system

Abstract

An object of a system according to an embodiment is to stereoscopically visualize a product in a store so that a user can purchase the product while freely moving around.SOLUTION: A system includes an installation part, a visual processing part, a reproduction part, a navigation part, and a transition part. The installation part installs a camera for acquiring three dimensional data in a store. The visual processing part performs visual processing of displayed commodities and inventory commodities in the store on the basis of the data acquired by the installation part. The reproduction part reproduces the commodity on the three dimensional shop data generated by the visual processing part. The navigation section performs navigation for a user to freely move around the shop data reproduced by the reproduction section and purchase a commodity. The transition unit transitions from the electronic map service to the stereoscopic EC shop.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to visualize products in a store in three dimensions and to make purchases while moving around freely.

[0005] The system according to the embodiment aims to visualize products in a store in three dimensions, allowing users to move around freely while making purchases. [Means for solving the problem]

[0006] The system according to the embodiment includes an installation unit, a visual processing unit, a reproduction unit, a navigation unit, and a transition unit. The installation unit installs a camera to acquire three-dimensional data of the store interior. The visual processing unit performs visual processing of the products displayed and inventory items in the store based on the data acquired by the installation unit. The reproduction unit reproduces the products on the three-dimensional shop data generated by the visual processing unit. The navigation unit provides navigation so that a user can freely move around the three-dimensional shop data reproduced by the reproduction unit and purchase products. The transition unit transitions from the electronic map service to the three-dimensional EC shop. [Effects of the Invention]

[0007] The system according to the embodiment can visualize products in a store in three dimensions, allowing users to move around freely while making purchases. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A 3D e-commerce shop system according to an embodiment of the present invention uses AI cameras to generate 3D data of the store, allowing users to freely move around and purchase products. The 3D e-commerce shop system installs cameras to capture 3D data of the store, performs visual processing of the products displayed and inventory in the store based on the acquired data, and reproduces the products in the generated 3D shop data. Users can freely move around the 3D shop, select and purchase products. It is also possible to transition from electronic map services to 3D e-commerce shops. For example, a 3D e-commerce shop system places multiple AI cameras in appropriate locations throughout the store to cover the entire store. Next, based on the acquired data, it performs visual processing of the products displayed and inventory in the store. AI analyzes the acquired image data to identify the location and status of each product, which then reproduces the product in the 3D shop. Users can freely move around the 3D shop using their smartphones or computers. For example, if a user is looking for a specific product, they can check which shelf the product is on and approach the shelf to view the product in detail. They can also select products and purchase them online. Furthermore, it is anticipated that the system will be linked to electronic map services, allowing users to search for a specific store on the electronic map service and then proceed directly to that store's 3D e-commerce shop. This allows users to purchase products online without actually going to the store. This allows the 3D e-commerce shop system to allow users to move freely around the store and purchase products from the comfort of their own homes. It will also enable stores to improve the efficiency of inventory management and product display. For example, AI can grasp product inventory in real time and automatically replenish products that are running low. This is expected to improve the efficiency of store operations.

[0029] A three-dimensional e-commerce shop system according to an embodiment includes an installation unit, a visual processing unit, a reproduction unit, a navigation unit, and a transition unit. The installation unit installs cameras to acquire three-dimensional data of the store interior. For example, the installation unit arranges multiple cameras in appropriate positions to cover the entire store. For example, the installation unit installs cameras on the ceiling or walls to capture images of each area of ​​the store. This allows three-dimensional data of the store interior to be acquired. The visual processing unit performs visual processing of products displayed and inventory items in the store based on the data acquired by the installation unit. For example, the visual processing unit analyzes the acquired image data to identify the location and status of each product. For example, the visual processing unit can determine which shelf a product is displayed on and how much inventory is available. The reproduction unit reproduces products on the three-dimensional shop data generated by the visual processing unit. For example, the reproduction unit reproduces products on the three-dimensional shop data using 3D modeling or rendering technology. The navigation unit navigates the three-dimensional shop data reproduced by the reproduction unit so that a user can freely move around and purchase products. For example, if a user is looking for a specific product, the navigation unit can check which shelf the product is on and then approach the shelf to view the product in detail. The navigation unit also provides navigation for the user to select a product and purchase it online. The transition unit provides a function for transitioning from an electronic map service to a three-dimensional e-commerce shop. For example, the transition unit can enable a user to search for a specific store on the electronic map service and transition directly to that store's three-dimensional e-commerce shop. As a result, the three-dimensional e-commerce shop system according to the embodiment can provide a three-dimensional e-commerce shop where users can move around freely and purchase products.

[0030] The installation unit can arrange multiple cameras at predetermined positions to cover the entire store. The predetermined positions include, but are not limited to, the installation height and angle of the cameras. For example, the installation unit can install multiple cameras on the ceiling or walls to cover the entire store. For example, the installation unit can adjust the installation height of the cameras to capture images of each area in the store. The installation unit can also adjust the angle of the cameras to grasp an overall view of the store. This allows the entire store to be covered, thereby acquiring three-dimensional data.

[0031] The visual processing unit can analyze the acquired image data and identify the position and status of each product. The visual processing unit analyzes the acquired image data using, for example, an image processing algorithm. For example, the visual processing unit can identify the position of each product using object detection technology. The visual processing unit can also identify the status of each product using data analysis technology. For example, the visual processing unit can determine which shelf a product is displayed on and how much inventory is available. This allows accurate visual processing to be performed by identifying the position and status of each product.

[0032] The reproduction unit can reproduce products on the three-dimensional shop data. The reproduction unit reproduces products on the three-dimensional shop data, for example, using 3D modeling technology. For example, the reproduction unit uses rendering technology to realistically reproduce the texture and gloss of the products. The reproduction unit can also simulate the movement of the products to provide a more realistic experience to the user. For example, the reproduction unit simulates the movement of the products in real time and changes the movement in response to user operations. This allows the products to be reproduced on the three-dimensional shop data, allowing the user to check the products.

[0033] The navigation unit can provide navigation for a user to freely move around the three-dimensional shop and select and purchase products. The navigation unit, for example, uses a user interface to enable the user to freely move around the three-dimensional shop. For example, if a user is looking for a specific product, the navigation unit can check which shelf the product is on and then approach the shelf to view the product in detail. The navigation unit can also provide navigation for a user to select a product and purchase it online. For example, the navigation unit provides an operation method for a user to select a product and perform a purchase procedure. This allows the user to freely move around the three-dimensional shop, select a product, and purchase it.

[0034] The transition unit may provide a function for transitioning from the electronic map service to the 3D e-commerce shop. The transition unit may transition from the electronic map service to the 3D e-commerce shop, for example, using a data transfer method. For example, the transition unit may use a user interface to enable a user to search for a specific store on the electronic map service and transition directly to the store's 3D e-commerce shop. The transition unit may also provide an operation method for a user to transition from the electronic map service to the 3D e-commerce shop. For example, the transition unit may provide a button for a user to select a store on the electronic map service and transition to the 3D e-commerce shop. This allows transition from the electronic map service to the 3D e-commerce shop.

[0035] The installation unit can analyze the congestion status within the store in real time and dynamically change the predetermined camera installation positions. The installation unit can, for example, use people counting technology to analyze the congestion status within the store in real time. For example, the installation unit can analyze images from cameras installed in each area of ​​the store and calculate the congestion level. The installation unit can also analyze the congestion status within the store using traffic flow analysis technology. For example, the installation unit can track the movement of people within the store and identify congested areas. Furthermore, the installation unit can dynamically change the installation positions of the cameras depending on the congestion status. For example, when the store is crowded, the installation positions of the cameras can be changed to focus on monitoring the congested areas. When the store is empty, the installation positions of the cameras can be adjusted to cover a wider area. In this way, the installation positions of the cameras can be dynamically changed depending on the congestion status within the store, allowing for focused monitoring of congested areas.

[0036] When installing the camera, the installation unit can automatically set predetermined shooting conditions taking into account the lighting conditions inside the store. The installation unit, for example, uses a light sensor to measure the lighting conditions inside the store. For example, the installation unit measures the light intensity and color temperature inside the store and sets optimal shooting conditions. The installation unit can also automatically adjust the exposure settings of the camera. For example, if the lighting inside the store is bright, the installation unit adjusts the exposure settings of the camera to set optimal shooting conditions. If the lighting inside the store is dim, the installation unit increases the sensitivity of the camera to optimize shooting conditions. Furthermore, if the lighting inside the store fluctuates, the installation unit adjusts the camera settings in real time to maintain optimal shooting conditions. This allows the quality of shooting to be improved by setting optimal shooting conditions according to the lighting conditions inside the store.

[0037] The installation unit may be provided with a predetermined relocation function to accommodate changes in the store layout when the cameras are installed. The installation unit, for example, uses a sensor to detect changes in the store layout. For example, the installation unit detects changes in the layout of shelves or aisles in the store and automatically relocates the installation positions of the cameras. The installation unit may also optimize the installation positions of the cameras in response to changes in the store layout. For example, when a new product shelf is added, the installation unit adjusts the installation positions of the cameras to cover the new area. The installation unit may also have a relocation algorithm to accommodate changes in the store layout. For example, the installation unit may detect changes in the store layout in real time and optimize the installation positions of the cameras. This allows the entire store to be covered by automatically relocating the installation positions of the cameras to accommodate changes in the store layout.

[0038] When installing the camera, the installation unit can select the optimal installation position taking into account the temperature and humidity inside the store. The installation unit measures the temperature and humidity inside the store using, for example, a temperature sensor and a humidity sensor. For example, if the temperature inside the store is high, the installation unit adjusts the installation position of the camera to minimize the impact of heat. Also, if the humidity inside the store is high, the installation unit can change the installation position of the camera to avoid the impact of humidity. Furthermore, if the temperature or humidity inside the store fluctuates, the installation unit adjusts the installation position of the camera in real time to maintain optimal conditions. In this way, the performance of the camera can be maintained by selecting the optimal installation position according to the temperature and humidity inside the store.

[0039] When installing the camera, the installation unit may analyze the acoustic environment in the store and adopt an installation method that minimizes noise. The installation unit may, for example, use an acoustic sensor to measure the acoustic environment in the store. For example, the installation unit may measure the noise level in the store and install the camera in an area with less noise. The installation unit may also minimize noise using noise filtering technology. For example, if the acoustic environment in the store changes, the installation unit may adjust the installation position of the camera in real time to minimize noise. Furthermore, if noise increases during a specific time period, the installation unit may change the installation position of the camera in advance to address the situation. In this way, noise can be minimized by adjusting the installation position of the camera according to the acoustic environment in the store.

[0040] When installing cameras, the installation unit can select the optimal installation location taking into account the security requirements within the store. The installation unit selects the optimal installation location, for example, taking into account the installation location and monitoring range of the security camera. For example, the installation unit installs cameras to cover important areas within the store. Furthermore, if the security requirements within the store change, the installation unit can adjust the installation location of the cameras in real time to maintain optimal security. Furthermore, if security risks increase during specific times of the day, the installation unit can respond by changing the installation location of the cameras in advance. In this way, security can be strengthened by selecting the optimal installation location according to the security requirements within the store.

[0041] The visual processing unit performs detailed analysis of the shape and color of a product during visual processing, thereby improving the accuracy of product identification. The visual processing unit, for example, uses a shape recognition algorithm to analyze the shape of the product in detail. For example, the visual processing unit extracts the contours and feature points of the product to improve the accuracy of identification from similar products. The visual processing unit can also analyze the color of the product in detail using color analysis technology. For example, the visual processing unit analyzes the color distribution and hue of the product to accurately identify products of different colors. Furthermore, the visual processing unit performs a combined analysis of shape and color to further improve the accuracy of identification. This allows for detailed analysis of the shape and color of the product to improve the accuracy of identification.

[0042] The visual processing unit can add a function to automatically read product barcodes and QR codes (registered trademark) during visual processing. The visual processing unit automatically reads product barcodes and QR codes, for example, using a barcode reader or QR code scanner. For example, the visual processing unit reads product barcodes and updates inventory information. The visual processing unit can also read product QR codes to obtain detailed information. Furthermore, the visual processing unit can read barcodes and QR codes in combination to improve product identification accuracy. This makes it possible to update inventory information by automatically reading product barcodes and QR codes.

[0043] The visual processing unit can have a function to detect the deterioration state of a product during visual processing and reflect the result in inventory management. The visual processing unit detects the deterioration state of a product, for example, using an algorithm that detects changes in color or shape. For example, the visual processing unit detects the deterioration state when the color of a product changes and updates the inventory information. The visual processing unit can also detect the deterioration state when the shape of a product changes and remove the product from inventory. Furthermore, the visual processing unit detects the deterioration state in real time and reflects the result in the inventory management system. In this way, inventory management can be optimized by detecting the deterioration state of a product.

[0044] The visual processing unit can add a function to automatically measure the weight and size of products during visual processing. The visual processing unit automatically measures the weight and size of products using, for example, a weight sensor or a size measuring device. For example, the visual processing unit measures the weight of products and updates inventory information. The visual processing unit can also measure the size of products and optimize display space. Furthermore, the visual processing unit measures weight and size in combination to improve the accuracy of product identification. This makes it possible to update inventory information by automatically measuring the weight and size of products.

[0045] The visual processing unit can add a function to automatically recognize the manufacturing date and expiration date of a product during visual processing. The visual processing unit automatically recognizes the manufacturing date and expiration date of a product, for example, using label reading technology. For example, the visual processing unit reads the label of the product and identifies the manufacturing date and expiration date. The visual processing unit can also recognize the manufacturing date and expiration date of a product using database reference technology. For example, the visual processing unit references a product database and identifies the manufacturing date and expiration date. Furthermore, the visual processing unit recognizes the manufacturing date and expiration date in combination, thereby optimizing inventory management. In this way, inventory management can be optimized by automatically recognizing the manufacturing date and expiration date of a product.

[0046] The visual processing unit can have a function of analyzing product placement patterns during visual processing and proposing an optimal display method. The visual processing unit, for example, uses a product placement algorithm to analyze product placement patterns. For example, the visual processing unit analyzes product placement patterns and proposes a display method that maximizes sales. The visual processing unit can also use pattern recognition technology to propose a display method that optimizes customer movement lines. For example, the visual processing unit analyzes customer movement lines and proposes an optimal display method. Furthermore, the visual processing unit analyzes placement patterns in real time and continuously proposes optimal display methods. In this way, it is possible to propose optimal display methods by analyzing product placement patterns.

[0047] The reproduction unit may employ technology for realistically reproducing the texture and gloss of the product during reproduction. The reproduction unit may, for example, use rendering technology to realistically reproduce the texture and gloss of the product. For example, the reproduction unit may reproduce the texture and gloss of the product's surface in detail, providing the user with a realistic experience. The reproduction unit may also use texture mapping technology to realistically reproduce the texture of the product. For example, the reproduction unit may reproduce the texture of the product's surface in detail to enhance its visual appeal. Furthermore, the reproduction unit may reproduce a combination of texture and gloss to enhance the realism of the product. This allows the texture and gloss of the product to be realistically reproduced, providing the user with a realistic experience.

[0048] The reproduction unit simulates the movement of the product during reproduction, thereby providing the user with a more realistic experience. The reproduction unit simulates the movement of the product, for example, using physical simulation technology. For example, the reproduction unit simulates the movement of the product in real time and changes the movement in response to the user's operation. The reproduction unit can also simulate the movement of the product in detail using animation technology. For example, the reproduction unit simulates the movement of the product in detail, providing the user with an experience that feels like they are actually touching the product. Furthermore, the reproduction unit simulates the movement in detail to improve the realism of the product. In this way, by simulating the movement of the product, the user can be provided with an experience that feels like they are actually touching the product.

[0049] During reproduction, the reproduction unit can add an audio guide for the product to provide detailed information about the product to the user. The reproduction unit can add an audio guide for the product using, for example, voice synthesis technology. For example, the reproduction unit can provide an audio guide that explains the features and usage of the product. The reproduction unit can also provide detailed information about the product using a narration script. For example, the reproduction unit can provide a narration script that explains the features and usage of the product. Furthermore, the reproduction unit can make the audio guide multilingual to accommodate users of different languages. For example, the reproduction unit can provide an audio guide in multiple languages, such as English, Japanese, and French. In this way, adding an audio guide for the product can explain the features and usage of the product to the user.

[0050] The reproduction unit can employ technology to reproduce the scent of the product during reproduction. The reproduction unit, for example, uses a scent diffuser to reproduce the scent of the product. For example, the reproduction unit reproduces the scent of the product in detail, providing the user with an experience that is close to the real thing. The reproduction unit can also adjust the type of fragrance and the strength of the scent. For example, the reproduction unit adjusts the strength of the scent to reproduce a scent that suits the user's preferences. Furthermore, the reproduction unit can reproduce a combination of multiple scents to enhance the appeal of the product. In this way, by reproducing the scent of the product, it is possible to provide the user with an experience that is close to the real thing.

[0051] The reproduction unit may employ technology for reproducing the temperature and texture of the product during reproduction. For example, the reproduction unit may use temperature control technology to reproduce the temperature of the product. For example, the reproduction unit may reproduce the temperature of the product in detail, providing the user with a realistic experience. The reproduction unit may also reproduce the texture of the product using a haptic feedback device. For example, the reproduction unit may reproduce the texture of the product in detail, providing the user with an experience similar to that of actually touching the product. Furthermore, the reproduction unit may reproduce a combination of temperature and texture to enhance the realism of the product. In this way, by reproducing the temperature and texture of the product, it is possible to provide the user with a realistic experience.

[0052] During reproduction, the reproduction unit can display background information about the product and provide the user with the product's history and manufacturing process. The reproduction unit displays the product's background information using, for example, text information or image information. For example, the reproduction unit provides text information explaining the product's history and manufacturing process. The reproduction unit can also provide image information showing the product's manufacturing process. For example, the reproduction unit displays images showing the product's manufacturing process and provides the user with detailed product information. Furthermore, the reproduction unit makes the background information multilingual to accommodate users of different languages. For example, the reproduction unit provides background information in multiple languages, such as English, Japanese, and French. In this way, the product's history and manufacturing process can be explained to the user by displaying the product's background information.

[0053] During navigation, the navigation unit can suggest an optimal route by referring to the user's past behavior history. The navigation unit acquires the user's past behavior history, for example, by using location information or an operation log. For example, the navigation unit suggests an optimal route based on routes the user has used in the past. The navigation unit can also suggest a route that avoids congestion based on the user's past behavior history. For example, the navigation unit analyzes the user's past behavior history and suggests the most efficient route. In this way, the navigation unit can suggest an optimal route by referring to the user's past behavior history.

[0054] The navigation unit can reflect the congestion status inside the store in real time during navigation and dynamically change the optimal route. The navigation unit analyzes the congestion status inside the store in real time using, for example, people counting technology and traffic flow analysis technology. For example, the navigation unit analyzes images from cameras installed in each area of ​​the store to calculate the congestion level. The navigation unit can also track people's movements inside the store and identify congested areas. Furthermore, the navigation unit dynamically changes the optimal route depending on the congestion status. For example, if the store is crowded, the navigation unit suggests a route that avoids the congestion. Furthermore, if the congestion status inside the store changes, the navigation unit changes the route in real time to provide optimal guidance. In this way, by reflecting the congestion status inside the store in real time, it is possible to suggest a route that avoids the congestion.

[0055] The navigation unit can display recommended products based on the user's interests and concerns during navigation. The navigation unit acquires the user's interests and concerns, for example, using survey results or past purchase history. For example, the navigation unit displays recommended products based on the user's interests during navigation. The navigation unit can also display related products based on the user's past purchase history. For example, the navigation unit displays products that the user is likely to be interested in based on the user's search history. In this way, by displaying recommended products based on the user's interests and concerns, it is possible to increase the user's purchasing motivation.

[0056] During navigation, the navigation unit can select the optimal display method by taking into consideration the user's device information. The navigation unit acquires the user's device information, for example, using technology to acquire the device type and screen size. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the navigation unit provides a simple and highly visible display method. This makes it possible to improve visibility by providing the optimal display method based on the user's device information.

[0057] The navigation unit can provide multilingual guidance during navigation according to the user's language setting. The navigation unit acquires the user's language setting, for example, using user settings or device settings. For example, the navigation unit automatically sets the navigation language based on the language setting of the user's device. The navigation unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the navigation unit provides navigation in that language. This improves user convenience by providing multilingual guidance according to the user's language setting.

[0058] The navigation unit can provide guidance customized according to the user's visual and auditory characteristics during navigation. The navigation unit acquires the user's visual and auditory characteristics, for example, using technology for acquiring visual acuity information and hearing information. For example, the navigation unit performs color vision correction and enlarged display according to the user's visual characteristics. The navigation unit can also adjust the volume and tone of audio guidance according to the user's auditory characteristics. For example, the navigation unit combines the user's visual and auditory characteristics to provide the optimal guidance method. This can improve user convenience by providing guidance customized according to the user's visual and auditory characteristics.

[0059] At the time of migration, the migration unit can select the optimal migration method by referring to the user's past migration history. The migration unit acquires the user's past migration history, for example, by using past migration data or operation logs. For example, the migration unit proposes the optimal migration method based on migration methods used by the user in the past. The migration unit can also propose a migration method that avoids congestion based on the user's past migration history. For example, the migration unit analyzes the user's past migration history and proposes the most efficient migration method. In this way, the optimal migration method can be proposed by referring to the user's past migration history.

[0060] During the transition, the transition unit can propose an optimal transition route by taking into account the user's current location information. The transition unit acquires the user's current location information, for example, using GPS data or beacon information. For example, the transition unit proposes an optimal transition route based on the user's current location information. Furthermore, the transition unit can also adjust the transition route in real time when the user's current location information changes. For example, the transition unit proposes a transition route that avoids congestion by taking into account the user's current location information. In this way, the optimal transition route can be proposed by taking into account the user's current location information.

[0061] The transition unit can provide an optimal transition method during transition by taking into account device information of the user. The transition unit acquires device information of the user, for example, using technology for acquiring the device type and screen size. For example, if the user is using a smartphone, the transition unit provides a transition method that matches the screen size. Furthermore, if the user is using a tablet, the transition unit can also provide a transition method optimized for a large screen. For example, if the user is using a smartwatch, the transition unit provides a simple and highly visible transition method. This can improve visibility by providing an optimal transition method based on the user's device information.

[0062] At the time of transition, the transition unit can propose the optimal transition timing by taking into account the user's schedule information. The transition unit acquires the user's schedule information, for example, using calendar information or a timetable. For example, the transition unit proposes the optimal transition timing based on the user's schedule information. Furthermore, the transition unit can also adjust the transition timing in real time when the user's schedule information changes. For example, the transition unit proposes a transition timing that avoids congestion by taking into account the user's schedule information. In this way, the optimal transition timing can be proposed by taking into account the user's schedule information.

[0063] The migration unit can customize the migration method by reflecting the user's past feedback during migration. The migration unit obtains the user's past feedback, for example, by using survey results or user reviews. For example, the migration unit proposes an optimal migration method based on the user's past feedback. The migration unit can also analyze the user's past feedback and customize the migration method. For example, the migration unit reflects the user's past feedback in real time and optimizes the migration method. In this way, the optimal migration method can be provided by reflecting the user's past feedback.

[0064] During the transition, the transition unit can display recommended three-dimensional e-commerce shops based on the user's interests and concerns. The transition unit acquires the user's interests and concerns, for example, using survey results or past purchase history. For example, the transition unit displays recommended three-dimensional e-commerce shops based on the user's interests. The transition unit can also display related three-dimensional e-commerce shops based on the user's past purchase history. For example, the transition unit displays three-dimensional e-commerce shops that the user is likely to be interested in based on the user's search history. This can increase the user's desire to purchase by displaying recommended three-dimensional e-commerce shops based on the user's interests and concerns.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The installation unit can analyze the congestion status within the store in real time and dynamically change the predetermined camera installation positions. The installation unit can, for example, use people counting technology to analyze the congestion status within the store in real time. For example, the installation unit can analyze images from cameras installed in each area of ​​the store and calculate the congestion level. The installation unit can also analyze the congestion status within the store using traffic flow analysis technology. For example, the installation unit can track the movement of people within the store and identify congested areas. Furthermore, the installation unit can dynamically change the installation positions of the cameras depending on the congestion status. For example, when the store is crowded, the installation positions of the cameras can be changed to focus on monitoring the congested areas. When the store is empty, the installation positions of the cameras can be adjusted to cover a wider area. In this way, the installation positions of the cameras can be dynamically changed depending on the congestion status within the store, allowing for focused monitoring of congested areas.

[0067] When installing the camera, the installation unit can automatically set predetermined shooting conditions taking into account the lighting conditions inside the store. The installation unit, for example, uses a light sensor to measure the lighting conditions inside the store. For example, the installation unit measures the light intensity and color temperature inside the store and sets optimal shooting conditions. The installation unit can also automatically adjust the exposure settings of the camera. For example, if the lighting inside the store is bright, the installation unit adjusts the exposure settings of the camera to set optimal shooting conditions. If the lighting inside the store is dim, the installation unit increases the sensitivity of the camera to optimize shooting conditions. Furthermore, if the lighting inside the store fluctuates, the installation unit adjusts the camera settings in real time to maintain optimal shooting conditions. This allows the quality of shooting to be improved by setting optimal shooting conditions according to the lighting conditions inside the store.

[0068] The installation unit may be provided with a predetermined relocation function to accommodate changes in the store layout when the cameras are installed. The installation unit, for example, uses a sensor to detect changes in the store layout. For example, the installation unit detects changes in the layout of shelves or aisles in the store and automatically relocates the installation positions of the cameras. The installation unit may also optimize the installation positions of the cameras in response to changes in the store layout. For example, when a new product shelf is added, the installation unit adjusts the installation positions of the cameras to cover the new area. The installation unit may also have a relocation algorithm to accommodate changes in the store layout. For example, the installation unit may detect changes in the store layout in real time and optimize the installation positions of the cameras. This allows the entire store to be covered by automatically relocating the installation positions of the cameras to accommodate changes in the store layout.

[0069] The visual processing unit performs detailed analysis of the shape and color of a product during visual processing, thereby improving the accuracy of product identification. The visual processing unit, for example, uses a shape recognition algorithm to analyze the shape of the product in detail. For example, the visual processing unit extracts the contours and feature points of the product to improve the accuracy of identification from similar products. The visual processing unit can also analyze the color of the product in detail using color analysis technology. For example, the visual processing unit analyzes the color distribution and hue of the product to accurately identify products of different colors. Furthermore, the visual processing unit performs a combined analysis of shape and color to further improve the accuracy of identification. This allows for detailed analysis of the shape and color of the product to improve the accuracy of identification.

[0070] The visual processing unit can add a function to automatically read product barcodes and QR codes during visual processing. The visual processing unit automatically reads product barcodes and QR codes, for example, using a barcode reader or QR code scanner. For example, the visual processing unit reads product barcodes and updates inventory information. The visual processing unit can also read product QR codes to obtain detailed information. Furthermore, the visual processing unit can read barcodes and QR codes in combination to improve product identification accuracy. This makes it possible to update inventory information by automatically reading product barcodes and QR codes.

[0071] The visual processing unit can have a function to detect the deterioration state of a product during visual processing and reflect the result in inventory management. The visual processing unit detects the deterioration state of a product, for example, using an algorithm that detects changes in color or shape. For example, the visual processing unit detects the deterioration state when the color of a product changes and updates the inventory information. The visual processing unit can also detect the deterioration state when the shape of a product changes and remove the product from inventory. Furthermore, the visual processing unit detects the deterioration state in real time and reflects the result in the inventory management system. In this way, inventory management can be optimized by detecting the deterioration state of a product.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The installation unit installs cameras to capture three-dimensional data inside the store. For example, the installation unit places multiple cameras in appropriate positions to cover the entire store. By installing cameras on the ceiling and walls and capturing images of each area inside the store, three-dimensional data inside the store can be captured. Step 2: The visual processing unit performs visual processing of the products displayed in the store and the inventory items based on the data acquired by the installation unit. The visual processing unit analyzes the acquired image data and identifies the location and status of each product. For example, it can determine which shelf a product is displayed on and how much stock is available. Step 3: The reproduction unit reproduces the products on the 3D shop data generated by the visual processing unit. The reproduction unit reproduces the products on the 3D shop data using 3D modeling and rendering techniques. Step 4: The navigation unit navigates the 3D shop data reproduced by the reproduction unit, allowing the user to move freely around and purchase products. If the user is looking for a specific product, the navigation unit checks which shelf the product is on and allows the user to approach the shelf and view the product in detail. The unit also navigates the user to select a product and purchase it online. Step 5: The transition unit provides a function for transitioning from the electronic map service to the 3D e-commerce shop. The transition unit allows a user to search for a specific store on the electronic map service and transition directly to the store's 3D e-commerce shop.

[0074] (Example 2) A 3D e-commerce shop system according to an embodiment of the present invention uses AI cameras to generate 3D data of the store, allowing users to freely move around and purchase products. The 3D e-commerce shop system installs cameras to capture 3D data of the store, performs visual processing of the products displayed and inventory in the store based on the acquired data, and reproduces the products in the generated 3D shop data. Users can freely move around the 3D shop, select and purchase products. It is also possible to transition from electronic map services to 3D e-commerce shops. For example, a 3D e-commerce shop system places multiple AI cameras in appropriate locations throughout the store to cover the entire store. Next, based on the acquired data, it performs visual processing of the products displayed and inventory in the store. AI analyzes the acquired image data to identify the location and status of each product, which then reproduces the product in the 3D shop. Users can freely move around the 3D shop using their smartphones or computers. For example, if a user is looking for a specific product, they can check which shelf the product is on and approach the shelf to view the product in detail. They can also select products and purchase them online. Furthermore, it is anticipated that the system will be linked to electronic map services, allowing users to search for a specific store on the electronic map service and then proceed directly to that store's 3D e-commerce shop. This allows users to purchase products online without actually going to the store. This allows the 3D e-commerce shop system to allow users to move freely around the store and purchase products from the comfort of their own homes. It will also enable stores to improve the efficiency of inventory management and product display. For example, AI can grasp product inventory in real time and automatically replenish products that are running low. This is expected to improve the efficiency of store operations.

[0075] A three-dimensional e-commerce shop system according to an embodiment includes an installation unit, a visual processing unit, a reproduction unit, a navigation unit, and a transition unit. The installation unit installs cameras to acquire three-dimensional data of the store interior. For example, the installation unit arranges multiple cameras in appropriate positions to cover the entire store. For example, the installation unit installs cameras on the ceiling or walls to capture images of each area of ​​the store. This allows three-dimensional data of the store interior to be acquired. The visual processing unit performs visual processing of products displayed and inventory items in the store based on the data acquired by the installation unit. For example, the visual processing unit analyzes the acquired image data to identify the location and status of each product. For example, the visual processing unit can determine which shelf a product is displayed on and how much inventory is available. The reproduction unit reproduces products on the three-dimensional shop data generated by the visual processing unit. For example, the reproduction unit reproduces products on the three-dimensional shop data using 3D modeling or rendering technology. The navigation unit navigates the three-dimensional shop data reproduced by the reproduction unit so that a user can freely move around and purchase products. For example, if a user is looking for a specific product, the navigation unit can check which shelf the product is on and then approach the shelf to view the product in detail. The navigation unit also provides navigation for the user to select a product and purchase it online. The transition unit provides a function for transitioning from an electronic map service to a three-dimensional e-commerce shop. For example, the transition unit can enable a user to search for a specific store on the electronic map service and transition directly to that store's three-dimensional e-commerce shop. As a result, the three-dimensional e-commerce shop system according to the embodiment can provide a three-dimensional e-commerce shop where users can move around freely and purchase products.

[0076] The installation unit can arrange multiple cameras at predetermined positions to cover the entire store. The predetermined positions include, but are not limited to, the installation height and angle of the cameras. For example, the installation unit can install multiple cameras on the ceiling or walls to cover the entire store. For example, the installation unit can adjust the installation height of the cameras to capture images of each area in the store. The installation unit can also adjust the angle of the cameras to grasp an overall view of the store. This allows the entire store to be covered, thereby acquiring three-dimensional data.

[0077] The visual processing unit can analyze the acquired image data and identify the position and status of each product. The visual processing unit analyzes the acquired image data using, for example, an image processing algorithm. For example, the visual processing unit can identify the position of each product using object detection technology. The visual processing unit can also identify the status of each product using data analysis technology. For example, the visual processing unit can determine which shelf a product is displayed on and how much inventory is available. This allows accurate visual processing to be performed by identifying the position and status of each product.

[0078] The reproduction unit can reproduce products on the three-dimensional shop data. The reproduction unit reproduces products on the three-dimensional shop data, for example, using 3D modeling technology. For example, the reproduction unit uses rendering technology to realistically reproduce the texture and gloss of the products. The reproduction unit can also simulate the movement of the products to provide a more realistic experience to the user. For example, the reproduction unit simulates the movement of the products in real time and changes the movement in response to user operations. This allows the products to be reproduced on the three-dimensional shop data, allowing the user to check the products.

[0079] The navigation unit can provide navigation for a user to freely move around the three-dimensional shop and select and purchase products. The navigation unit, for example, uses a user interface to enable the user to freely move around the three-dimensional shop. For example, if a user is looking for a specific product, the navigation unit can check which shelf the product is on and then approach the shelf to view the product in detail. The navigation unit can also provide navigation for a user to select a product and purchase it online. For example, the navigation unit provides an operation method for a user to select a product and perform a purchase procedure. This allows the user to freely move around the three-dimensional shop, select a product, and purchase it.

[0080] The transition unit may provide a function for transitioning from the electronic map service to the 3D e-commerce shop. The transition unit may transition from the electronic map service to the 3D e-commerce shop, for example, using a data transfer method. For example, the transition unit may use a user interface to enable a user to search for a specific store on the electronic map service and transition directly to the store's 3D e-commerce shop. The transition unit may also provide an operation method for a user to transition from the electronic map service to the 3D e-commerce shop. For example, the transition unit may provide a button for a user to select a store on the electronic map service and transition to the 3D e-commerce shop. This allows transition from the electronic map service to the 3D e-commerce shop.

[0081] The installation unit can estimate the user's emotions and adjust the installation position of the camera based on the estimated user's emotions. The installation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the installation unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The installation unit can also estimate the user's emotions using voice analysis technology. For example, the installation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the installation unit adjusts the installation position of the camera based on the estimated user's emotions. For example, if the user is feeling stressed, the installation position of the camera is changed to reduce visual strain. Furthermore, if the user is relaxed, the installation position of the camera is optimized to cover a wider area. In this way, visual strain can be reduced by adjusting the installation position of the camera based on the user's emotions.

[0082] The installation unit can analyze the congestion status within the store in real time and dynamically change the predetermined camera installation positions. The installation unit can, for example, use people counting technology to analyze the congestion status within the store in real time. For example, the installation unit can analyze images from cameras installed in each area of ​​the store and calculate the congestion level. The installation unit can also analyze the congestion status within the store using traffic flow analysis technology. For example, the installation unit can track the movement of people within the store and identify congested areas. Furthermore, the installation unit can dynamically change the installation positions of the cameras depending on the congestion status. For example, when the store is crowded, the installation positions of the cameras can be changed to focus on monitoring the congested areas. When the store is empty, the installation positions of the cameras can be adjusted to cover a wider area. In this way, the installation positions of the cameras can be dynamically changed depending on the congestion status within the store, allowing for focused monitoring of congested areas.

[0083] When installing the camera, the installation unit can automatically set predetermined shooting conditions taking into account the lighting conditions inside the store. The installation unit, for example, uses a light sensor to measure the lighting conditions inside the store. For example, the installation unit measures the light intensity and color temperature inside the store and sets optimal shooting conditions. The installation unit can also automatically adjust the exposure settings of the camera. For example, if the lighting inside the store is bright, the installation unit adjusts the exposure settings of the camera to set optimal shooting conditions. If the lighting inside the store is dim, the installation unit increases the sensitivity of the camera to optimize shooting conditions. Furthermore, if the lighting inside the store fluctuates, the installation unit adjusts the camera settings in real time to maintain optimal shooting conditions. This allows the quality of shooting to be improved by setting optimal shooting conditions according to the lighting conditions inside the store.

[0084] The installation unit may be provided with a predetermined relocation function to accommodate changes in the store layout when the cameras are installed. The installation unit, for example, uses a sensor to detect changes in the store layout. For example, the installation unit detects changes in the layout of shelves or aisles in the store and automatically relocates the installation positions of the cameras. The installation unit may also optimize the installation positions of the cameras in response to changes in the store layout. For example, when a new product shelf is added, the installation unit adjusts the installation positions of the cameras to cover the new area. The installation unit may also have a relocation algorithm to accommodate changes in the store layout. For example, the installation unit may detect changes in the store layout in real time and optimize the installation positions of the cameras. This allows the entire store to be covered by automatically relocating the installation positions of the cameras to accommodate changes in the store layout.

[0085] The installation unit can estimate the user's emotions and adjust the installation timing of the camera based on the estimated user's emotions. The installation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the installation unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The installation unit can also estimate the user's emotions using voice analysis technology. For example, the installation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the installation unit adjusts the installation timing of the camera based on the estimated user's emotions. For example, if the user is feeling stressed, the installation timing of the camera is delayed to reduce the burden on the user. Also, if the user is relaxed, the installation timing of the camera is advanced to efficiently install the camera. In this way, visual burden can be reduced by adjusting the installation timing of the camera based on the user's emotions.

[0086] When installing the camera, the installation unit can select the optimal installation position taking into account the temperature and humidity inside the store. The installation unit measures the temperature and humidity inside the store using, for example, a temperature sensor and a humidity sensor. For example, if the temperature inside the store is high, the installation unit adjusts the installation position of the camera to minimize the impact of heat. Also, if the humidity inside the store is high, the installation unit can change the installation position of the camera to avoid the impact of humidity. Furthermore, if the temperature or humidity inside the store fluctuates, the installation unit adjusts the installation position of the camera in real time to maintain optimal conditions. In this way, the performance of the camera can be maintained by selecting the optimal installation position according to the temperature and humidity inside the store.

[0087] When installing the camera, the installation unit may analyze the acoustic environment in the store and adopt an installation method that minimizes noise. The installation unit may, for example, use an acoustic sensor to measure the acoustic environment in the store. For example, the installation unit may measure the noise level in the store and install the camera in an area with less noise. The installation unit may also minimize noise using noise filtering technology. For example, if the acoustic environment in the store changes, the installation unit may adjust the installation position of the camera in real time to minimize noise. Furthermore, if noise increases during a specific time period, the installation unit may change the installation position of the camera in advance to address the situation. In this way, noise can be minimized by adjusting the installation position of the camera according to the acoustic environment in the store.

[0088] When installing cameras, the installation unit can select the optimal installation location taking into account the security requirements within the store. The installation unit selects the optimal installation location, for example, taking into account the installation location and monitoring range of the security camera. For example, the installation unit installs cameras to cover important areas within the store. Furthermore, if the security requirements within the store change, the installation unit can adjust the installation location of the cameras in real time to maintain optimal security. Furthermore, if security risks increase during specific times of the day, the installation unit can respond by changing the installation location of the cameras in advance. In this way, security can be strengthened by selecting the optimal installation location according to the security requirements within the store.

[0089] The visual processing unit can estimate the user's emotions and determine the priority of visual processing based on the estimated user's emotions. The visual processing unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the visual processing unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The visual processing unit can also estimate the user's emotions using voice analysis technology. For example, the visual processing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the visual processing unit determines the priority of visual processing based on the estimated user's emotions. For example, if the user is feeling stressed, the visual processing unit prioritizes visual processing of important products. Also, if the user is relaxed, the visual processing unit performs overall visual processing evenly. In this way, by determining the priority of visual processing based on the user's emotions, the visual processing of important products can be prioritized.

[0090] The visual processing unit performs detailed analysis of the shape and color of a product during visual processing, thereby improving the accuracy of product identification. The visual processing unit, for example, uses a shape recognition algorithm to analyze the shape of the product in detail. For example, the visual processing unit extracts the contours and feature points of the product to improve the accuracy of identification from similar products. The visual processing unit can also analyze the color of the product in detail using color analysis technology. For example, the visual processing unit analyzes the color distribution and hue of the product to accurately identify products of different colors. Furthermore, the visual processing unit performs a combined analysis of shape and color to further improve the accuracy of identification. This allows for detailed analysis of the shape and color of the product to improve the accuracy of identification.

[0091] The visual processing unit can add a function to automatically read product barcodes and QR codes during visual processing. The visual processing unit automatically reads product barcodes and QR codes, for example, using a barcode reader or QR code scanner. For example, the visual processing unit reads product barcodes and updates inventory information. The visual processing unit can also read product QR codes to obtain detailed information. Furthermore, the visual processing unit can read barcodes and QR codes in combination to improve product identification accuracy. This makes it possible to update inventory information by automatically reading product barcodes and QR codes.

[0092] The visual processing unit can have a function to detect the deterioration state of a product during visual processing and reflect the result in inventory management. The visual processing unit detects the deterioration state of a product, for example, using an algorithm that detects changes in color or shape. For example, the visual processing unit detects the deterioration state when the color of a product changes and updates the inventory information. The visual processing unit can also detect the deterioration state when the shape of a product changes and remove the product from inventory. Furthermore, the visual processing unit detects the deterioration state in real time and reflects the result in the inventory management system. In this way, inventory management can be optimized by detecting the deterioration state of a product.

[0093] The visual processing unit can estimate the user's emotion and adjust the speed of visual processing based on the estimated user's emotion. The visual processing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the visual processing unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The visual processing unit can also estimate the user's emotion using voice analysis technology. For example, the visual processing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the visual processing unit adjusts the speed of visual processing based on the estimated user's emotion. For example, if the user is feeling stressed, the speed of visual processing is increased to provide quick results. On the other hand, if the user is relaxed, the speed of visual processing is maintained at a normal level. In this way, by adjusting the speed of visual processing based on the user's emotion, it is possible to provide quick results.

[0094] The visual processing unit can add a function to automatically measure the weight and size of products during visual processing. The visual processing unit automatically measures the weight and size of products using, for example, a weight sensor or a size measuring device. For example, the visual processing unit measures the weight of products and updates inventory information. The visual processing unit can also measure the size of products and optimize display space. Furthermore, the visual processing unit measures weight and size in combination to improve the accuracy of product identification. This makes it possible to update inventory information by automatically measuring the weight and size of products.

[0095] The visual processing unit can add a function to automatically recognize the manufacturing date and expiration date of a product during visual processing. The visual processing unit automatically recognizes the manufacturing date and expiration date of a product, for example, using label reading technology. For example, the visual processing unit reads the label of the product and identifies the manufacturing date and expiration date. The visual processing unit can also recognize the manufacturing date and expiration date of a product using database reference technology. For example, the visual processing unit references a product database and identifies the manufacturing date and expiration date. Furthermore, the visual processing unit recognizes the manufacturing date and expiration date in combination, thereby optimizing inventory management. In this way, inventory management can be optimized by automatically recognizing the manufacturing date and expiration date of a product.

[0096] The visual processing unit can have a function of analyzing product placement patterns during visual processing and proposing an optimal display method. The visual processing unit, for example, uses a product placement algorithm to analyze product placement patterns. For example, the visual processing unit analyzes product placement patterns and proposes a display method that maximizes sales. The visual processing unit can also use pattern recognition technology to propose a display method that optimizes customer movement lines. For example, the visual processing unit analyzes customer movement lines and proposes an optimal display method. Furthermore, the visual processing unit analyzes placement patterns in real time and continuously proposes optimal display methods. In this way, it is possible to propose optimal display methods by analyzing product placement patterns.

[0097] The reproduction unit can estimate the user's emotions and adjust the product reproduction method based on the estimated user's emotions. The reproduction unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reproduction unit analyzes the user's facial expression data to estimate emotions such as stress or relaxation. The reproduction unit can also estimate the user's emotions using voice analysis technology. For example, the reproduction unit analyzes the tone and speed of the user's voice to estimate emotions. Furthermore, the reproduction unit adjusts the product reproduction method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple reproduction method is provided. Also, if the user is relaxed, a detailed reproduction method is provided. In this way, visual burden can be reduced by adjusting the product reproduction method based on the user's emotions.

[0098] The reproduction unit may employ technology for realistically reproducing the texture and gloss of the product during reproduction. The reproduction unit may, for example, use rendering technology to realistically reproduce the texture and gloss of the product. For example, the reproduction unit may reproduce the texture and gloss of the product's surface in detail, providing the user with a realistic experience. The reproduction unit may also use texture mapping technology to realistically reproduce the texture of the product. For example, the reproduction unit may reproduce the texture of the product's surface in detail to enhance its visual appeal. Furthermore, the reproduction unit may reproduce a combination of texture and gloss to enhance the realism of the product. This allows the texture and gloss of the product to be realistically reproduced, providing the user with a realistic experience.

[0099] The reproduction unit simulates the movement of the product during reproduction, thereby providing the user with a more realistic experience. The reproduction unit simulates the movement of the product, for example, using physical simulation technology. For example, the reproduction unit simulates the movement of the product in real time and changes the movement in response to the user's operation. The reproduction unit can also simulate the movement of the product in detail using animation technology. For example, the reproduction unit simulates the movement of the product in detail, providing the user with an experience that feels like they are actually touching the product. Furthermore, the reproduction unit simulates the movement in detail to improve the realism of the product. In this way, by simulating the movement of the product, the user can be provided with an experience that feels like they are actually touching the product.

[0100] During reproduction, the reproduction unit can add an audio guide for the product to provide detailed information about the product to the user. The reproduction unit can add an audio guide for the product using, for example, voice synthesis technology. For example, the reproduction unit can provide an audio guide that explains the features and usage of the product. The reproduction unit can also provide detailed information about the product using a narration script. For example, the reproduction unit can provide a narration script that explains the features and usage of the product. Furthermore, the reproduction unit can make the audio guide multilingual to accommodate users of different languages. For example, the reproduction unit can provide an audio guide in multiple languages, such as English, Japanese, and French. In this way, adding an audio guide for the product can explain the features and usage of the product to the user.

[0101] The reproduction unit can estimate the user's emotions and adjust the order in which products are displayed based on the estimated user's emotions. The reproduction unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reproduction unit analyzes the user's facial expression data to estimate emotions such as stress or relaxation. The reproduction unit can also estimate the user's emotions using voice analysis technology. For example, the reproduction unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the reproduction unit adjusts the order in which products are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the reproduction of important products is prioritized. Also, if the user is relaxed, the overall reproduction order is kept even. In this way, by adjusting the order in which products are displayed based on the user's emotions, the reproduction of important products can be prioritized.

[0102] The reproduction unit can employ technology to reproduce the scent of the product during reproduction. The reproduction unit, for example, uses a scent diffuser to reproduce the scent of the product. For example, the reproduction unit reproduces the scent of the product in detail, providing the user with an experience that is close to the real thing. The reproduction unit can also adjust the type of fragrance and the strength of the scent. For example, the reproduction unit adjusts the strength of the scent to reproduce a scent that suits the user's preferences. Furthermore, the reproduction unit can reproduce a combination of multiple scents to enhance the appeal of the product. In this way, by reproducing the scent of the product, it is possible to provide the user with an experience that is close to the real thing.

[0103] The reproduction unit may employ technology for reproducing the temperature and texture of the product during reproduction. For example, the reproduction unit may use temperature control technology to reproduce the temperature of the product. For example, the reproduction unit may reproduce the temperature of the product in detail, providing the user with a realistic experience. The reproduction unit may also reproduce the texture of the product using a haptic feedback device. For example, the reproduction unit may reproduce the texture of the product in detail, providing the user with an experience similar to that of actually touching the product. Furthermore, the reproduction unit may reproduce a combination of temperature and texture to enhance the realism of the product. In this way, by reproducing the temperature and texture of the product, it is possible to provide the user with a realistic experience.

[0104] During reproduction, the reproduction unit can display background information about the product and provide the user with the product's history and manufacturing process. The reproduction unit displays the product's background information using, for example, text information or image information. For example, the reproduction unit provides text information explaining the product's history and manufacturing process. The reproduction unit can also provide image information showing the product's manufacturing process. For example, the reproduction unit displays images showing the product's manufacturing process and provides the user with detailed product information. Furthermore, the reproduction unit makes the background information multilingual to accommodate users of different languages. For example, the reproduction unit provides background information in multiple languages, such as English, Japanese, and French. In this way, the product's history and manufacturing process can be explained to the user by displaying the product's background information.

[0105] The navigation unit can estimate the user's emotions and adjust the navigation guidance method based on the estimated user's emotions. The navigation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the navigation unit analyzes the user's facial expression data to estimate emotions such as stress or relaxation. The navigation unit can also estimate the user's emotions using voice analysis technology. For example, the navigation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the navigation unit adjusts the navigation guidance method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple guidance method with high visibility is provided. Also, if the user is relaxed, a guidance method including detailed information is provided. In this way, by adjusting the navigation guidance method based on the user's emotions, a highly visible guidance method can be provided.

[0106] During navigation, the navigation unit can suggest an optimal route by referring to the user's past behavior history. The navigation unit acquires the user's past behavior history, for example, by using location information or an operation log. For example, the navigation unit suggests an optimal route based on routes the user has used in the past. The navigation unit can also suggest a route that avoids congestion based on the user's past behavior history. For example, the navigation unit analyzes the user's past behavior history and suggests the most efficient route. In this way, the navigation unit can suggest an optimal route by referring to the user's past behavior history.

[0107] The navigation unit can reflect the congestion status inside the store in real time during navigation and dynamically change the optimal route. The navigation unit analyzes the congestion status inside the store in real time using, for example, people counting technology and traffic flow analysis technology. For example, the navigation unit analyzes images from cameras installed in each area of ​​the store to calculate the congestion level. The navigation unit can also track people's movements inside the store and identify congested areas. Furthermore, the navigation unit dynamically changes the optimal route depending on the congestion status. For example, if the store is crowded, the navigation unit suggests a route that avoids the congestion. Furthermore, if the congestion status inside the store changes, the navigation unit changes the route in real time to provide optimal guidance. In this way, by reflecting the congestion status inside the store in real time, it is possible to suggest a route that avoids the congestion.

[0108] The navigation unit can display recommended products based on the user's interests and concerns during navigation. The navigation unit acquires the user's interests and concerns, for example, using survey results or past purchase history. For example, the navigation unit displays recommended products based on the user's interests during navigation. The navigation unit can also display related products based on the user's past purchase history. For example, the navigation unit displays products that the user is likely to be interested in based on the user's search history. In this way, by displaying recommended products based on the user's interests and concerns, it is possible to increase the user's purchasing motivation.

[0109] The navigation unit can estimate the user's emotions and adjust the navigation speed based on the estimated user's emotions. The navigation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the navigation unit analyzes the user's facial expression data to estimate emotions such as stress or relaxation. The navigation unit can also estimate the user's emotions using voice analysis technology. For example, the navigation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the navigation unit adjusts the navigation speed based on the estimated user's emotions. For example, if the user is in a hurry, the navigation speed is increased to provide quick guidance. On the other hand, if the user is relaxed, the navigation speed is maintained at a normal speed. In this way, by adjusting the navigation speed based on the user's emotions, quick guidance can be provided.

[0110] During navigation, the navigation unit can select the optimal display method by taking into consideration the user's device information. The navigation unit acquires the user's device information, for example, using technology to acquire the device type and screen size. For example, if the user is using a smartphone, the navigation unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the navigation unit provides a simple and highly visible display method. This makes it possible to improve visibility by providing the optimal display method based on the user's device information.

[0111] The navigation unit can provide multilingual guidance during navigation according to the user's language setting. The navigation unit acquires the user's language setting, for example, using user settings or device settings. For example, the navigation unit automatically sets the navigation language based on the language setting of the user's device. The navigation unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the navigation unit provides navigation in that language. This improves user convenience by providing multilingual guidance according to the user's language setting.

[0112] The navigation unit can provide guidance customized according to the user's visual and auditory characteristics during navigation. The navigation unit acquires the user's visual and auditory characteristics, for example, using technology for acquiring visual acuity information and hearing information. For example, the navigation unit performs color vision correction and enlarged display according to the user's visual characteristics. The navigation unit can also adjust the volume and tone of audio guidance according to the user's auditory characteristics. For example, the navigation unit combines the user's visual and auditory characteristics to provide the optimal guidance method. This can improve user convenience by providing guidance customized according to the user's visual and auditory characteristics.

[0113] The transition unit can estimate the user's emotion and adjust the timing of the transition based on the estimated user's emotion. The transition unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the transition unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The transition unit can also estimate the user's emotion using voice analysis technology. For example, the transition unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the transition unit adjusts the timing of the transition based on the estimated user's emotion. For example, if the user is feeling stressed, the timing of the transition is delayed to reduce the burden. Also, if the user is relaxed, the timing of the transition is advanced to make the transition efficient. In this way, by adjusting the timing of the transition based on the user's emotion, visual burden can be reduced.

[0114] At the time of migration, the migration unit can select the optimal migration method by referring to the user's past migration history. The migration unit acquires the user's past migration history, for example, by using past migration data or operation logs. For example, the migration unit proposes the optimal migration method based on migration methods used by the user in the past. The migration unit can also propose a migration method that avoids congestion based on the user's past migration history. For example, the migration unit analyzes the user's past migration history and proposes the most efficient migration method. In this way, the optimal migration method can be proposed by referring to the user's past migration history.

[0115] During the transition, the transition unit can propose an optimal transition route by taking into account the user's current location information. The transition unit acquires the user's current location information, for example, using GPS data or beacon information. For example, the transition unit proposes an optimal transition route based on the user's current location information. Furthermore, the transition unit can also adjust the transition route in real time when the user's current location information changes. For example, the transition unit proposes a transition route that avoids congestion by taking into account the user's current location information. In this way, the optimal transition route can be proposed by taking into account the user's current location information.

[0116] The transition unit can provide an optimal transition method during transition by taking into account device information of the user. The transition unit acquires device information of the user, for example, using technology for acquiring the device type and screen size. For example, if the user is using a smartphone, the transition unit provides a transition method that matches the screen size. Furthermore, if the user is using a tablet, the transition unit can also provide a transition method optimized for a large screen. For example, if the user is using a smartwatch, the transition unit provides a simple and highly visible transition method. This can improve visibility by providing an optimal transition method based on the user's device information.

[0117] The transition unit can estimate the user's emotions and determine the priority of transitions based on the estimated user's emotions. The transition unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the transition unit analyzes the user's facial expression data to estimate emotions such as stress or relaxation. The transition unit can also estimate the user's emotions using voice analysis technology. For example, the transition unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the transition unit determines the priority of transitions based on the estimated user's emotions. For example, if the user is feeling stressed, important transitions are prioritized. Also, if the user is relaxed, the overall transitions are kept even. In this way, by determining the priority of transitions based on the user's emotions, important transitions can be prioritized.

[0118] At the time of transition, the transition unit can propose the optimal transition timing by taking into account the user's schedule information. The transition unit acquires the user's schedule information, for example, using calendar information or a timetable. For example, the transition unit proposes the optimal transition timing based on the user's schedule information. Furthermore, the transition unit can also adjust the transition timing in real time when the user's schedule information changes. For example, the transition unit proposes a transition timing that avoids congestion by taking into account the user's schedule information. In this way, the optimal transition timing can be proposed by taking into account the user's schedule information.

[0119] The migration unit can customize the migration method by reflecting the user's past feedback during migration. The migration unit obtains the user's past feedback, for example, by using survey results or user reviews. For example, the migration unit proposes an optimal migration method based on the user's past feedback. The migration unit can also analyze the user's past feedback and customize the migration method. For example, the migration unit reflects the user's past feedback in real time and optimizes the migration method. In this way, the optimal migration method can be provided by reflecting the user's past feedback.

[0120] During the transition, the transition unit can display recommended three-dimensional e-commerce shops based on the user's interests and concerns. The transition unit acquires the user's interests and concerns, for example, using survey results or past purchase history. For example, the transition unit displays recommended three-dimensional e-commerce shops based on the user's interests. The transition unit can also display related three-dimensional e-commerce shops based on the user's past purchase history. For example, the transition unit displays three-dimensional e-commerce shops that the user is likely to be interested in based on the user's search history. This can increase the user's desire to purchase by displaying recommended three-dimensional e-commerce shops based on the user's interests and concerns. === Hard Collateral 1-1 === Each of the above-described elements, including the installation unit, visual processing unit, reproduction unit, navigation unit, and transition unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the installation unit acquires three-dimensional data of the store interior using the camera 42 of the smart device 14 or the communication I / F 26 of the data processing device 12. The visual processing unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the acquired data and identifies the product locations and statuses. The reproduction unit, implemented, for example, by the control unit 46A of the smart device 14, reproduces products on the three-dimensional shop data. The navigation unit, implemented, for example, by the control unit 46A of the smart device 14, navigates the user to freely move around the three-dimensional shop and purchase products. The transition unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides a function for transitioning from an electronic map service to a three-dimensional e-commerce shop. === Hard Collateral 1-2 === Each of the multiple elements, including the installation unit, visual processing unit, reproduction unit, navigation unit, and transition unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the installation unit acquires three-dimensional data of the store interior using the camera 42 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The visual processing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the acquired data and identifies the product locations and statuses. The reproduction unit, realized, for example, by the control unit 46A of the smart glasses 214, reproduces products on the three-dimensional shop data. The navigation unit, realized, for example, by the control unit 46A of the smart glasses 214, navigates the user to freely move around the three-dimensional shop and purchase products. The transition unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides a function for transitioning from an electronic map service to a three-dimensional e-commerce shop. === Hard Collateral 1-3 === Each of the multiple elements, including the installation unit, visual processing unit, reproduction unit, navigation unit, and transition unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the installation unit acquires three-dimensional data of the store interior using the camera 42 of the headset terminal 314 or the communication I / F 26 of the data processing device 12. The visual processing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the acquired data and identifies the product locations and statuses. The reproduction unit, realized, for example, by the control unit 46A of the headset terminal 314, reproduces products on the three-dimensional shop data. The navigation unit, realized, for example, by the control unit 46A of the headset terminal 314, navigates the user to freely move around the three-dimensional shop and purchase products. The transition unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides a function for transitioning from an electronic map service to a three-dimensional e-commerce shop. === Hard Collateral 1-4 === Each of the multiple elements, including the installation unit, visual processing unit, reproduction unit, navigation unit, and transition unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the installation unit acquires three-dimensional data of the store interior using the camera 42 of the robot 414 or the communication I / F 26 of the data processing device 12. The visual processing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the acquired data and identifies the product positions and statuses. The reproduction unit, realized, for example, by the control unit 46A of the robot 414, reproduces products on the three-dimensional shop data. The navigation unit, realized, for example, by the control unit 46A of the robot 414, navigates the user to freely move around the three-dimensional shop and purchase products. The transition unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides a function for transitioning from an electronic map service to a three-dimensional e-commerce shop.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The installation unit can estimate the user's emotions and adjust the installation position of the camera based on the estimated user's emotions. The installation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the installation unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The installation unit can also estimate the user's emotions using voice analysis technology. For example, the installation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the installation unit adjusts the installation position of the camera based on the estimated user's emotions. For example, if the user is feeling stressed, the installation position of the camera is changed to reduce visual strain. Furthermore, if the user is relaxed, the installation position of the camera is optimized to cover a wider area. In this way, visual strain can be reduced by adjusting the installation position of the camera based on the user's emotions.

[0123] The installation unit can analyze the congestion status within the store in real time and dynamically change the predetermined camera installation positions. The installation unit can, for example, use people counting technology to analyze the congestion status within the store in real time. For example, the installation unit can analyze images from cameras installed in each area of ​​the store and calculate the congestion level. The installation unit can also analyze the congestion status within the store using traffic flow analysis technology. For example, the installation unit can track the movement of people within the store and identify congested areas. Furthermore, the installation unit can dynamically change the installation positions of the cameras depending on the congestion status. For example, when the store is crowded, the installation positions of the cameras can be changed to focus on monitoring the congested areas. When the store is empty, the installation positions of the cameras can be adjusted to cover a wider area. In this way, the installation positions of the cameras can be dynamically changed depending on the congestion status within the store, allowing for focused monitoring of congested areas.

[0124] When installing the camera, the installation unit can automatically set predetermined shooting conditions taking into account the lighting conditions inside the store. The installation unit, for example, uses a light sensor to measure the lighting conditions inside the store. For example, the installation unit measures the light intensity and color temperature inside the store and sets optimal shooting conditions. The installation unit can also automatically adjust the exposure settings of the camera. For example, if the lighting inside the store is bright, the installation unit adjusts the exposure settings of the camera to set optimal shooting conditions. If the lighting inside the store is dim, the installation unit increases the sensitivity of the camera to optimize shooting conditions. Furthermore, if the lighting inside the store fluctuates, the installation unit adjusts the camera settings in real time to maintain optimal shooting conditions. This allows the quality of shooting to be improved by setting optimal shooting conditions according to the lighting conditions inside the store.

[0125] The installation unit may be provided with a predetermined relocation function to accommodate changes in the store layout when the cameras are installed. The installation unit, for example, uses a sensor to detect changes in the store layout. For example, the installation unit detects changes in the layout of shelves or aisles in the store and automatically relocates the installation positions of the cameras. The installation unit may also optimize the installation positions of the cameras in response to changes in the store layout. For example, when a new product shelf is added, the installation unit adjusts the installation positions of the cameras to cover the new area. The installation unit may also have a relocation algorithm to accommodate changes in the store layout. For example, the installation unit may detect changes in the store layout in real time and optimize the installation positions of the cameras. This allows the entire store to be covered by automatically relocating the installation positions of the cameras to accommodate changes in the store layout.

[0126] The installation unit can estimate the user's emotions and adjust the installation timing of the camera based on the estimated user's emotions. The installation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the installation unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The installation unit can also estimate the user's emotions using voice analysis technology. For example, the installation unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the installation unit adjusts the installation timing of the camera based on the estimated user's emotions. For example, if the user is feeling stressed, the installation timing of the camera is delayed to reduce the burden on the user. Also, if the user is relaxed, the installation timing of the camera is advanced to efficiently install the camera. In this way, visual burden can be reduced by adjusting the installation timing of the camera based on the user's emotions.

[0127] The visual processing unit can estimate the user's emotions and determine the priority of visual processing based on the estimated user's emotions. The visual processing unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the visual processing unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The visual processing unit can also estimate the user's emotions using voice analysis technology. For example, the visual processing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the visual processing unit determines the priority of visual processing based on the estimated user's emotions. For example, if the user is feeling stressed, the visual processing unit prioritizes visual processing of important products. Also, if the user is relaxed, the visual processing unit performs overall visual processing evenly. In this way, by determining the priority of visual processing based on the user's emotions, the visual processing of important products can be prioritized.

[0128] The visual processing unit performs detailed analysis of the shape and color of a product during visual processing, thereby improving the accuracy of product identification. The visual processing unit, for example, uses a shape recognition algorithm to analyze the shape of the product in detail. For example, the visual processing unit extracts the contours and feature points of the product to improve the accuracy of identification from similar products. The visual processing unit can also analyze the color of the product in detail using color analysis technology. For example, the visual processing unit analyzes the color distribution and hue of the product to accurately identify products of different colors. Furthermore, the visual processing unit performs a combined analysis of shape and color to further improve the accuracy of identification. This allows for detailed analysis of the shape and color of the product to improve the accuracy of identification.

[0129] The visual processing unit can add a function to automatically read product barcodes and QR codes during visual processing. The visual processing unit automatically reads product barcodes and QR codes, for example, using a barcode reader or QR code scanner. For example, the visual processing unit reads product barcodes and updates inventory information. The visual processing unit can also read product QR codes to obtain detailed information. Furthermore, the visual processing unit can read barcodes and QR codes in combination to improve product identification accuracy. This makes it possible to update inventory information by automatically reading product barcodes and QR codes.

[0130] The visual processing unit can have a function to detect the deterioration state of a product during visual processing and reflect the result in inventory management. The visual processing unit detects the deterioration state of a product, for example, using an algorithm that detects changes in color or shape. For example, the visual processing unit detects the deterioration state when the color of a product changes and updates the inventory information. The visual processing unit can also detect the deterioration state when the shape of a product changes and remove the product from inventory. Furthermore, the visual processing unit detects the deterioration state in real time and reflects the result in the inventory management system. In this way, inventory management can be optimized by detecting the deterioration state of a product.

[0131] The visual processing unit can estimate the user's emotion and adjust the speed of visual processing based on the estimated user's emotion. The visual processing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the visual processing unit analyzes the user's facial expression data and estimates emotions such as stress or relaxation. The visual processing unit can also estimate the user's emotion using voice analysis technology. For example, the visual processing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the visual processing unit adjusts the speed of visual processing based on the estimated user's emotion. For example, if the user is feeling stressed, the speed of visual processing is increased to provide quick results. On the other hand, if the user is relaxed, the speed of visual processing is maintained at a normal level. In this way, by adjusting the speed of visual processing based on the user's emotion, it is possible to provide quick results.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The installation unit installs cameras to capture three-dimensional data inside the store. For example, the installation unit places multiple cameras in appropriate positions to cover the entire store. By installing cameras on the ceiling and walls and capturing images of each area inside the store, three-dimensional data inside the store can be captured. Step 2: The visual processing unit performs visual processing of the products displayed in the store and the inventory items based on the data acquired by the installation unit. The visual processing unit analyzes the acquired image data and identifies the location and status of each product. For example, it can determine which shelf a product is displayed on and how much stock is available. Step 3: The reproduction unit reproduces the products on the 3D shop data generated by the visual processing unit. The reproduction unit reproduces the products on the 3D shop data using 3D modeling and rendering techniques. Step 4: The navigation unit navigates the 3D shop data reproduced by the reproduction unit, allowing the user to move freely around and purchase products. If the user is looking for a specific product, the navigation unit checks which shelf the product is on and allows the user to approach the shelf and view the product in detail. The unit also navigates the user to select a product and purchase it online. Step 5: The transition unit provides a function for transitioning from the electronic map service to the 3D e-commerce shop. The transition unit allows a user to search for a specific store on the electronic map service and transition directly to the store's 3D e-commerce shop.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an installation unit that installs cameras to acquire three-dimensional data inside the store; a visual processing unit that performs visual processing of the products on display and the products in stock in the store based on the data acquired by the installation unit; a reproduction unit that reproduces the products on the three-dimensional shop data generated by the visual processing unit; a navigation unit that performs navigation so that a user can freely move around in the three-dimensional shop data reproduced by the reproduction unit and purchase products; A transition unit for transitioning from an electronic map service to a three-dimensional EC shop is provided. A system characterized by:

2. The system according to claim 1 , wherein the installation unit arranges a plurality of cameras at predetermined positions to cover the entire store.

3. The visual processing unit Analyze the acquired image data to identify the location and condition of each product 2. The system of claim 1.

4. The reproducing section Reproducing products on 3D shop data 2. The system of claim 1.

5. The navigation unit Users can navigate freely through the 3D shop, select and purchase products, and more.

2. The system of claim 1.

6. The transition portion is Provides functionality for transitioning from an electronic map service to a 3D e-commerce shop 2. The system of claim 1.

7. The installation unit includes: Estimate the user's emotions and adjust the camera's installation position based on the estimated user emotions.

2. The system of claim 1.

8. The system according to claim 1, wherein the installation unit analyzes the congestion status in the store in real time and dynamically changes the predetermined camera installation positions.

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