system

Abstract

An object of the system according to the embodiment is to realize efficient production and sales in the apparel industry.SOLUTION: A system includes a demand prediction unit, a design generation unit, and an EC operation unit. The demand prediction unit performs demand prediction using the generated AI. The design generation unit automatically generates a design or a pattern on the basis of the demand predicted by the demand prediction unit. The EC operation unit automates the operation of the EC site based on the design and the pattern generated by the design generation unit.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, the apparel industry faced issues such as overproduction, mass waste, and labor shortages, making it difficult to produce and sell efficiently.

[0005] The system according to the embodiment aims to realize efficient production and sales in the apparel industry. [Means for solving the problem]

[0006] The system according to the embodiment includes a demand forecasting unit, a design generation unit, and an EC operation unit. The demand forecasting unit performs demand forecasting using a generation AI. The design generation unit automatically generates designs and patterns based on the demand predicted by the demand forecasting unit. The EC operation unit automates the operation of the EC site based on the designs and patterns generated by the design generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can realize efficient production and sales in the apparel industry. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The apparel value chain system according to an embodiment of the present invention utilizes generative AI to realize demand forecasting, automatic design / pattern generation, automated e-commerce operations, virtual customer service, and virtual try-on. As a result, the apparel value chain system can solve problems such as overproduction, mass waste, labor shortages, and supply chain issues, thereby redefining the industry.

[0029] An apparel value chain system according to an embodiment includes a demand forecasting unit, a design generation unit, and an e-commerce operations unit. The demand forecasting unit performs demand forecasting using a generation AI. For example, the generation AI analyzes past sales data, market trends, and consumer purchasing behavior data to predict future demand. The generation AI also predicts what designs will be popular in the next season and can create appropriate production plans based on the results. The design generation unit automatically generates designs and patterns based on the demand predicted by the demand forecasting unit. For example, the generation AI generates designs and patterns based on consumer needs and trends. When instructed to generate a casual yet elegant design, the generation AI can generate specific designs and patterns based on the instructions. The e-commerce operations unit automates the operation of an e-commerce site based on the designs and patterns generated by the design generation unit. For example, the generation AI optimizes inventory management, pricing, and promotions. The generation AI can optimize the operation of an e-commerce site based on inventory data and prompts related to pricing. As a result, the apparel value chain system according to an embodiment can solve challenges in the apparel industry and achieve efficient production and sales.

[0030] The demand forecasting unit can analyze social media trends and influencer posts and update the demand forecast in real time. For example, the generation AI in the demand forecasting unit analyzes social media trends and makes a demand forecast based on popular hashtags and keywords. For example, if a specific trend is rapidly rising, the demand for products related to that trend is predicted. The demand forecasting unit also analyzes influencer posts and makes a demand forecast taking their influence into account. For example, it can predict demand based on the reaction when an influencer introduces a specific product. The demand forecasting unit also collects real-time social media data, and the generation AI updates the demand forecast based on that data. For example, it can analyze the impact of specific events or news on consumer purchasing behavior and reflect this in the demand forecast. This makes it possible to make demand forecasts based on social media trends.

[0031] The demand forecasting unit can analyze regional climate data and predict seasonal demand fluctuations. For example, the generation AI in the demand forecasting unit collects regional climate data and makes demand forecasts based on seasonal temperatures and precipitation. For example, it predicts periods when demand for winter clothing will increase in cold regions. The demand forecasting unit also combines and analyzes climate data with past sales data to predict demand fluctuations under specific climate conditions. For example, it can predict demand for raincoats and waterproof items during the rainy season. The demand forecasting unit also collects climate data in real time, and the generation AI updates the demand forecast based on that data. For example, it can predict demand fluctuations when abnormal weather occurs and respond quickly. This makes it possible to make demand forecasts based on regional climate data.

[0032] The design generation unit can analyze data from past fashion shows, predict trends, and generate designs. For example, the design generation unit uses a generation AI to collect data from past fashion shows and analyze trends. For example, it predicts when a particular design or pattern will be popular and generates that design. The design generation unit also combines and analyzes fashion show data with market trends, and the generation AI generates designs based on that data. For example, it can predict future trends based on past trends and reflect them in the design. The design generation unit also collects fashion show data in real time, and the generation AI generates designs based on that data. For example, it can predict design changes when new trends emerge and respond quickly. This makes it possible to generate designs based on data from past fashion shows.

[0033] The design generation unit can analyze different cultures and historical backgrounds and generate designs based on them. For example, the design generation unit's generation AI collects data on different cultures and generates designs based on those cultures. For example, it generates designs related to a specific culture or tradition. The design generation unit can also analyze historical backgrounds and the generation AI generates designs based on that data. For example, it can generate designs related to a specific era or event. The design generation unit can also collect data on different cultures and historical backgrounds in real time and the generation AI generates designs based on that data. For example, it can predict design changes when new cultural trends emerge and respond quickly. This makes it possible to generate designs based on different cultures and historical backgrounds.

[0034] The EC Operations Department can analyze competitors' pricing data in real time and set optimal prices. For example, the EC Operations Department's generation AI collects price data from competitors' EC sites and analyzes it in real time. For example, the EC Operations Department adjusts the prices of its own products in response to competitors' price fluctuations. The EC Operations Department also combines competitors' price data with market trends for analysis, and the generation AI sets optimal prices based on that data. For example, prices can be raised when demand for a particular product increases. The EC Operations Department also collects competitors' price data in real time, and the generation AI updates pricing based on that data. For example, when a competitor starts a large-scale sale, the company can make similar price adjustments. This makes it possible to set optimal prices based on competitors' price data.

[0035] The EC Operations Department can perform optimal inventory management by combining consumer purchase history and inventory data. For example, the generation AI in the EC Operations Department analyzes consumer purchase history and predicts how much of a particular product will be sold and when. For example, inventory is optimized based on past data. The EC Operations Department also combines and analyzes inventory data and purchase history, and the generation AI manages inventory based on that data. For example, a specific product can be automatically reordered before it sells out. The EC Operations Department also collects consumer purchase history and inventory data in real time, and the generation AI updates inventory management based on that data. For example, inventory can be replenished quickly when demand suddenly increases. This enables optimal inventory management based on consumer purchase history and inventory data.

[0036] The EC operations department can analyze EC operation data from different industries and apply it to apparel EC operations. For example, the generation AI in the EC operations department analyzes EC operation data from the food industry to extract patterns of consumer purchasing behavior. For example, apparel products can be promoted based on seasonal increases in food demand. The EC operations department can also analyze EC operation data from the electronics industry to promote apparel products based on technological trends and consumer interests. For example, apparel products can be promoted in relation to the release of new gadgets. The EC operations department can also integrate EC operation data from different industries, and the generation AI can use that data to perform apparel EC operations. For example, apparel product promotions can be carried out comprehensively, taking into account trends from multiple industries. This makes it possible to perform apparel EC operations based on EC operation data from different industries.

[0037] The EC operations department can analyze consumer lifestyle data and run promotions that suit their lifestyles. For example, the EC operations department has a generation AI collect consumer lifestyle data and run promotions based on that data. For example, it can promote clothing that suits active lifestyles. The EC operations department can also combine and analyze lifestyle data and market trends, and the generation AI can run promotions based on that data. For example, it can run promotions that reflect trends related to specific lifestyles. The EC operations department can also collect lifestyle data in real time, and the generation AI can run promotions based on that data. For example, it can quickly run promotions when new lifestyle trends emerge. This makes it possible to run promotions based on consumers' lifestyles.

[0038] The virtual customer service department can combine a consumer's purchase history and social media data to make personalized product suggestions. For example, the generation AI analyzes a consumer's purchase history and makes personalized product suggestions based on previously purchased products. For example, it can suggest products from the same brand or style. The virtual customer service department can also analyze social media data and make personalized product suggestions based on the consumer's interests. For example, it can suggest products taking into account the influence of specific trends or influencers. The virtual customer service department also collects purchase history and social media data in real time, and the generation AI updates its product suggestions based on that data. For example, it can respond quickly when new trends emerge. This makes it possible to make personalized product suggestions based on a consumer's purchase history and social media data.

[0039] The virtual customer service unit can analyze consumers' lifestyle data and make product suggestions that fit their lifestyle. For example, the generation AI in the virtual customer service unit collects consumers' lifestyle data and makes product suggestions based on that data. For example, it can suggest clothing that is suitable for an active lifestyle. The virtual customer service unit can also combine and analyze lifestyle data and market trends, and the generation AI can make product suggestions based on that data. For example, it can suggest products that reflect trends related to specific lifestyles. The virtual customer service unit can also collect lifestyle data in real time, and the generation AI can update its product suggestions based on that data. For example, it can respond quickly when new lifestyle trends emerge. This makes it possible to make product suggestions based on consumers' lifestyles.

[0040] The virtual customer service unit can analyze customer service data from different industries and apply it to apparel customer service. For example, the virtual customer service unit's generative AI analyzes customer service data from the food industry to extract patterns of consumer purchasing behavior. For example, it can suggest apparel products based on seasonal increases in food demand. The virtual customer service unit can also analyze customer service data from the electronics industry to suggest apparel products based on technology trends and consumer interests. For example, it can suggest apparel products related to the release dates of new gadgets. The virtual customer service unit can also integrate customer service data from different industries, and the generative AI can use that data to provide apparel customer service. For example, it can take trends from multiple industries into account to comprehensively suggest apparel products. This makes it possible to provide apparel customer service based on customer service data from different industries.

[0041] The virtual customer service department can analyze consumers' health data and make product suggestions for health-conscious consumers. For example, the virtual customer service department's generation AI collects consumers' health data and makes product suggestions for health-conscious consumers based on that data. For example, it can suggest fitness wear or clothing made from organic materials. The virtual customer service department can also combine and analyze health data with market trends, and the generation AI can make product suggestions based on that data. For example, it can suggest products related to specific health trends. The virtual customer service department can also collect health data in real time, and the generation AI can update product suggestions based on that data. For example, it can respond quickly when new health trends emerge. This makes it possible to make product suggestions based on consumers' health data.

[0042] The virtual fitting room can combine a consumer's body type data with past fitting data to provide an optimal virtual try-on experience. In the virtual fitting room, for example, the generation AI collects the consumer's body type data and combines it with past fitting data for analysis. For example, it can suggest clothing that is best suited to a specific body type. In addition, the virtual fitting room can use the generation AI to provide an optimal virtual try-on experience based on the body type data and fitting data. For example, it can simulate a fit tailored to the consumer's body type. In addition, the virtual fitting room collects body type data and fitting data in real time, and the generation AI updates the virtual try-on experience based on that data. For example, it can respond quickly when new fitting data is added. This enables an optimal virtual try-on experience based on the consumer's body type data and past fitting data.

[0043] The virtual fitting room can simulate different lighting conditions and backgrounds to provide a realistic virtual try-on experience. For example, the generation AI simulates different lighting conditions to recreate how clothes will look when actually being tried on by a consumer. For example, it can provide a try-on experience that takes indoor and outdoor lighting conditions into account. The virtual fitting room can also simulate different backgrounds, and the generation AI uses that data to provide a virtual try-on experience. For example, it can set a background that matches a specific location or event. The virtual fitting room also collects lighting condition and background data in real time, and the generation AI updates the virtual try-on experience based on that data. For example, it can quickly respond when new lighting conditions or backgrounds are added. This enables a realistic virtual try-on experience based on different lighting conditions and backgrounds.

[0044] The virtual try-on unit can analyze virtual experience data from different industries and apply it to virtual apparel try-on. For example, in the virtual try-on unit, the generation AI analyzes virtual experience data from interior design and applies the design elements to virtual apparel try-on. For example, specific colors or patterns can be incorporated into the try-on experience. The virtual try-on unit can also analyze virtual experience data from automobile design, and the generation AI can provide a virtual apparel try-on based on that data. For example, specific shapes or materials can be applied to the try-on experience. The virtual try-on unit can also integrate virtual experience data from different industries, and the generation AI can provide a virtual apparel try-on based on that data. For example, it can provide a try-on experience that combines design elements from multiple industries. This makes it possible to perform virtual apparel try-on based on virtual experience data from different industries.

[0045] The virtual fitting room can analyze a consumer's lifestyle data and provide a virtual fitting experience that suits that lifestyle. For example, in the virtual fitting room, a generation AI collects a consumer's lifestyle data and provides a virtual fitting experience based on that data. For example, a fitting experience of clothing suitable for an active lifestyle can be provided. The virtual fitting room can also combine and analyze lifestyle data and market trends, and a generation AI can provide a virtual fitting experience based on that data. For example, a fitting experience that reflects trends related to a specific lifestyle can be provided. The virtual fitting room can also collect lifestyle data in real time, and a generation AI can update the virtual fitting experience based on that data. For example, it can respond quickly when new lifestyle trends emerge. This makes it possible to provide a virtual fitting experience based on the consumer's lifestyle.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The demand forecasting unit can combine consumer purchasing history and weather data to predict demand under specific weather conditions. For example, it can predict that demand for raincoats and waterproof shoes will increase on rainy days and adjust inventory accordingly. The demand forecasting unit can also predict demand for winter and summer items, taking into account seasonal temperature fluctuations. Furthermore, the demand forecasting unit can analyze demand fluctuations in real time when abnormal weather occurs and respond quickly. This makes it possible to forecast demand based on consumer purchasing history and weather data.

[0048] The design generation unit can generate personalized designs by combining consumer purchasing history and social media trend data. For example, for a consumer who prefers a particular brand or style, it can generate a design based on that brand or style. The design generation unit can also analyze social media trends and generate designs that incorporate popular design elements. Furthermore, the design generation unit collects consumer purchasing history and trend data in real time, allowing the generation AI to update designs based on that data. This makes it possible to generate personalized designs based on consumer purchasing history and trend data.

[0049] The EC Operations Department can combine consumer purchase history and inventory data to perform optimal inventory management. For example, it can use past data to predict how much of a particular product will sell at what time, optimizing inventory. The EC Operations Department can also combine and analyze inventory data and purchase history to automatically reorder specific products before they sell out. Furthermore, the EC Operations Department can collect consumer purchase history and inventory data in real time, and the generation AI can update inventory management based on that data. This enables optimal inventory management based on consumer purchase history and inventory data.

[0050] The virtual customer service department can analyze consumers' lifestyle data and make product suggestions that fit their lifestyle. For example, it can suggest sportswear and outdoor gear to consumers with active lifestyles. The virtual customer service department can also combine lifestyle data with market trends and analyze them to make product suggestions that reflect trends related to specific lifestyles. Furthermore, the virtual customer service department can collect lifestyle data in real time, and the generation AI can update product suggestions based on that data. This makes it possible to make product suggestions based on consumers' lifestyles.

[0051] The virtual fitting room can combine a consumer's body type data with past fitting data to provide an optimal virtual fitting experience. For example, it can suggest clothing that is best suited to a specific body type and simulate how it will fit when the consumer actually tries it on. The virtual fitting room can also use generation AI to provide an optimal virtual fitting experience based on the body type data and fitting data. Furthermore, the virtual fitting room can collect body type data and fitting data in real time, and the generation AI can update the virtual fitting experience based on that data. This enables an optimal virtual fitting experience based on the consumer's body type data and past fitting data.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The demand forecasting department uses generative AI to make demand forecasts. Generative AI analyzes past sales data, market trends, and consumer purchasing behavior data to predict future demand. It can also predict what designs will be popular in the next season and create appropriate production plans based on the results. Step 2: The design generation unit automatically generates designs and patterns based on the demand predicted by the demand forecasting unit. The generation AI generates designs and patterns based on consumer needs and trends, and when instructed to generate a casual yet elegant design, it can generate specific designs and patterns based on that instruction. Step 3: The E-commerce Operations Department automates the operation of the E-commerce website based on the designs and patterns generated by the Design Generation Department. The Generative AI can optimize inventory management, pricing, and promotions, and can optimize the operation of the E-commerce website based on inventory data and pricing prompts.

[0054] (Example 2) The apparel value chain system according to an embodiment of the present invention utilizes generative AI to realize demand forecasting, automatic design / pattern generation, automated e-commerce operations, virtual customer service, and virtual try-on. As a result, the apparel value chain system can solve problems such as overproduction, mass waste, labor shortages, and supply chain issues, thereby redefining the industry.

[0055] An apparel value chain system according to an embodiment includes a demand forecasting unit, a design generation unit, and an e-commerce operations unit. The demand forecasting unit performs demand forecasting using a generation AI. For example, the generation AI analyzes past sales data, market trends, and consumer purchasing behavior data to predict future demand. The generation AI also predicts what designs will be popular in the next season and can create appropriate production plans based on the results. The design generation unit automatically generates designs and patterns based on the demand predicted by the demand forecasting unit. For example, the generation AI generates designs and patterns based on consumer needs and trends. When instructed to generate a casual yet elegant design, the generation AI can generate specific designs and patterns based on the instructions. The e-commerce operations unit automates the operation of an e-commerce site based on the designs and patterns generated by the design generation unit. For example, the generation AI optimizes inventory management, pricing, and promotions. The generation AI can optimize the operation of an e-commerce site based on inventory data and prompts related to pricing. As a result, the apparel value chain system according to an embodiment can solve challenges in the apparel industry and achieve efficient production and sales.

[0056] The demand forecasting unit can analyze consumer emotional data and make demand forecasts based on emotional fluctuations. For example, the generation AI analyzes social media posts and reviews to collect consumer emotional data. For example, it identifies products and trends that have a high proportion of positive emotions and makes demand forecasts based on that data. The demand forecasting unit also analyzes consumer purchasing history and emotional data in combination to predict demand based on periods and events when specific emotions are strongly expressed. For example, it can predict demand taking into account seasonal emotional fluctuations. The demand forecasting unit also collects emotional data in real time, and the generation AI updates the demand forecast based on that data. For example, it can analyze the impact of specific events or campaigns on consumer emotions and reflect this in the demand forecast. This makes it possible to make demand forecasts based on consumer emotions.

[0057] The demand forecasting unit can analyze social media trends and influencer posts and update the demand forecast in real time. For example, the generation AI in the demand forecasting unit analyzes social media trends and makes a demand forecast based on popular hashtags and keywords. For example, if a specific trend is rapidly rising, the demand for products related to that trend is predicted. The demand forecasting unit also analyzes influencer posts and makes a demand forecast taking their influence into account. For example, it can predict demand based on the reaction when an influencer introduces a specific product. The demand forecasting unit also collects real-time social media data, and the generation AI updates the demand forecast based on that data. For example, it can analyze the impact of specific events or news on consumer purchasing behavior and reflect this in the demand forecast. This makes it possible to make demand forecasts based on social media trends.

[0058] The demand forecasting unit can analyze regional climate data and predict seasonal demand fluctuations. For example, the generation AI in the demand forecasting unit collects regional climate data and makes demand forecasts based on seasonal temperatures and precipitation. For example, it predicts periods when demand for winter clothing will increase in cold regions. The demand forecasting unit also combines and analyzes climate data with past sales data to predict demand fluctuations under specific climate conditions. For example, it can predict demand for raincoats and waterproof items during the rainy season. The demand forecasting unit also collects climate data in real time, and the generation AI updates the demand forecast based on that data. For example, it can predict demand fluctuations when abnormal weather occurs and respond quickly. This makes it possible to make demand forecasts based on regional climate data.

[0059] The design generation unit can analyze consumer emotional data and generate designs and patterns based on those emotions. For example, the design generation unit uses a generation AI to analyze social media posts and reviews to collect consumer emotional data. For example, it can generate designs and patterns that emphasize positive emotions. The design generation unit can also combine and analyze consumer purchasing history and emotional data to generate designs and patterns that strongly express specific emotions. For example, it can generate designs that take seasonal emotional fluctuations into consideration. The design generation unit can also collect emotional data in real time, and the generation AI can generate designs and patterns based on that data. For example, it can analyze the impact of specific events or campaigns on consumer emotions and reflect that in the design. This makes it possible to generate designs and patterns based on consumer emotions.

[0060] The design generation unit can analyze data from past fashion shows, predict trends, and generate designs. For example, the design generation unit uses a generation AI to collect data from past fashion shows and analyze trends. For example, it predicts when a particular design or pattern will be popular and generates that design. The design generation unit also combines and analyzes fashion show data with market trends, and the generation AI generates designs based on that data. For example, it can predict future trends based on past trends and reflect them in the design. The design generation unit also collects fashion show data in real time, and the generation AI generates designs based on that data. For example, it can predict design changes when new trends emerge and respond quickly. This makes it possible to generate designs based on data from past fashion shows.

[0061] The design generation unit can analyze different cultures and historical backgrounds and generate designs based on them. For example, the design generation unit's generation AI collects data on different cultures and generates designs based on those cultures. For example, it generates designs related to a specific culture or tradition. The design generation unit can also analyze historical backgrounds and the generation AI generates designs based on that data. For example, it can generate designs related to a specific era or event. The design generation unit can also collect data on different cultures and historical backgrounds in real time and the generation AI generates designs based on that data. For example, it can predict design changes when new cultural trends emerge and respond quickly. This makes it possible to generate designs based on different cultures and historical backgrounds.

[0062] The EC operations department can analyze consumer sentiment data and set prices and promotions based on those sentiments. For example, the EC operations department uses a generation AI to analyze social media posts and reviews to collect consumer sentiment data. For example, the EC operations department can raise prices or strengthen promotions for products with a high level of positive sentiment. The EC operations department can also combine and analyze consumer purchasing history and sentiment data to set prices and promotions based on periods or events when specific emotions are most strongly expressed. For example, prices can be adjusted taking into account seasonal emotional fluctuations. The EC operations department can also collect sentiment data in real time, and the generation AI can use that data to update pricing and promotions. For example, the impact of specific events or campaigns on consumer sentiment can be analyzed and reflected in prices and promotions. This makes it possible to set prices and promotions based on consumer sentiment.

[0063] The EC Operations Department can analyze competitors' pricing data in real time and set optimal prices. For example, the EC Operations Department's generation AI collects price data from competitors' EC sites and analyzes it in real time. For example, the EC Operations Department adjusts the prices of its own products in response to competitors' price fluctuations. The EC Operations Department also combines competitors' price data with market trends for analysis, and the generation AI sets optimal prices based on that data. For example, prices can be raised when demand for a particular product increases. The EC Operations Department also collects competitors' price data in real time, and the generation AI updates pricing based on that data. For example, when a competitor starts a large-scale sale, the company can make similar price adjustments. This makes it possible to set optimal prices based on competitors' price data.

[0064] The EC Operations Department can perform optimal inventory management by combining consumer purchase history and inventory data. For example, the generation AI in the EC Operations Department analyzes consumer purchase history and predicts how much of a particular product will be sold and when. For example, inventory is optimized based on past data. The EC Operations Department also combines and analyzes inventory data and purchase history, and the generation AI manages inventory based on that data. For example, a specific product can be automatically reordered before it sells out. The EC Operations Department also collects consumer purchase history and inventory data in real time, and the generation AI updates inventory management based on that data. For example, inventory can be replenished quickly when demand suddenly increases. This enables optimal inventory management based on consumer purchase history and inventory data.

[0065] The EC operations department can analyze EC operation data from different industries and apply it to apparel EC operations. For example, the generation AI in the EC operations department analyzes EC operation data from the food industry to extract patterns of consumer purchasing behavior. For example, apparel products can be promoted based on seasonal increases in food demand. The EC operations department can also analyze EC operation data from the electronics industry to promote apparel products based on technological trends and consumer interests. For example, apparel products can be promoted in relation to the release of new gadgets. The EC operations department can also integrate EC operation data from different industries, and the generation AI can use that data to perform apparel EC operations. For example, apparel product promotions can be carried out comprehensively, taking into account trends from multiple industries. This makes it possible to perform apparel EC operations based on EC operation data from different industries.

[0066] The EC operations department can analyze consumer lifestyle data and run promotions that suit their lifestyles. For example, the EC operations department has a generation AI collect consumer lifestyle data and run promotions based on that data. For example, it can promote clothing that suits active lifestyles. The EC operations department can also combine and analyze lifestyle data and market trends, and the generation AI can run promotions based on that data. For example, it can run promotions that reflect trends related to specific lifestyles. The EC operations department can also collect lifestyle data in real time, and the generation AI can run promotions based on that data. For example, it can quickly run promotions when new lifestyle trends emerge. This makes it possible to run promotions based on consumers' lifestyles.

[0067] The EC operations department can use the emotion estimation function to perform EC operations based on consumer emotions and identify promotions that elicit positive emotional responses. For example, the EC operations department can use the emotion estimation function to analyze consumer emotion data and identify promotions that elicit strong positive emotions. For example, it can prioritize promotions that elicit joy and satisfaction. The EC operations department can also use the consumer emotion data to perform promotions based on times and events when specific emotions are most strongly expressed. For example, it can perform promotions that take seasonal emotional fluctuations into consideration. The EC operations department can also collect emotion data in real time, and the generation AI can update promotions based on that data. For example, it can analyze the impact of specific events or campaigns on consumer emotions and reflect that in promotions. This makes it possible to perform EC operations based on consumer emotions.

[0068] The virtual customer service department can analyze consumer emotional data and make product suggestions based on those emotions. For example, the virtual customer service department uses a generation AI to analyze social media posts and reviews to collect consumer emotional data. For example, it can prioritize suggestions of products with a high percentage of positive emotions. The virtual customer service department can also combine and analyze a consumer's purchasing history and emotional data to suggest products that strongly express certain emotions. For example, it can suggest products that take seasonal emotional fluctuations into account. The virtual customer service department can also collect emotional data in real time, and the generation AI can update product suggestions based on that data. For example, it can analyze the impact of specific events or campaigns on consumer emotions and reflect that in product suggestions. This makes it possible to suggest products based on consumer emotions.

[0069] The virtual customer service department can combine a consumer's purchase history and social media data to make personalized product suggestions. For example, the generation AI analyzes a consumer's purchase history and makes personalized product suggestions based on previously purchased products. For example, it can suggest products from the same brand or style. The virtual customer service department can also analyze social media data and make personalized product suggestions based on the consumer's interests. For example, it can suggest products taking into account the influence of specific trends or influencers. The virtual customer service department also collects purchase history and social media data in real time, and the generation AI updates its product suggestions based on that data. For example, it can respond quickly when new trends emerge. This makes it possible to make personalized product suggestions based on a consumer's purchase history and social media data.

[0070] The virtual customer service unit can analyze consumers' lifestyle data and make product suggestions that fit their lifestyle. For example, the generation AI in the virtual customer service unit collects consumers' lifestyle data and makes product suggestions based on that data. For example, it can suggest clothing that is suitable for an active lifestyle. The virtual customer service unit can also combine and analyze lifestyle data and market trends, and the generation AI can make product suggestions based on that data. For example, it can suggest products that reflect trends related to specific lifestyles. The virtual customer service unit can also collect lifestyle data in real time, and the generation AI can update its product suggestions based on that data. For example, it can respond quickly when new lifestyle trends emerge. This makes it possible to make product suggestions based on consumers' lifestyles.

[0071] The virtual customer service unit can analyze customer service data from different industries and apply it to apparel customer service. For example, the virtual customer service unit's generative AI analyzes customer service data from the food industry to extract patterns of consumer purchasing behavior. For example, it can suggest apparel products based on seasonal increases in food demand. The virtual customer service unit can also analyze customer service data from the electronics industry to suggest apparel products based on technology trends and consumer interests. For example, it can suggest apparel products related to the release dates of new gadgets. The virtual customer service unit can also integrate customer service data from different industries, and the generative AI can use that data to provide apparel customer service. For example, it can take trends from multiple industries into account to comprehensively suggest apparel products. This makes it possible to provide apparel customer service based on customer service data from different industries.

[0072] The virtual customer service department can analyze consumers' health data and make product suggestions for health-conscious consumers. For example, the virtual customer service department's generation AI collects consumers' health data and makes product suggestions for health-conscious consumers based on that data. For example, it can suggest fitness wear or clothing made from organic materials. The virtual customer service department can also combine and analyze health data with market trends, and the generation AI can make product suggestions based on that data. For example, it can suggest products related to specific health trends. The virtual customer service department can also collect health data in real time, and the generation AI can update product suggestions based on that data. For example, it can respond quickly when new health trends emerge. This makes it possible to make product suggestions based on consumers' health data.

[0073] The virtual customer service unit uses the emotion estimation function to provide virtual customer service based on the consumer's emotions and identify product proposals that elicit a positive emotional response. For example, the virtual customer service unit uses the emotion estimation function to analyze the consumer's emotional data and identify products that evoke strong positive emotions. For example, it prioritizes the proposal of products that elicit joy and satisfaction. The virtual customer service unit also proposes products that strongly express specific emotions based on the consumer's emotional data. For example, it can propose products that take into account seasonal emotional fluctuations. The virtual customer service unit also collects emotional data in real time, and the generation AI updates product proposals based on that data. For example, it can analyze the impact of specific events or campaigns on consumer emotions and reflect that in product proposals. This makes it possible to provide virtual customer service based on the consumer's emotions.

[0074] The virtual fitting room can analyze consumer emotional data and provide a virtual fitting experience based on those emotions. For example, the virtual fitting room uses a generation AI to analyze social media posts and reviews to collect consumer emotional data. For example, it can provide a fitting experience that emphasizes positive emotions. The virtual fitting room can also combine and analyze a consumer's purchase history and emotional data to provide a fitting experience that strongly expresses specific emotions. For example, it can provide a fitting experience that takes into account seasonal emotional fluctuations. The virtual fitting room can also collect emotional data in real time, and the generation AI can update the virtual fitting experience based on that data. For example, it can analyze the impact of specific events or campaigns on consumer emotions and reflect that in the fitting experience. This makes it possible to provide a virtual fitting experience based on consumer emotions.

[0075] The virtual fitting room can combine a consumer's body type data with past fitting data to provide an optimal virtual try-on experience. In the virtual fitting room, for example, the generation AI collects the consumer's body type data and combines it with past fitting data for analysis. For example, it can suggest clothing that is best suited to a specific body type. In addition, the virtual fitting room can use the generation AI to provide an optimal virtual try-on experience based on the body type data and fitting data. For example, it can simulate a fit tailored to the consumer's body type. In addition, the virtual fitting room collects body type data and fitting data in real time, and the generation AI updates the virtual try-on experience based on that data. For example, it can respond quickly when new fitting data is added. This enables an optimal virtual try-on experience based on the consumer's body type data and past fitting data.

[0076] The virtual fitting room can simulate different lighting conditions and backgrounds to provide a realistic virtual try-on experience. For example, the generation AI simulates different lighting conditions to recreate how clothes will look when actually being tried on by a consumer. For example, it can provide a try-on experience that takes indoor and outdoor lighting conditions into account. The virtual fitting room can also simulate different backgrounds, and the generation AI uses that data to provide a virtual try-on experience. For example, it can set a background that matches a specific location or event. The virtual fitting room also collects lighting condition and background data in real time, and the generation AI updates the virtual try-on experience based on that data. For example, it can quickly respond when new lighting conditions or backgrounds are added. This enables a realistic virtual try-on experience based on different lighting conditions and backgrounds.

[0077] The virtual try-on unit can analyze virtual experience data from different industries and apply it to virtual apparel try-on. For example, in the virtual try-on unit, the generation AI analyzes virtual experience data from interior design and applies the design elements to virtual apparel try-on. For example, specific colors or patterns can be incorporated into the try-on experience. The virtual try-on unit can also analyze virtual experience data from automobile design, and the generation AI can provide a virtual apparel try-on based on that data. For example, specific shapes or materials can be applied to the try-on experience. The virtual try-on unit can also integrate virtual experience data from different industries, and the generation AI can provide a virtual apparel try-on based on that data. For example, it can provide a try-on experience that combines design elements from multiple industries. This makes it possible to perform virtual apparel try-on based on virtual experience data from different industries.

[0078] The virtual fitting room can analyze a consumer's lifestyle data and provide a virtual fitting experience that suits that lifestyle. For example, in the virtual fitting room, a generation AI collects a consumer's lifestyle data and provides a virtual fitting experience based on that data. For example, a fitting experience of clothing suitable for an active lifestyle can be provided. The virtual fitting room can also combine and analyze lifestyle data and market trends, and a generation AI can provide a virtual fitting experience based on that data. For example, a fitting experience that reflects trends related to a specific lifestyle can be provided. The virtual fitting room can also collect lifestyle data in real time, and a generation AI can update the virtual fitting experience based on that data. For example, it can respond quickly when new lifestyle trends emerge. This makes it possible to provide a virtual fitting experience based on the consumer's lifestyle.

[0079] The virtual fitting unit can use the emotion estimation function to provide a virtual fitting experience based on the consumer's emotions and identify virtual fitting experiences that elicit a positive emotional response. For example, the virtual fitting unit can use the emotion estimation function to analyze the consumer's emotional data and provide fitting experiences that elicit strong positive emotions. For example, it can prioritize fitting experiences that elicit joy and satisfaction. The virtual fitting unit can also provide fitting experiences that strongly express specific emotions based on the consumer's emotional data. For example, it can provide fitting experiences that take seasonal emotional fluctuations into consideration. The virtual fitting unit can also collect emotional data in real time, and the generation AI can update the virtual fitting experience based on that data. For example, it can analyze the impact of specific events or campaigns on the consumer's emotions and reflect that in the fitting experience. This makes it possible to provide a virtual fitting experience based on the consumer's emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The demand forecasting unit can combine consumer purchasing history and weather data to predict demand under specific weather conditions. For example, it can predict that demand for raincoats and waterproof shoes will increase on rainy days and adjust inventory accordingly. The demand forecasting unit can also predict demand for winter and summer items, taking into account seasonal temperature fluctuations. Furthermore, the demand forecasting unit can analyze demand fluctuations in real time when abnormal weather occurs and respond quickly. This makes it possible to forecast demand based on consumer purchasing history and weather data.

[0082] The design generation unit can generate personalized designs by combining consumer purchasing history and social media trend data. For example, for a consumer who prefers a particular brand or style, it can generate a design based on that brand or style. The design generation unit can also analyze social media trends and generate designs that incorporate popular design elements. Furthermore, the design generation unit collects consumer purchasing history and trend data in real time, allowing the generation AI to update designs based on that data. This makes it possible to generate personalized designs based on consumer purchasing history and trend data.

[0083] The EC Operations Department can combine consumer purchase history and inventory data to perform optimal inventory management. For example, it can use past data to predict how much of a particular product will sell at what time, optimizing inventory. The EC Operations Department can also combine and analyze inventory data and purchase history to automatically reorder specific products before they sell out. Furthermore, the EC Operations Department can collect consumer purchase history and inventory data in real time, and the generation AI can update inventory management based on that data. This enables optimal inventory management based on consumer purchase history and inventory data.

[0084] The virtual customer service department can analyze consumers' lifestyle data and make product suggestions that fit their lifestyle. For example, it can suggest sportswear and outdoor gear to consumers with active lifestyles. The virtual customer service department can also combine lifestyle data with market trends and analyze them to make product suggestions that reflect trends related to specific lifestyles. Furthermore, the virtual customer service department can collect lifestyle data in real time, and the generation AI can update product suggestions based on that data. This makes it possible to make product suggestions based on consumers' lifestyles.

[0085] The virtual fitting room can combine a consumer's body type data with past fitting data to provide an optimal virtual fitting experience. For example, it can suggest clothing that is best suited to a specific body type and simulate how it will fit when the consumer actually tries it on. The virtual fitting room can also use generation AI to provide an optimal virtual fitting experience based on the body type data and fitting data. Furthermore, the virtual fitting room can collect body type data and fitting data in real time, and the generation AI can update the virtual fitting experience based on that data. This enables an optimal virtual fitting experience based on the consumer's body type data and past fitting data.

[0086] The demand forecasting unit can analyze consumer emotional data and make demand forecasts based on emotional fluctuations. For example, the generation AI can analyze social media posts and reviews to identify products and trends with a high level of positive emotion, and then make demand forecasts based on that data. It can also combine and analyze consumer purchasing history and emotional data to predict demand based on periods and events when specific emotions are most strongly expressed. Furthermore, emotional data can be collected in real time, and the generation AI can update demand forecasts based on that data. This makes it possible to make demand forecasts based on consumer emotions.

[0087] The design generation unit can analyze consumer emotional data and generate designs and patterns based on those emotions. For example, the generation AI can analyze social media posts and reviews to generate designs and patterns with a high proportion of positive emotions. It can also combine and analyze consumer purchasing history and emotional data to generate designs and patterns that strongly express specific emotions. Furthermore, emotional data can be collected in real time, and the generation AI can generate designs and patterns based on that data. This makes it possible to generate designs and patterns based on consumer emotions.

[0088] The e-commerce operations department can analyze consumer sentiment data and set prices and promotions based on those sentiments. For example, the generation AI can analyze social media posts and reviews and raise prices or strengthen promotions for products with a high level of positive sentiment. It can also analyze a consumer's purchasing history in combination with their sentiment data and set prices and promotions based on periods or events when specific emotions are most strongly expressed. Furthermore, sentiment data can be collected in real time, and the generation AI can update pricing and promotions based on that data. This makes it possible to set prices and promotions based on consumer sentiment.

[0089] The virtual customer service department can analyze consumer emotional data and make product suggestions based on those emotions. For example, the generation AI can analyze social media posts and reviews and prioritize products with a high percentage of positive emotions. It can also combine and analyze a consumer's purchasing history and emotional data to suggest products that strongly express certain emotions. Furthermore, it can collect emotional data in real time and allow the generation AI to update product suggestions based on that data. This makes it possible to make product suggestions based on consumer emotions.

[0090] The virtual fitting section can analyze consumers' emotional data and provide a virtual fitting experience based on their emotions. For example, the generation AI can analyze social media posts and reviews to provide a fitting experience that is dominated by positive emotions. It can also combine and analyze consumers' purchase history and emotional data to provide a fitting experience that strongly expresses specific emotions. Furthermore, emotional data can be collected in real time, and the generation AI can update the virtual fitting experience based on that data. This makes it possible to provide a virtual fitting experience that is based on consumers' emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The demand forecasting department uses generative AI to make demand forecasts. Generative AI analyzes past sales data, market trends, and consumer purchasing behavior data to predict future demand. It can also predict what designs will be popular in the next season and create appropriate production plans based on the results. Step 2: The design generation unit automatically generates designs and patterns based on the demand predicted by the demand forecasting unit. The generation AI generates designs and patterns based on consumer needs and trends, and when instructed to generate a casual yet elegant design, it can generate specific designs and patterns based on that instruction. Step 3: The E-commerce Operations Department automates the operation of the E-commerce website based on the designs and patterns generated by the Design Generation Department. The Generative AI can optimize inventory management, pricing, and promotions, and can optimize the operation of the E-commerce website based on inventory data and pricing prompts.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 demand forecasting unit that uses generation AI to make demand forecasts; a design generation unit that automatically generates designs and patterns based on the demand predicted by the demand prediction unit; an EC operation unit that automates the operation of an EC site based on the designs and patterns generated by the design generation unit; A system characterized by:

2. The demand forecasting unit Analyzing consumer sentiment data and forecasting demand based on sentiment fluctuations 2. The system of claim 1.

3. The demand forecasting unit Analyze social media trends and influencer posts to update demand forecasts in real time 2. The system of claim 1.

4. The demand forecasting unit Analyzing regional climate data to predict seasonal demand fluctuations 2. The system of claim 1.

5. The design generation unit Analyzing consumer sentiment data and generating designs and patterns based on emotions 2. The system of claim 1.

6. The design generation unit Analyzing data from past fashion shows to predict trends and generate designs 2. The system of claim 1.

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