according to the weight and size of the goods, and it automatically flies to the customer's address using GPS. When using robotic cars, the delivery route is calculated and safe delivery is carried out using autonomous driving technology. When using courier services, goods are delivered quickly in cooperation with the delivery company. The delivery department can track the delivery status in real time and notify the customer of the delivery progress. For example, the customer can check the current location and estimated arrival time of the goods through a smartphone app. The delivery department also provides options for safely storing goods when the customer is absent. For example, goods can be stored in a delivery box or a designated pick-up location, allowing the customer to pick them up at their convenience. In this way, the delivery department can deliver goods quickly and reliably and provide a convenient delivery service to the customer.

[0087] The detection unit monitors the operation of the mobile unit and the manipulator unit. The detection unit can monitor the operation of the mobile unit and the manipulator unit using methods such as cameras, sensors, and log analysis. Specifically, it uses cameras to monitor the direction of movement and surrounding conditions of the mobile unit in real time to prevent collisions with obstacles and people. When using sensors, ultrasonic sensors or LiDAR are used to detect surrounding obstacles and support safe movement. When using log analysis, the operation history of the mobile unit and the manipulator unit is analyzed to detect abnormal behavior and errors. Based on this information, the detection unit controls the operation of the mobile unit and the manipulator unit in real time to achieve safe and efficient operation. For example, if the mobile unit approaches an obstacle, the detection unit automatically stops movement, calculates an avoidance route, and safely resumes movement. Also, if abnormal force is applied when the manipulator unit picks up a product, the detection unit immediately interrupts the operation to prevent damage to the product. In this way, the detection unit can safely and efficiently monitor the operation of the mobile unit and the manipulator unit, improving the reliability of the entire system.

[0088] The reception desk can estimate the buyer's emotions and adjust the online operation interface based on the estimated emotions. For example, if the buyer is stressed, the reception desk can provide a simple interface and minimize the number of steps. If the buyer is relaxed, the reception desk can also provide detailed operation options and suggest customizable methods. Furthermore, if the buyer is in a hurry, the reception desk can prioritize voice input to allow for quick completion of the operation. This improves the user experience by providing an interface that responds to the buyer's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The reception desk can analyze the buyer's past operation history and suggest the optimal operation method. For example, the reception desk can prioritize suggesting operation methods (voice, text, etc.) that the buyer has frequently used in the past. The reception desk can also display relevant operation options based on the product categories the buyer has previously selected. Furthermore, the reception desk can predict and suggest operation methods to be used at specific times based on the buyer's past operation history. This improves operational efficiency by suggesting the optimal operation method based on past operation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0090] The reception desk can automatically adjust the priority of operations based on the buyer's current purchase intent. For example, if the buyer shows high purchase intent, the reception desk will prioritize displaying key operation options. Conversely, if the buyer shows low purchase intent, the reception desk can simplify the operation procedure and display only basic options. Furthermore, the reception desk can dynamically change the order of operations according to the buyer's purchase intent to provide an optimal user experience. This improves customer satisfaction by adjusting the priority of operations according to purchase intent. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0091] The reception desk can estimate the buyer's emotions and adjust the difficulty of the operation based on the estimated emotions. For example, if the buyer is nervous, the reception desk can provide simple instructions to reduce stress. If the buyer is relaxed, the reception desk can also provide detailed operation options to increase the freedom of operation. Furthermore, if the buyer is in a hurry, the reception desk can provide the shortest possible procedure to allow for quick completion. This improves the user experience by adjusting the difficulty of the operation according to emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The reception desk can suggest the most suitable supermarket based on the customer's geographical location. For example, the reception desk can automatically suggest the supermarket closest to the customer's current location. It can also prioritize displaying frequently used supermarkets based on the customer's past visit history. Furthermore, the reception desk can suggest the most suitable supermarket considering the inventory status of specific products based on the customer's geographical location. This improves customer convenience by suggesting the most suitable supermarket based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0093] The reception desk can analyze the buyer's social media activity and suggest relevant products. For example, the reception desk can suggest relevant products based on products the buyer has mentioned on social media. It can also analyze the buyer's interests and preferences on social media and suggest products based on that. Furthermore, the reception desk can suggest products related to specific events or seasons based on the buyer's social media activity. This allows the reception desk to provide products that match the buyer's interests by suggesting relevant products based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0094] The mobile unit can estimate the buyer's emotions and adjust its movement speed based on the estimated emotions. For example, if the buyer is in a hurry, the mobile unit will increase its movement speed to quickly select products. Conversely, if the buyer is relaxed, the mobile unit can slow down its movement speed to allow the buyer to select products at a leisurely pace. Furthermore, if the buyer is stressed, the mobile unit can adjust its movement speed appropriately to alleviate stress. By adjusting the movement speed according to emotions, the buyer's satisfaction is improved. 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.

[0095] The mobile unit can analyze congestion levels within the supermarket in real time and select the optimal route. For example, it can suggest a route that avoids crowded areas within the supermarket in real time. The mobile unit can also dynamically change the shortest route according to congestion levels to achieve efficient travel. Furthermore, the mobile unit can suggest the optimal travel time considering the expected congestion times. This enables efficient travel by selecting the optimal route according to congestion levels. Some or all of the above processing in the mobile unit may be performed using AI, for example, or without AI.

[0096] The mobile unit can move efficiently by referring to product placement information. For example, the mobile unit calculates the shortest route based on product placement information and moves efficiently. The mobile unit can also update product placement information in real time and suggest the optimal route. Furthermore, the mobile unit can efficiently select necessary products during movement, taking product placement information into consideration. As a result, travel time is reduced by moving efficiently based on product placement information. Some or all of the above processing in the mobile unit may be performed using AI, for example, or without using AI.

[0097] The travel unit can estimate the buyer's emotions and adjust the travel route based on those emotions. For example, if the buyer is in a hurry, the travel unit will prioritize suggesting the shortest route. If the buyer is relaxed, it can also suggest a scenic route. Furthermore, if the buyer is stressed, it can suggest a route that avoids congestion. By adjusting the travel route according to emotions, customer satisfaction is improved. Emotion estimation is achieved using emotion estimation functions, 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.

[0098] The mobile unit can select the optimal route to maintain product quality by considering the temperature and humidity within the supermarket. For example, the mobile unit can avoid moving through high-temperature areas to maintain product quality. It can also avoid moving through high-humidity areas to prevent product deterioration. Furthermore, the mobile unit can monitor changes in temperature and humidity in real time and dynamically adjust the optimal route. This ensures that product quality is maintained by selecting the optimal route based on temperature and humidity. Some or all of the above processes in the mobile unit may be performed using AI, for example, or without AI.

[0099] The mobile unit can perform coordinated movements with other robots to achieve efficient movement. For example, the mobile unit can acquire the position information of other robots in real time and select a route to avoid collisions. It can also work with other robots to plan a movement route for efficiently selecting products. Furthermore, the mobile unit can communicate with other robots and move in coordination to achieve efficient work. This enables efficient movement through coordinated movements with other robots. Some or all of the above-described processes in the mobile unit may be performed using AI, for example, or without AI.

[0100] The manipulator unit can estimate the buyer's emotions and adjust its operating speed based on the estimated emotions. For example, if the buyer is in a hurry, the manipulator unit can increase its operating speed to quickly pick up the product. Conversely, if the buyer is relaxed, the manipulator unit can slow down its operating speed to carefully pick up the product. Furthermore, if the buyer is stressed, the manipulator unit can adjust its operating speed appropriately to reduce stress. By adjusting the operating speed according to emotions, the buyer's satisfaction is improved. 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.

[0101] The manipulator unit can automatically select the optimal gripping method according to the shape and weight of the product. For example, the manipulator unit can analyze the shape of the product with a camera and select the optimal gripping method. The manipulator unit can also measure the weight of the product with a sensor and grip it with the appropriate force. Furthermore, the manipulator unit can automatically adjust the position of its fingers according to the shape and weight of the product. This prevents damage to the product by selecting a gripping method appropriate to the shape and weight of the product. Some or all of the above processes in the manipulator unit may be performed using AI, for example, or without using AI.

[0102] The manipulator unit can adjust the amount of force applied to prevent damage to the product during handling. For example, the manipulator unit can analyze the material of the product using a camera and grasp it with the appropriate amount of force. The manipulator unit can also dynamically adjust the amount of force according to the shape and weight of the product. Furthermore, the manipulator unit can monitor the amount of force applied in real time using sensors during handling to prevent damage. In this way, the quality of the product is maintained by adjusting the amount of force applied to prevent damage. Some or all of the above processes in the manipulator unit may be performed using AI, for example, or without using AI.

[0103] The manipulator unit can estimate the buyer's emotions and adjust its range of motion based on the estimated emotions. For example, if the buyer is in a hurry, the manipulator unit can widen its range of motion to quickly pick up the product. Conversely, if the buyer is relaxed, the manipulator unit can narrow its range of motion to carefully pick up the product. Furthermore, if the buyer is stressed, the manipulator unit can adjust its range of motion appropriately to reduce stress. By adjusting the range of motion according to emotions, the buyer's satisfaction is improved. 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.

[0104] The manipulator unit can sense the temperature and humidity of the product and select the optimal handling method. For example, the manipulator unit can measure the temperature of the product with a sensor and select an appropriate handling method. It can also measure the humidity of the product with a sensor and select an appropriate handling method. Furthermore, the manipulator unit can dynamically adjust its operation according to the temperature and humidity of the product. This ensures that the quality of the product is maintained by selecting the optimal handling method based on temperature and humidity. Some or all of the above processing in the manipulator unit may be performed using AI, for example, or without AI.

[0105] The manipulator unit can perform coordinated actions with other robots to achieve efficient work. For example, the manipulator unit can acquire the position information of other robots in real time and pick up products in cooperation with them. The manipulator unit can also plan actions to efficiently select products in cooperation with other robots. Furthermore, the manipulator unit can achieve efficient work by communicating with other robots and working in coordination with them. This enables efficient work through coordinated actions with other robots. Some or all of the above-described processes in the manipulator unit may be performed using AI, for example, or without using AI.

[0106] The camera unit can estimate the buyer's emotions and adjust the camera's zoom level based on the estimated emotions. For example, if the buyer is seeking detailed information, the camera unit can increase the zoom level to display the product's details. Conversely, if the buyer wants to see the whole picture, the camera unit can decrease the zoom level to display the entire product. Furthermore, if the buyer is in a hurry, the camera unit can quickly provide product information at an appropriate zoom level. By adjusting the zoom level according to emotions, this improves buyer satisfaction. Emotion estimation is achieved using emotion estimation functions, 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.

[0107] The camera unit can automatically analyze the condition and expiration date of a product and notify the buyer. For example, the camera unit can take a picture of the product's condition, and the AI ​​can perform image analysis to evaluate the product's freshness. The camera unit can also read the product's expiration date, and the AI ​​can automatically notify the buyer. Furthermore, the camera unit can take a picture of the product's exterior, and the AI ​​can detect scratches or stains and notify the buyer. This allows the buyer to make an appropriate decision by automatically analyzing the product's condition and expiration date. Some or all of the above processes in the camera unit may be performed using AI, for example, or without AI.

[0108] The camera unit can adjust its field of view to capture the overall image of the product. For example, the camera unit can widen its field of view to display the entire product at once. Alternatively, it can narrow its field of view to display a specific part of the product in detail. Furthermore, the camera unit can dynamically adjust its field of view to alternately display the overall image and details of the product. This allows for an appropriate understanding of both the overall image and details of the product by adjusting the field of view. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI.

[0109] The camera unit can estimate the buyer's emotions and adjust the camera resolution based on the estimated emotions. For example, if the buyer is seeking detailed information, the camera unit can increase the resolution to provide a high-definition image. Alternatively, if the buyer wants to grasp the overall picture, the camera unit can decrease the resolution to provide a wider-angle image. Furthermore, if the buyer is in a hurry, the camera unit can quickly provide an image at a suitable resolution. This adjustment of resolution according to emotions improves customer satisfaction. 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.

[0110] The camera unit can analyze the color and shape of a product and evaluate its quality. For example, the camera unit can capture the color of a product with its camera, and the AI ​​can evaluate the vividness of the color. The camera unit can also capture the shape of a product with its camera, and the AI ​​can evaluate the consistency of the shape. Furthermore, the camera unit can capture the appearance of a product with its camera, and the AI ​​can detect abnormalities in color and shape to evaluate its quality. This makes it possible to evaluate the quality of a product by analyzing its color and shape. Some or all of the above processing in the camera unit may be performed using AI, for example, or without using AI.

[0111] The camera unit can stream camera footage in real time and provide it to the buyer. For example, the camera unit can stream camera footage in real time so that the buyer can check the condition of the product. The camera unit can also stream camera footage in real time so that the buyer can remotely select a product. Furthermore, the camera unit can stream camera footage in real time so that the buyer can check the details of the product. This allows the buyer to check the condition of the product by providing video in real time. Some or all of the above processing in the camera unit may be performed using AI, for example, or without using AI.

[0112] The storage unit can estimate the buyer's emotions and adjust the storage method based on those emotions. For example, if the buyer is in a hurry, the storage unit can select a method for quickly storing the items. If the buyer is relaxed, the storage unit can select a method for carefully storing the items. Furthermore, if the buyer is stressed, the storage unit can select a method to reduce stress. By adjusting the storage method according to emotions, customer satisfaction is improved. 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.

[0113] The storage unit can automatically select the optimal storage method according to the shape and weight of the product. For example, the storage unit can analyze the shape of the product with a camera and select the optimal storage method. It can also measure the weight of the product with a sensor and store it with the appropriate amount of force. Furthermore, the storage unit can automatically adjust the storage space according to the shape and weight of the product. This prevents damage to the product by selecting a storage method that is appropriate for the shape and weight of the product. Some or all of the above processes in the storage unit may be performed using AI, for example, or without using AI.

[0114] The storage unit can adjust the amount of force applied during storage to prevent damage to the products. For example, the storage unit can analyze the material of the product with a camera and store it with the appropriate amount of force. The storage unit can also dynamically adjust the amount of force according to the shape and weight of the product. Furthermore, the storage unit can monitor the amount of force applied in real time with sensors during storage to prevent damage. In this way, the quality of the products is maintained by adjusting the amount of force applied to prevent damage. Some or all of the above processes in the storage unit may be performed using AI, for example, or without using AI.

[0115] The storage unit can estimate the buyer's emotions and adjust the storage order based on those emotions. For example, if the buyer is in a hurry, the unit will prioritize storing important items. If the buyer is relaxed, the unit can carefully arrange and store items. Furthermore, if the buyer is stressed, the unit can store items in an order that reduces stress. By adjusting the storage order according to emotions, customer satisfaction is improved. 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.

[0116] The storage unit can sense the temperature and humidity of the products and select the optimal storage method. For example, the storage unit can measure the temperature of the products with a sensor and select an appropriate storage method. It can also measure the humidity of the products with a sensor and select an appropriate storage method. Furthermore, the storage unit can dynamically adjust the storage space according to the temperature and humidity of the products. This ensures that the quality of the products is maintained by selecting the optimal storage method based on temperature and humidity. Some or all of the above processes in the storage unit may be performed using AI, for example, or without using AI.

[0117] The storage unit can perform coordinated operations with other robots during storage to achieve efficient storage. For example, the storage unit can acquire the location information of other robots in real time and store products in coordination. The storage unit can also plan operations to efficiently store products in cooperation with other robots. Furthermore, the storage unit can achieve efficient storage by communicating with other robots and operating in coordination. This enables efficient storage through coordinated operations with other robots. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without using AI.

[0118] The payment unit can estimate the buyer's emotions and suggest payment methods based on those emotions. For example, if the buyer is in a hurry, the payment unit can suggest a quick payment method. If the buyer is relaxed, the payment unit can also offer detailed payment options. Furthermore, if the buyer is stressed, the payment unit can suggest payment methods to reduce stress. By suggesting payment methods that are appropriate to the buyer's emotions, customer satisfaction is improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The payment department can analyze past purchase history and select the optimal payment method. For example, the payment department may prioritize suggesting payment methods that the buyer has used in the past. The payment department can also select the optimal payment method based on the buyer's past purchase history. Furthermore, the payment department can predict and suggest payment methods to be used during specific time periods based on the buyer's past purchase history. This improves payment efficiency by selecting the optimal payment method based on past purchase history. Some or all of the above processes in the payment department may be performed using AI, for example, or without AI.

[0120] The payment unit can automatically select the buyer's credit card information and electronic payment system at the time of payment. For example, the payment unit can automatically acquire the buyer's credit card information and process the payment. The payment unit can also automatically select the electronic payment system registered by the buyer and process the payment. Furthermore, the payment unit can automatically select the optimal payment method based on the buyer's past payment history. This allows for smooth payment processing by automatically selecting credit card information and electronic payment systems. Some or all of the above-described processes in the payment unit may be performed using AI, for example, or without the use of AI.

[0121] The payment processing unit can estimate the buyer's emotions and determine payment priorities based on those emotions. For example, if the buyer is in a hurry, the unit can prioritize completing the payment quickly. It can also prioritize providing detailed payment options if the buyer is relaxed. Furthermore, if the buyer is stressed, the unit can prioritize stress reduction. This improves customer satisfaction by prioritizing payments according to emotions. Emotion estimation is achieved using emotion estimation functions, 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.

[0122] The payment unit can propose the most suitable payment method based on the buyer's geographical location information at the time of payment. For example, the payment unit can propose the payment method closest to the buyer's current location. The payment unit can also propose payment methods available in a specific region based on the buyer's geographical location information. Furthermore, the payment unit can select the most suitable payment method considering the buyer's geographical location information. This improves the convenience for the buyer by proposing the most suitable payment method based on geographical location information. Some or all of the above processing in the payment unit may be performed using AI, for example, or without using AI.

[0123] The payment department can analyze the buyer's social media activity at the time of payment and suggest relevant payment methods. For example, the payment department can suggest relevant payment methods based on payment methods mentioned by the buyer on social media. The payment department can also analyze the buyer's interests and preferences on social media and suggest payment methods based on that. Furthermore, the payment department can suggest payment methods related to specific events or seasons based on the buyer's social media activity. In this way, by suggesting relevant payment methods based on social media activity, it is possible to provide payment methods that match the buyer's interests. Some or all of the above processing in the payment department may be performed using AI, for example, or not using AI.

[0124] The delivery department can estimate the customer's emotions and adjust the delivery route based on those emotions. For example, if the customer is in a hurry, the delivery department will prioritize the shortest route. If the customer is relaxed, the delivery department can select a route with good scenery. Furthermore, if the customer is stressed, the delivery department can select a route that avoids congestion. By adjusting the delivery route according to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The delivery unit can select the optimal delivery method based on the shape and weight of the product during delivery. For example, the delivery unit can analyze the shape of the product with a camera and select the optimal delivery method. It can also measure the weight of the product with a sensor and deliver it with appropriate force. Furthermore, the delivery unit can automatically adjust the delivery route and delivery method according to the shape and weight of the product. This prevents damage to the product by selecting a delivery method appropriate to the shape and weight of the product. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI.

[0126] The delivery unit can adjust the amount of force applied during delivery to prevent damage to the goods. For example, the delivery unit can analyze the material of the goods using a camera and deliver them with the appropriate amount of force. The delivery unit can also dynamically adjust the amount of force according to the shape and weight of the goods. Furthermore, the delivery unit can monitor the amount of force applied in real time using sensors during delivery to prevent damage. In this way, the quality of the goods is maintained by adjusting the amount of force applied to prevent damage. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without using AI.

[0127] The delivery department can estimate the customer's emotions and prioritize deliveries based on those emotions. For example, if the customer is in a hurry, the delivery department can prioritize quick delivery. If the customer is relaxed, the delivery department can prioritize providing detailed delivery options. Furthermore, if the customer is stressed, the delivery department can prioritize stress reduction. This improves customer satisfaction by prioritizing deliveries according to emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The delivery department can select the optimal delivery route based on the buyer's geographical location information during delivery. For example, the delivery department can select the route closest to the buyer's current location. The delivery department can also select delivery routes available in a specific area based on the buyer's geographical location information. Furthermore, the delivery department can select the optimal delivery route by taking the buyer's geographical location information into consideration. This improves delivery efficiency by selecting the optimal delivery route based on geographical location information. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI.

[0129] The delivery unit can achieve efficient delivery by coordinating with other delivery robots during delivery. For example, the delivery unit can acquire the location information of other delivery robots in real time and perform deliveries in coordination. The delivery unit can also plan routes for efficient delivery in cooperation with other delivery robots. Furthermore, the delivery unit can achieve efficient delivery by communicating with other delivery robots and coordinating their operations. This enables efficient delivery through coordinated operation with other delivery robots. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without using AI.

[0130] The detection unit can estimate the buyer's emotions and adjust the detection accuracy based on the estimated emotions. For example, if the buyer is nervous, the detection unit can increase the detection accuracy to ensure safety. It can also adjust the detection accuracy appropriately if the buyer is relaxed. Furthermore, if the buyer is in a hurry, the detection unit can set the accuracy to perform detection quickly. This improves safety by adjusting the detection accuracy according to 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.

[0131] The detection unit can analyze the congestion situation inside the supermarket in real time and select the optimal detection method. For example, the detection unit can propose a detection method in real time to avoid crowded areas inside the supermarket. The detection unit can also dynamically change the optimal detection method according to the congestion situation. Furthermore, the detection unit can select the optimal detection method considering the time of day when congestion is expected. This improves safety by selecting the optimal detection method according to the congestion situation. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0132] The detection unit can perform efficient detection by referring to the product placement information when detection occurs. For example, the detection unit can select the optimal detection method based on the product placement information. The detection unit can also update the product placement information in real time to perform efficient detection. Furthermore, the detection unit can dynamically adjust the detection accuracy by taking the product placement information into consideration. This improves safety by performing efficient detection based on product placement information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0133] The detection unit can estimate the buyer's emotions and adjust the detection range based on the estimated emotions. For example, if the buyer is tense, the detection unit can widen the detection range to ensure safety. It can also appropriately adjust the detection range if the buyer is relaxed. Furthermore, if the buyer is in a hurry, the detection unit can set a range for rapid detection. This improves safety by adjusting the detection range according to emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0134] The detection unit can select the optimal detection method considering the temperature and humidity inside the supermarket. For example, the detection unit can select a detection method to avoid areas with high temperatures. It can also select a detection method to avoid areas with high humidity. Furthermore, the detection unit can monitor changes in temperature and humidity in real time and dynamically adjust the optimal detection method. This improves safety by selecting the optimal detection method based on temperature and humidity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0135] The detection unit can perform coordinated operations with other robots during detection to achieve efficient detection. For example, the detection unit can acquire the position information of other robots in real time and perform coordinated detection. The detection unit can also plan methods for efficient detection in cooperation with other robots. Furthermore, the detection unit can achieve efficient detection by communicating with other robots and operating in coordination. This enables efficient detection through coordinated operations with other robots. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without using AI.

[0136] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0137] The reception desk can estimate the customer's emotions and adjust its response speed based on those emotions. For example, if the customer is in a hurry, the reception desk will respond quickly to ensure a smooth transaction. If the customer is relaxed, the reception desk can provide detailed explanations, allowing the customer to make choices at their own pace. Furthermore, if the customer is stressed, the reception desk can provide concise responses to alleviate their stress. By adjusting the response speed according to emotions, customer satisfaction is improved. Emotion estimation can be achieved using, for example, an emotion engine or generative AI.

[0138] The mobile unit can estimate the customer's emotions and select a route based on those emotions. For example, if the customer is in a hurry, the unit will select the shortest route to quickly choose products. If the customer is relaxed, the unit will select a route with good scenery, allowing the customer to enjoy choosing products. Furthermore, if the customer is stressed, the unit can select a route that avoids crowds to reduce stress. By selecting a route that matches the customer's emotions, customer satisfaction is improved. Emotion estimation can be achieved using, for example, an emotion engine or generative AI.

[0139] The manipulator unit can automatically select the optimal gripping method according to the shape and weight of the product. For example, it can analyze the shape of the product with a camera and select the optimal gripping method. It can also measure the weight of the product with a sensor and grip it with the appropriate force. Furthermore, it can automatically adjust the position of the manipulator's fingers according to the shape and weight of the product. By selecting a gripping method according to the shape and weight of the product, damage to the product can be prevented. Some or all of the above processes in the manipulator unit may be performed using AI, for example, or without the use of AI.

[0140] The camera unit can automatically analyze the condition and expiration date of a product and notify the buyer. For example, the camera can photograph the condition of the product, and the AI ​​can perform image analysis to evaluate the product's freshness. The camera can also read the product's expiration date, and the AI ​​can automatically notify the buyer. Furthermore, the camera can photograph the product's exterior, and the AI ​​can detect scratches or stains and notify the buyer. This allows the buyer to make an appropriate decision by automatically analyzing the condition and expiration date of the product. Some or all of the above-described processes in the camera unit may be performed using AI, for example, or without AI.

[0141] The storage unit can estimate the buyer's emotions and adjust the storage method based on those emotions. For example, if the buyer is in a hurry, it can select a method for quickly storing the items. If the buyer is relaxed, it can select a method for carefully storing the items. Furthermore, if the buyer is stressed, it can select a storage method to reduce stress. By adjusting the storage method according to the buyer's emotions, customer satisfaction is improved. Emotion estimation is achieved using, for example, an emotion engine or generative AI.

[0142] The payment system can estimate the buyer's emotions and suggest payment methods based on those estimates. For example, if the buyer is in a hurry, it can suggest a quick payment method. If the buyer is relaxed, it can offer detailed payment options. Furthermore, if the buyer is stressed, it can suggest payment methods to reduce stress. By suggesting payment methods tailored to emotions, customer satisfaction is improved. Emotion estimation is achieved using, for example, an emotion engine or generative AI.

[0143] The delivery department can estimate the customer's emotions and adjust the delivery route based on those estimates. For example, if the customer is in a hurry, the shortest route will be prioritized. If the customer is relaxed, a scenic route can be selected. Furthermore, if the customer is stressed, a route that avoids congestion can be chosen. By adjusting the delivery route according to the customer's emotions, customer satisfaction can be improved. Emotion estimation is achieved using, for example, an emotion engine or generative AI.

[0144] The detection unit can analyze the congestion situation within the supermarket in real time and select the optimal detection method. For example, it can propose a detection method in real time to avoid crowded areas within the supermarket. It can also dynamically change the optimal detection method according to the congestion situation. Furthermore, it can select the optimal detection method considering the time of day when congestion is expected. By selecting the optimal detection method according to the congestion situation, safety is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0145] The mobile unit can select the optimal route to maintain product quality by considering the temperature and humidity within the supermarket. For example, it can avoid moving through high-temperature areas to maintain product quality. It can also avoid moving through high-humidity areas to prevent product deterioration. Furthermore, it can monitor changes in temperature and humidity in real time and dynamically adjust the optimal route. This ensures that product quality is maintained by selecting the optimal route based on temperature and humidity. Some or all of the above processing in the mobile unit may be performed using AI, for example, or without AI.

[0146] The manipulator unit can sense the temperature and humidity of the product and select the optimal handling method. For example, it can measure the product's temperature with a sensor and select an appropriate handling method. It can also measure the product's humidity with a sensor and select an appropriate handling method. Furthermore, it can dynamically adjust the manipulator's operation according to the product's temperature and humidity. This ensures that the product's quality is maintained by selecting the optimal handling method based on temperature and humidity. Some or all of the above-described processes in the manipulator unit may be performed using AI, for example, or without AI.

[0147] The following briefly describes the processing flow for example form 2.

[0148] Step 1: The reception desk accepts online requests from the purchaser. The reception desk can accept requests via, for example, a web browser, a mobile app, or voice commands. Step 2: The mobile unit moves around the supermarket based on the information received by the reception unit. The mobile unit can move by means of, for example, wheels, tracks, or by flight. Step 3: The manipulator unit picks up the product at the position moved by the moving unit. The manipulator unit can pick up the product using, for example, a gripper, a suction pad, etc. Step 4: The camera unit checks the condition and expiration date of the product picked up by the manipulator unit. The camera unit can check the condition and expiration date of the product using methods such as image analysis or barcode scanning. Step 5: The storage unit places the items into the basket based on the product information confirmed by the camera unit. The storage unit can place items into the basket using methods such as alignment and pressure. Step 6: The payment unit automatically processes payment for the items placed in the basket by the storage unit. The payment unit can automatically process payment for items using methods such as credit cards, electronic money, or QR codes. Step 7: The delivery department delivers the goods, which have been paid for by the payment department, to the customer's home. The delivery department can deliver the goods to the customer's home using methods such as drones, robotic cars, or courier services. Step 8: The detection unit monitors the operation of the mobile unit and the manipulator unit. The detection unit can monitor the operation of the mobile unit and the manipulator unit by methods such as cameras, sensors, and log analysis.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, mobile unit, manipulator unit, camera unit, storage unit, payment unit, delivery unit, and detection unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts online operations. The mobile unit is implemented by the control unit 46A of the smart device 14 and moves around the supermarket. The manipulator unit is implemented by the control unit 46A of the smart device 14 and picks up products. The camera unit is implemented by the camera 42 of the smart device 14 and checks the condition and expiration date of the products. The storage unit is implemented by the control unit 46A of the smart device 14 and places products in the basket. The payment unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically pays for the products. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12 and delivers the products to the customer's home. The detection unit is implemented by the camera 42 and sensors of the smart device 14, and monitors the operation of the moving unit and the manipulator unit. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0153] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reception unit, movement unit, manipulator unit, camera unit, storage unit, payment unit, delivery unit, and detection unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts online operations. The movement unit is implemented by the control unit 46A of the smart glasses 214 and moves around inside the supermarket. The manipulator unit is implemented by the control unit 46A of the smart glasses 214 and picks up products. The camera unit is implemented by the camera 42 of the smart glasses 214 and checks the condition and expiration date of the products. The storage unit is implemented by the control unit 46A of the smart glasses 214 and places products in a basket. The payment unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically pays for the products. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12 and delivers the products to the customer's home. The detection unit is implemented by the camera 42 and sensors of the smart glasses 214, and monitors the operation of the moving unit and the manipulator unit. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0169] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[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 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.

[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 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.

[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 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.

[0184] Each of the multiple elements described above, including the reception unit, mobile unit, manipulator unit, camera unit, storage unit, payment unit, delivery unit, and detection unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts online operations. The mobile unit is implemented by the control unit 46A of the headset terminal 314 and moves around the supermarket. The manipulator unit is implemented by the control unit 46A of the headset terminal 314 and picks up products. The camera unit is implemented by the camera 42 of the headset terminal 314 and checks the condition and expiration date of the products. The storage unit is implemented by the control unit 46A of the headset terminal 314 and places products in the basket. The payment unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically pays for the products. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12 and delivers the products to the customer's home. The detection unit is implemented by the camera 42 and sensors of the headset-type terminal 314, and monitors the operation of the mobile unit and the manipulator unit. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0185] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] Each of the multiple elements described above, including the reception unit, mobile unit, manipulator unit, camera unit, storage unit, payment unit, delivery unit, and detection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts online operations. The mobile unit is implemented by the control unit 46A of the robot 414 and moves around the supermarket. The manipulator unit is implemented by the control unit 46A of the robot 414 and picks up products. The camera unit is implemented by the camera 42 of the robot 414 and checks the condition and expiration date of the products. The storage unit is implemented by the control unit 46A of the robot 414 and places products in a basket. The payment unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically pays for the products. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12 and delivers the products to the customer's home. The detection unit is implemented by the camera 42 and sensors of the robot 414, and monitors the operation of the moving unit and the manipulator unit. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] (Note 1) A reception desk that accepts online requests from purchasers, A mobile unit that moves within the supermarket based on the information received by the reception unit, A manipulator unit that picks up the product at the position moved by the aforementioned moving unit, A camera unit that checks the condition and expiration date of the product picked up by the manipulator unit, A storage unit into which products are placed based on product information confirmed by the aforementioned camera unit, The aforementioned storage unit automatically processes payment for items placed in the basket, A delivery unit delivers the goods that have been paid for by the aforementioned payment unit to the customer's home. The system includes a detection unit that monitors the operation of the moving part and the manipulator part. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the buyer's emotions and adjusts the online interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We analyze the customer's past usage history and suggest the optimal method of operation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system automatically adjusts the priority of operations based on the buyer's current purchasing intent. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the buyer's emotions and adjusts the difficulty of the operation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Based on the buyer's geographical location, we suggest the most suitable supermarket. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the social media activity of buyers and suggest relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned movable part is It estimates the buyer's emotions and adjusts the movement speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned movable part is The system analyzes congestion levels within the supermarket in real time and selects the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned movable part is Refer to product placement information to perform efficient movement. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned movable part is It estimates the buyer's emotions and adjusts the travel route based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned movable part is Considering the temperature and humidity inside the supermarket, we select the optimal route for transporting products to maintain their quality. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned movable part is It can cooperate with other robots to achieve efficient movement. The system described in Appendix 1, characterized by the features described herein. (Note 14) The manipulator unit is, It estimates the buyer's emotions and adjusts the manipulator's operating speed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The manipulator unit is, The system automatically selects the optimal gripping method based on the shape and weight of the product. The system described in Appendix 1, characterized by the features described herein. (Note 16) The manipulator unit is, Adjust the amount of force applied when handling the product to prevent damage. The system described in Appendix 1, characterized by the features described herein. (Note 17) The manipulator unit is, The system estimates the buyer's emotions and adjusts the manipulator's range of motion based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The manipulator unit is, It senses the temperature and humidity of the product and selects the optimal handling method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The manipulator unit is, It works in coordination with other robots to achieve efficient tasks. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned camera unit is It estimates the buyer's emotions and adjusts the camera's zoom level based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned camera unit is The system automatically analyzes the product's condition and expiration date and notifies the buyer. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned camera unit is Adjust the camera's field of view to capture the overall image of the product. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned camera unit is It estimates the buyer's emotions and adjusts the camera resolution based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned camera unit is Analyze the color and shape of the product to evaluate its quality. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned camera unit is The camera's video feed is streamed in real time and provided to the buyer. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned storage compartment is It estimates the buyer's emotions and adjusts the storage method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned storage compartment is The system automatically selects the optimal storage method based on the shape and weight of the product. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned storage compartment is Adjust the amount of force applied during storage to prevent damage to the product. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned storage compartment is It estimates the buyer's emotions and adjusts the storage order based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned storage compartment is It senses the temperature and humidity of the product and selects the optimal storage method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned storage compartment is During storage, the robots work in coordination with other robots to achieve efficient storage. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned settlement unit, It estimates the buyer's emotions and suggests payment methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned settlement unit, We analyze past purchase history and select the optimal payment method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned settlement unit, At the time of payment, the system automatically selects the buyer's credit card information and electronic payment system. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned settlement unit, The system estimates the buyer's emotions and determines the priority of the transaction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned settlement unit, At checkout, the system suggests the most suitable payment method based on the buyer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned settlement unit, During checkout, the system analyzes the buyer's social media activity and suggests relevant payment methods. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned delivery department, The system estimates the customer's emotions and adjusts the delivery route based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned delivery department, When delivering, the most suitable delivery method is selected based on the shape and weight of the product. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned delivery department, We adjust the amount of force applied during delivery to prevent damage to the product. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned delivery department, Estimate the emotions of the purchaser and determine the delivery priority based on the estimated emotions of the purchaser The system according to Appendix 1, characterized by the above. (Appendix 42) The delivery unit Select an optimal delivery route based on the geographical location information of the purchaser during delivery The system according to Appendix 1, characterized by the above. (Appendix 43) The delivery unit Perform coordinated operations with other delivery robots during delivery to achieve efficient delivery The system according to Appendix 1, characterized by the above. (Appendix 44) The detection unit Estimate the emotions of the purchaser and adjust the detection accuracy based on the estimated emotions of the purchaser The system according to Appendix 1, characterized by the above. (Appendix 45) The detection unit Analyze the congestion situation in the supermarket in real time and select an optimal detection method The system according to Appendix 1, characterized by the above. (Appendix 46) The detection unit Perform efficient detection by referring to the product placement information during detection The system according to Appendix 1, characterized by the above. (Appendix 47) The detection unit Estimate the emotions of the purchaser and adjust the detection range based on the estimated emotions of the purchaser The system according to Appendix 1, characterized by the above. (Appendix 48) The detection unit Select an optimal detection method considering the temperature and humidity in the supermarket The system according to Appendix 1, characterized by the above. (Appendix 49) The detection unit Perform coordinated operations with other robots during detection to achieve efficient detection The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0221] 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. A reception desk that accepts online requests from purchasers, A mobile unit that moves within the supermarket based on the information received by the reception unit, A manipulator unit that picks up the product at the position moved by the aforementioned moving unit, A camera unit that checks the condition and expiration date of the product picked up by the manipulator unit, A storage unit into which products are placed based on product information confirmed by the aforementioned camera unit, The aforementioned storage unit automatically processes payment for items placed in the basket, A delivery unit delivers the goods that have been paid for by the aforementioned payment unit to the customer's home. The system includes a detection unit that monitors the operation of the moving part and the manipulator part. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the buyer's emotions and adjusts the online interface based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is We analyze the customer's past usage history and suggest the optimal method of operation. The system according to feature 1.

4. The aforementioned reception unit is The system automatically adjusts the priority of operations based on the buyer's current purchasing intent. The system according to feature 1.

5. The aforementioned reception unit is It estimates the buyer's emotions and adjusts the difficulty of the operation based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Based on the buyer's geographical location, we suggest the most suitable supermarket. The system according to feature 1.

7. The aforementioned reception unit is Analyze the social media activity of buyers and suggest relevant products. The system according to feature 1.

8. The aforementioned movable part is It estimates the buyer's emotions and adjusts the movement speed based on the estimated emotions. The system according to feature 1.

Images