system

Abstract

An object of a system according to an embodiment is to enable a customer to easily find coordinates suitable for a product at the time of purchase in an apparel store.SOLUTION: A system includes a commodity information acquisition unit, an information transmission unit, and a coordination proposal unit. The product information acquisition unit acquires product information using RFID technology. The information transmission unit transmits the product information acquired by the product information acquisition unit to the smartphone. The coordination proposing part proposes coordination by using the generated AI on the basis of the commodity information transmitted by the information transmitting part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for customers to find a coordinated outfit that matches the product when shopping at an apparel store, which can reduce their motivation to buy.

[0005] The system according to the embodiment aims to enable customers to easily find outfits that go well with products when making purchases at apparel stores. [Means for solving the problem]

[0006] The system according to the embodiment includes a product information acquisition unit, an information transmission unit, and a coordination suggestion unit. The product information acquisition unit acquires product information using RFID technology. The information transmission unit transmits the product information acquired by the product information acquisition unit to a smartphone. The coordination suggestion unit uses a generation AI to suggest coordination based on the product information transmitted by the information transmission unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable customers to easily find outfits that go well with products when making purchases at apparel stores. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A purchasing experience improvement system according to an embodiment of the present invention is a system that uses RFID technology to read an item placed in a shopping cart in an apparel store, sends the information to a smartphone attached to the shopping cart, and then a generating AI selects and suggests matching top and bottom items to go with the item. This system improves the customer's purchasing experience and increases store sales.

[0029] A purchasing experience improvement system according to an embodiment includes a product information acquisition unit, an information transmission unit, and a coordination suggestion unit. The product information acquisition unit acquires product information using RFID technology. For example, the product information acquisition unit uses an RFID reader to read product information from an RFID tag attached to a product. The product information acquisition unit can also acquire product information using passive RFID or active RFID. The product information acquisition unit can acquire information such as product name, price, size, and color. The information transmission unit transmits the product information acquired by the product information acquisition unit to a smartphone. For example, the information transmission unit transmits the product information to the smartphone using Bluetooth or Wi-Fi. The information transmission unit can transmit the product information using an operating system and necessary applications compatible with the smartphone. The information transmission unit can transmit the product information to the smartphone in real time. The coordination suggestion unit uses a generation AI to make coordination suggestions based on the product information transmitted by the information transmission unit. For example, the coordination suggestion unit uses the generation AI to select and suggest top and bottom items that match a product. The coordination suggestion unit can generate coordination suggestions based on the product information using the generation AI. Furthermore, the coordination suggestion unit can use algorithms and models to suggest coordination based on product information using the generation AI. As a result, the purchasing experience improvement system according to the embodiment can improve the customer's purchasing experience and increase store sales. For example, when a customer places an item in their cart, they can select additional items by referring to the top and bottom items suggested by the generation AI. Stores can also expect an increase in sales.

[0030] The coordination suggestion unit can make suggestions based on past purchase data or stylist coordination information. For example, the coordination suggestion unit analyzes past purchase data and makes suggestions based on data on what other products the same product has been purchased with in the past. The coordination suggestion unit can also use the stylist's coordination information to suggest coordinations recommended by the stylist. For example, it can suggest top and bottom items that go well with a product based on the coordination information suggested by the stylist. The coordination suggestion unit can also use generation AI to analyze past purchase data and stylist coordination information to suggest optimal coordinations. This makes it possible to make more attractive suggestions by utilizing past purchase data and stylist coordination information. For example, when a customer adds a product to their cart, they can select additional products by referring to the top and bottom items suggested by the generation AI based on past purchase data and stylist coordination information.

[0031] The information transmission unit can simultaneously transmit product images using the smartphone camera to add visual information. For example, when transmitting product information, the information transmission unit can take a product image using the smartphone camera and transmit the image at the same time. The information transmission unit can also take high-resolution images using the smartphone camera to provide customers with detailed product images. For example, this allows the customer to visually confirm the product's color, design, material texture, etc. The information transmission unit can also take product images using the smartphone camera and transmit the images in real time. This allows the customer to better understand product details by adding visual information. For example, when a customer adds a product to their cart, an image of the product is displayed on the smartphone screen, allowing the customer to confirm the product details based on the image.

[0032] The information transmission unit can compare the product information transmitted to the smartphone with the customer's past purchase history and make personalized suggestions. The information transmission unit, for example, builds a system that compares the product information transmitted to the smartphone with the customer's past purchase history. The information transmission unit can also analyze the customer's purchase history to make suggestions that combine products with previously purchased products. For example, the information transmission unit can suggest products of the same brand or style as products previously purchased by the customer. The information transmission unit can also make personalized suggestions based on the customer's purchase history to make suggestions based on the customer's preferences and behavioral patterns. This improves customer satisfaction by making personalized suggestions based on the customer's past purchase history. For example, when a customer adds a product to their cart, suggestions that combine products with previously purchased products can be made based on the product information transmitted to the smartphone, allowing the customer to select a product that will satisfy them more.

[0033] The coordination suggestion unit can take seasonal or weather information into account to suggest optimal coordination. For example, the coordination suggestion unit inputs seasonal information into the generation AI and suggests coordination according to the season. The coordination suggestion unit can also suggest coordination according to the weather based on the weather forecast. For example, it suggests clothes made of cool materials in summer and clothes made of warm materials in winter. The coordination suggestion unit can also use the generation AI to analyze seasonal and weather information to suggest optimal coordination. This makes it possible to suggest more practical coordination by taking seasonal and weather information into account. For example, when a customer adds an item to their cart, they can select additional items based on the top and bottom items suggested by the generation AI based on seasonal and weather information.

[0034] The outfit suggestion unit can make personalized suggestions that reflect the customer's body type and preferences. For example, the outfit suggestion unit inputs the customer's body type information, and the generation AI uses that information to suggest the optimal outfit. The outfit suggestion unit can also suggest outfits that suit the customer's tastes based on the customer's preference information. For example, suggestions are made based on the customer's favorite colors, styles, and brands. The outfit suggestion unit can also use the generation AI to analyze the customer's body type and preferences and make personalized suggestions. This makes it possible to make more personalized suggestions by reflecting the customer's body type and preferences. For example, when a customer adds items to their cart, they can select additional items by referring to the top and bottom items suggested by the generation AI based on the customer's body type and preferences.

[0035] The coordination suggestion unit can reflect reviews or ratings from other customers and make highly reliable suggestions. For example, the coordination suggestion unit collects reviews and ratings from other customers, and the generation AI makes coordination suggestions based on that information. The coordination suggestion unit can also prioritize suggestions that have received high ratings. For example, suggestions are made based on products and coordinations that other customers have given high ratings. The coordination suggestion unit can also use the generation AI to analyze reviews and ratings from other customers and make highly reliable suggestions. This makes it possible to make highly reliable suggestions by reflecting reviews and ratings from other customers. For example, when a customer adds an item to their cart, they can select additional items by referring to the top and bottom items suggested by the generation AI based on reviews and ratings from other customers.

[0036] The coordination suggestion unit can reflect the latest fashion trends and make suggestions that match the trends. For example, the coordination suggestion unit collects the latest fashion trend information, and the generation AI makes coordination suggestions based on that information. The coordination suggestion unit can also make suggestions that reflect the trend colors and styles of the current season. For example, it can suggest top and bottom items that go well with an item based on the latest fashion trends. The coordination suggestion unit can also analyze the latest fashion trends using the generation AI and make suggestions that match the trends. This makes it possible to make suggestions that match the trends to customers by reflecting the latest fashion trends. For example, when a customer adds an item to their cart, they can select additional items based on the top and bottom items suggested by the generation AI based on the latest fashion trends.

[0037] When transmitting product information, the information transmission unit simultaneously transmits store inventory information, allowing the unit to make suggestions to avoid out-of-stock products. The information transmission unit, for example, builds a system that transmits store inventory information to a smartphone along with product information. The information transmission unit can also update store inventory information in real time to make suggestions to avoid out-of-stock products. For example, when the inventory quantity is zero or below a certain quantity, the information transmission unit can make suggestions to avoid out-of-stock products. The information transmission unit can also suggest only in-stock products based on the inventory information. This improves the customer's purchasing experience by making suggestions to avoid out-of-stock products. For example, when a customer places a product in their cart, only in-stock products can be suggested based on the product information transmitted to the smartphone, preventing the customer from selecting an out-of-stock product.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The purchasing experience improvement system can further include a voice assistant unit. When a customer places an item in the cart, the voice assistant unit can provide product information and coordination suggestions by voice. For example, the voice assistant unit can explain the product's material, washing instructions, and coordination tips by voice. The voice assistant unit can also answer customer questions in real time. This can further improve the customer's purchasing experience by providing not only visual information but also auditory information. For example, when a customer places an item in the cart, the voice assistant can explain detailed product information, allowing the customer to select a product based on that information.

[0040] The purchasing experience improvement system can further include a feedback collection unit. The feedback collection unit can collect impressions and evaluations of products purchased by customers after they have used them. For example, the feedback collection unit can send a questionnaire to customers about their experience using the product and their level of satisfaction after a certain period of time has passed since purchase. The feedback collection unit can also analyze the collected feedback and reflect it in future outfit suggestions. This makes it possible to make suggestions based on the customer's actual experience using the product, resulting in more accurate outfit suggestions. For example, when a customer adds a product to their cart, they can select additional products by referring to suggestions based on past feedback.

[0041] The shopping experience improvement system can further include a virtual fitting section. The virtual fitting section allows customers to virtually try on selected products using a smartphone camera. For example, the virtual fitting section recognizes the customer's body type and facial features and displays a virtual image of the customer wearing the selected products on the smartphone screen. The virtual fitting section can also virtually try on outfits that combine multiple products. This allows customers to check the fit and appearance of products without actually trying them on. For example, when a customer adds products to their shopping cart, they can try on the products using the virtual fitting section and select products based on the results.

[0042] The purchasing experience improvement system can further include a social sharing unit. The social sharing unit can share products and outfits selected by customers on social media. For example, the social sharing unit provides a function for customers to post images of their selected products and outfits on social media. The social sharing unit can also collect comments and ratings from the customer's friends and followers and reflect them in outfit suggestions. This allows customers to select products while taking into consideration the opinions of their friends and followers. For example, when a customer adds a product to their cart, they can use the social sharing unit to share the selected product on social media, allowing them to select products based on the opinions of their friends and followers.

[0043] The purchasing experience improvement system may further include a points management unit. The points management unit awards points each time a customer purchases a product, allowing the customer to use the points for their next purchase. For example, the points management unit awards points according to the purchase amount when a customer purchases a product. The points management unit may also allow the customer to use the points they have accumulated as a discount for their next purchase. This increases the enjoyment of accumulating points for customers, encouraging repeat purchases. For example, when a customer adds a product to their cart, the points management unit displays the current points balance, allowing the customer to check the points available for use for their next purchase.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The product information acquisition unit acquires product information using RFID technology. For example, the product information acquisition unit uses an RFID reader to read product information from an RFID tag attached to a product. The product information acquisition unit can also acquire product information using passive RFID or active RFID. Furthermore, the product information acquisition unit can acquire information such as product name, price, size, and color. Step 2: The information transmission unit transmits the product information acquired by the product information acquisition unit to the smartphone. For example, the information transmission unit transmits the product information to the smartphone using Bluetooth or Wi-Fi. The information transmission unit can also transmit the product information using an OS or necessary applications compatible with the smartphone. Furthermore, the information transmission unit can transmit the product information to the smartphone in real time. Step 3: The coordination suggestion unit uses the generation AI to make coordination suggestions based on the product information sent by the information sending unit. For example, the coordination suggestion unit uses the generation AI to pick out and suggest top and bottom items that go well with the product. The coordination suggestion unit can also use the generation AI to generate coordination suggestions based on the product information. Furthermore, the coordination suggestion unit can use algorithms and models to make coordination suggestions based on the product information using the generation AI.

[0046] (Example 2) A purchasing experience improvement system according to an embodiment of the present invention is a system that uses RFID technology to read an item placed in a shopping cart in an apparel store, sends the information to a smartphone attached to the shopping cart, and then a generating AI selects and suggests matching top and bottom items to go with the item. This system improves the customer's purchasing experience and increases store sales.

[0047] A purchasing experience improvement system according to an embodiment includes a product information acquisition unit, an information transmission unit, and a coordination suggestion unit. The product information acquisition unit acquires product information using RFID technology. For example, the product information acquisition unit uses an RFID reader to read product information from an RFID tag attached to a product. The product information acquisition unit can also acquire product information using passive RFID or active RFID. The product information acquisition unit can acquire information such as product name, price, size, and color. The information transmission unit transmits the product information acquired by the product information acquisition unit to a smartphone. For example, the information transmission unit transmits the product information to the smartphone using Bluetooth or Wi-Fi. The information transmission unit can transmit the product information using an operating system and necessary applications compatible with the smartphone. The information transmission unit can transmit the product information to the smartphone in real time. The coordination suggestion unit uses a generation AI to make coordination suggestions based on the product information transmitted by the information transmission unit. For example, the coordination suggestion unit uses the generation AI to select and suggest top and bottom items that match a product. The coordination suggestion unit can generate coordination suggestions based on the product information using the generation AI. Furthermore, the coordination suggestion unit can use algorithms and models to suggest coordination based on product information using the generation AI. As a result, the purchasing experience improvement system according to the embodiment can improve the customer's purchasing experience and increase store sales. For example, when a customer places an item in their cart, they can select additional items by referring to the top and bottom items suggested by the generation AI. Stores can also expect an increase in sales.

[0048] The coordination suggestion unit can make suggestions based on past purchase data or stylist coordination information. For example, the coordination suggestion unit analyzes past purchase data and makes suggestions based on data on what other products the same product has been purchased with in the past. The coordination suggestion unit can also use the stylist's coordination information to suggest coordinations recommended by the stylist. For example, it can suggest top and bottom items that go well with a product based on the coordination information suggested by the stylist. The coordination suggestion unit can also use generation AI to analyze past purchase data and stylist coordination information to suggest optimal coordinations. This makes it possible to make more attractive suggestions by utilizing past purchase data and stylist coordination information. For example, when a customer adds a product to their cart, they can select additional products by referring to the top and bottom items suggested by the generation AI based on past purchase data and stylist coordination information.

[0049] The information transmission unit can simultaneously transmit product images using the smartphone camera to add visual information. For example, when transmitting product information, the information transmission unit can take a product image using the smartphone camera and transmit the image at the same time. The information transmission unit can also take high-resolution images using the smartphone camera to provide customers with detailed product images. For example, this allows the customer to visually confirm the product's color, design, material texture, etc. The information transmission unit can also take product images using the smartphone camera and transmit the images in real time. This allows the customer to better understand product details by adding visual information. For example, when a customer adds a product to their cart, an image of the product is displayed on the smartphone screen, allowing the customer to confirm the product details based on the image.

[0050] The information transmission unit can compare the product information transmitted to the smartphone with the customer's past purchase history and make personalized suggestions. The information transmission unit, for example, builds a system that compares the product information transmitted to the smartphone with the customer's past purchase history. The information transmission unit can also analyze the customer's purchase history to make suggestions that combine products with previously purchased products. For example, the information transmission unit can suggest products of the same brand or style as products previously purchased by the customer. The information transmission unit can also make personalized suggestions based on the customer's purchase history to make suggestions based on the customer's preferences and behavioral patterns. This improves customer satisfaction by making personalized suggestions based on the customer's past purchase history. For example, when a customer adds a product to their cart, suggestions that combine products with previously purchased products can be made based on the product information transmitted to the smartphone, allowing the customer to select a product that will satisfy them more.

[0051] The coordination suggestion unit can take seasonal or weather information into account to suggest optimal coordination. For example, the coordination suggestion unit inputs seasonal information into the generation AI and suggests coordination according to the season. The coordination suggestion unit can also suggest coordination according to the weather based on the weather forecast. For example, it suggests clothes made of cool materials in summer and clothes made of warm materials in winter. The coordination suggestion unit can also use the generation AI to analyze seasonal and weather information to suggest optimal coordination. This makes it possible to suggest more practical coordination by taking seasonal and weather information into account. For example, when a customer adds an item to their cart, they can select additional items based on the top and bottom items suggested by the generation AI based on seasonal and weather information.

[0052] The outfit suggestion unit can make personalized suggestions that reflect the customer's body type and preferences. For example, the outfit suggestion unit inputs the customer's body type information, and the generation AI uses that information to suggest the optimal outfit. The outfit suggestion unit can also suggest outfits that suit the customer's tastes based on the customer's preference information. For example, suggestions are made based on the customer's favorite colors, styles, and brands. The outfit suggestion unit can also use the generation AI to analyze the customer's body type and preferences and make personalized suggestions. This makes it possible to make more personalized suggestions by reflecting the customer's body type and preferences. For example, when a customer adds items to their cart, they can select additional items by referring to the top and bottom items suggested by the generation AI based on the customer's body type and preferences.

[0053] The coordination suggestion unit can reflect reviews or ratings from other customers and make highly reliable suggestions. For example, the coordination suggestion unit collects reviews and ratings from other customers, and the generation AI makes coordination suggestions based on that information. The coordination suggestion unit can also prioritize suggestions that have received high ratings. For example, suggestions are made based on products and coordinations that other customers have given high ratings. The coordination suggestion unit can also use the generation AI to analyze reviews and ratings from other customers and make highly reliable suggestions. This makes it possible to make highly reliable suggestions by reflecting reviews and ratings from other customers. For example, when a customer adds an item to their cart, they can select additional items by referring to the top and bottom items suggested by the generation AI based on reviews and ratings from other customers.

[0054] The coordination suggestion unit can reflect the latest fashion trends and make suggestions that match the trends. For example, the coordination suggestion unit collects the latest fashion trend information, and the generation AI makes coordination suggestions based on that information. The coordination suggestion unit can also make suggestions that reflect the trend colors and styles of the current season. For example, it can suggest top and bottom items that go well with an item based on the latest fashion trends. The coordination suggestion unit can also analyze the latest fashion trends using the generation AI and make suggestions that match the trends. This makes it possible to make suggestions that match the trends to customers by reflecting the latest fashion trends. For example, when a customer adds an item to their cart, they can select additional items based on the top and bottom items suggested by the generation AI based on the latest fashion trends.

[0055] The outfit suggestion unit uses the emotion estimation function to analyze the emotions of customers when they view the proposed outfit in real time, and can suggest outfits that elicit positive emotions. For example, the outfit suggestion unit uses facial expression recognition technology to analyze emotions in real time when a customer views the proposed outfit. The outfit suggestion unit can also detect smiling or excited expressions and suggest outfits that elicit positive emotions. For example, when a customer views the proposed outfit, the emotion estimation function can analyze the customer's emotions in real time and make suggestions that elicit positive emotions. The outfit suggestion unit can also use generation AI to analyze the customer's emotions and suggest outfits that elicit positive emotions. This improves customer satisfaction by analyzing customer emotions in real time and suggesting outfits that elicit positive emotions. For example, when a customer adds an item to their cart, they can select additional items based on the top and bottom items suggested by the generation AI using the emotion estimation function.

[0056] When transmitting product information, the information transmission unit simultaneously transmits store inventory information, allowing the unit to make suggestions to avoid out-of-stock products. The information transmission unit, for example, builds a system that transmits store inventory information to a smartphone along with product information. The information transmission unit can also update store inventory information in real time to make suggestions to avoid out-of-stock products. For example, when the inventory quantity is zero or below a certain quantity, the information transmission unit can make suggestions to avoid out-of-stock products. The information transmission unit can also suggest only in-stock products based on the inventory information. This improves the customer's purchasing experience by making suggestions to avoid out-of-stock products. For example, when a customer places a product in their cart, only in-stock products can be suggested based on the product information transmitted to the smartphone, preventing the customer from selecting an out-of-stock product.

[0057] The information transmission unit can use the emotion estimation function to analyze the emotions of customers in real time when they check product information on their smartphones and display information that elicits positive emotions. For example, the information transmission unit can use facial expression recognition technology to analyze emotions in real time when customers check product information on their smartphones. The information transmission unit can also detect smiling or excited facial expressions and display positive information. For example, when a customer checks product information on their smartphones, the emotion estimation function can analyze the customer's emotions in real time and display information that elicits positive emotions. The information transmission unit can also use a generation AI to analyze the customer's emotions and display information that elicits positive emotions. This improves customer satisfaction by analyzing customer emotions in real time and displaying information that elicits positive emotions. For example, when a customer adds a product to their cart, the generation AI can use the emotion estimation function to display positive information when checking product information on their smartphones, allowing the customer to select a product that will satisfy them more.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The purchasing experience improvement system can further include a voice assistant unit. When a customer places an item in the cart, the voice assistant unit can provide product information and coordination suggestions by voice. For example, the voice assistant unit can explain the product's material, washing instructions, and coordination tips by voice. The voice assistant unit can also answer customer questions in real time. This can further improve the customer's purchasing experience by providing not only visual information but also auditory information. For example, when a customer places an item in the cart, the voice assistant can explain detailed product information, allowing the customer to select a product based on that information.

[0060] The purchasing experience improvement system can further include a feedback collection unit. The feedback collection unit can collect impressions and evaluations of products purchased by customers after they have used them. For example, the feedback collection unit can send a questionnaire to customers about their experience using the product and their level of satisfaction after a certain period of time has passed since purchase. The feedback collection unit can also analyze the collected feedback and reflect it in future outfit suggestions. This makes it possible to make suggestions based on the customer's actual experience using the product, resulting in more accurate outfit suggestions. For example, when a customer adds a product to their cart, they can select additional products by referring to suggestions based on past feedback.

[0061] The shopping experience improvement system can further include a virtual fitting section. The virtual fitting section allows customers to virtually try on selected products using a smartphone camera. For example, the virtual fitting section recognizes the customer's body type and facial features and displays a virtual image of the customer wearing the selected products on the smartphone screen. The virtual fitting section can also virtually try on outfits that combine multiple products. This allows customers to check the fit and appearance of products without actually trying them on. For example, when a customer adds products to their shopping cart, they can try on the products using the virtual fitting section and select products based on the results.

[0062] The purchasing experience improvement system can further include a social sharing unit. The social sharing unit can share products and outfits selected by customers on social media. For example, the social sharing unit provides a function for customers to post images of their selected products and outfits on social media. The social sharing unit can also collect comments and ratings from the customer's friends and followers and reflect them in outfit suggestions. This allows customers to select products while taking into consideration the opinions of their friends and followers. For example, when a customer adds a product to their cart, they can use the social sharing unit to share the selected product on social media, allowing them to select products based on the opinions of their friends and followers.

[0063] The purchasing experience improvement system may further include a points management unit. The points management unit awards points each time a customer purchases a product, allowing the customer to use the points for their next purchase. For example, the points management unit awards points according to the purchase amount when a customer purchases a product. The points management unit may also allow the customer to use the points they have accumulated as a discount for their next purchase. This increases the enjoyment of accumulating points for customers, encouraging repeat purchases. For example, when a customer adds a product to their cart, the points management unit displays the current points balance, allowing the customer to check the points available for use for their next purchase.

[0064] The outfit suggestion unit estimates the customer's emotions and, based on the estimated emotions, suggests a casual outfit if the customer is relaxed and a formal outfit if the customer is nervous. For example, if the customer is relaxed, casual clothing that suits the relaxed atmosphere can be suggested. Conversely, if the customer is nervous, clothing suitable for a formal occasion can be suggested. This makes it possible to suggest outfits according to the customer's emotions, resulting in more personalized suggestions. For example, when a customer adds an item to their cart, the emotion estimation function can be used to analyze the customer's emotions and suggest outfits based on those emotions.

[0065] The information transmission unit can use the emotion estimation function to analyze the emotions of customers in real time when checking product information on their smartphones and display information that elicits positive emotions. For example, when a customer checks product information on their smartphone, the emotion is analyzed in real time using facial expression recognition technology. The information transmission unit can also detect smiling or excited facial expressions and display positive information. For example, when a customer checks product information on their smartphone, the emotion estimation function can analyze the customer's emotions in real time and display information that elicits positive emotions. The information transmission unit can also use the generation AI to analyze the customer's emotions and display information that elicits positive emotions. This improves customer satisfaction by analyzing customer emotions in real time and displaying information that elicits positive emotions. For example, when a customer adds a product to their cart, the generation AI can use the emotion estimation function to display positive information when checking product information on their smartphone, allowing the customer to select a product that will satisfy them more.

[0066] The outfit suggestion unit uses the emotion estimation function to analyze the emotions of customers when they view the proposed outfit in real time, and can suggest outfits that elicit positive emotions. For example, when a customer views the proposed outfit, it uses facial expression recognition technology to analyze their emotions in real time. The outfit suggestion unit can also detect smiling or excited expressions and suggest outfits that elicit positive emotions. For example, when a customer views the proposed outfit, it uses the emotion estimation function to analyze the customer's emotions in real time and makes suggestions that elicit positive emotions. The outfit suggestion unit can also use generation AI to analyze the customer's emotions and suggest outfits that elicit positive emotions. This improves customer satisfaction by analyzing customer emotions in real time and suggesting outfits that elicit positive emotions. For example, when a customer adds an item to their cart, they can select additional items based on the top and bottom items suggested by the generation AI using the emotion estimation function.

[0067] The information transmission unit can use the emotion estimation function to analyze the emotions of customers in real time when checking product information on their smartphones and display information that elicits positive emotions. For example, when a customer checks product information on their smartphone, the emotion is analyzed in real time using facial expression recognition technology. The information transmission unit can also detect smiling or excited facial expressions and display positive information. For example, when a customer checks product information on their smartphone, the emotion estimation function can analyze the customer's emotions in real time and display information that elicits positive emotions. The information transmission unit can also use the generation AI to analyze the customer's emotions and display information that elicits positive emotions. This improves customer satisfaction by analyzing customer emotions in real time and displaying information that elicits positive emotions. For example, when a customer adds a product to their cart, the generation AI can use the emotion estimation function to display positive information when checking product information on their smartphone, allowing the customer to select a product that will satisfy them more.

[0068] The outfit suggestion unit uses the emotion estimation function to analyze the emotions of customers when they view the proposed outfit in real time, and can suggest outfits that elicit positive emotions. For example, when a customer views the proposed outfit, it uses facial expression recognition technology to analyze their emotions in real time. The outfit suggestion unit can also detect smiling or excited expressions and suggest outfits that elicit positive emotions. For example, when a customer views the proposed outfit, it uses the emotion estimation function to analyze the customer's emotions in real time and makes suggestions that elicit positive emotions. The outfit suggestion unit can also use generation AI to analyze the customer's emotions and suggest outfits that elicit positive emotions. This improves customer satisfaction by analyzing customer emotions in real time and suggesting outfits that elicit positive emotions. For example, when a customer adds an item to their cart, they can select additional items based on the top and bottom items suggested by the generation AI using the emotion estimation function.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The product information acquisition unit acquires product information using RFID technology. For example, the product information acquisition unit uses an RFID reader to read product information from an RFID tag attached to a product. The product information acquisition unit can also acquire product information using passive RFID or active RFID. Furthermore, the product information acquisition unit can acquire information such as product name, price, size, and color. Step 2: The information transmission unit transmits the product information acquired by the product information acquisition unit to the smartphone. For example, the information transmission unit transmits the product information to the smartphone using Bluetooth or Wi-Fi. The information transmission unit can also transmit the product information using an OS or necessary applications compatible with the smartphone. Furthermore, the information transmission unit can transmit the product information to the smartphone in real time. Step 3: The coordination suggestion unit uses the generation AI to make coordination suggestions based on the product information sent by the information sending unit. For example, the coordination suggestion unit uses the generation AI to pick out and suggest top and bottom items that go well with the product. The coordination suggestion unit can also use the generation AI to generate coordination suggestions based on the product information. Furthermore, the coordination suggestion unit can use algorithms and models to make coordination suggestions based on the product information using the generation AI.

[0071] 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0119] The data processing system 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a product information acquisition unit that acquires product information using RFID technology; an information transmitting unit that transmits the product information acquired by the product information acquiring unit to a smartphone; a coordinate suggestion unit that suggests a coordinate using a generation AI based on the product information transmitted by the information transmission unit. A system characterized by:

2. The information transmission unit Using a smartphone camera, images of the product are also sent simultaneously to add visual information.

2. The system of claim 1.

3. The coordination suggestion unit Taking into account seasonal or weather information, we suggest the best outfits 2. The system of claim 1.

4. The information transmission unit Product information can be sent not only to smartphones but also to smartwatches and AR glasses, allowing users to view information on multiple devices.

2. The system of claim 1.

5. The coordination suggestion unit Analyzes the emotions customers feel when they see suggested outfits in real time and suggests outfits that evoke positive emotions 2. The system of claim 1.

Images