System

Abstract

An object of a system according to an embodiment is to efficiently manufacture an original accessory based on a customer's request.SOLUTION: A system according to an embodiment includes a demand collection unit, a design generation unit, and a manufacturing unit. The request collection unit collects requests of customers. The design generation unit generates a design on the basis of the requests collected by the request collection unit. The manufacturing section manufactures the accessory based on the design generated by the design generation section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not efficiently produce original accessories based on customer requests, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently manufacture original accessories based on customer requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a requirement collection unit, a design generation unit, and a manufacturing unit. The requirement collection unit collects customer requirements. The design generation unit generates designs based on the requirements collected by the requirement collection unit. The manufacturing unit manufactures accessories based on the designs generated by the design generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manufacture original accessories based on customer requests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The original accessory manufacturing system according to an embodiment of the present invention utilizes generative AI and a 3D printer to manufacture original accessories for customers who purchase mobile phones at a mobile phone shop. As a result, the original accessory manufacturing system can provide original accessories based on customer requests, thereby improving customer satisfaction.

[0029] An original accessory manufacturing system according to an embodiment includes a request collection unit, a design generation unit, and a manufacturing unit. The request collection unit collects customer requests. For example, a customer may input a request, such as "I want a smartphone case with a simple and modern design," in natural language. The request collection unit may also collect customer requests through questionnaires, interviews, or online forms. The design generation unit generates designs based on the requests collected by the request collection unit. For example, a generation AI generates a smartphone case with a simple and modern design based on the customer's request. The generation AI may also generate a keychain design that includes the customer's name. The manufacturing unit manufactures accessories based on the designs generated by the design generation unit. For example, a 3D printer reads the design data output by the generation AI and manufactures smartphone cases and keychains using the specified materials. The manufacturing unit can also manufacture accessories such as earphone holders. This allows the original accessory manufacturing system according to an embodiment to manufacture original accessories based on customer requests.

[0030] The request collection unit learns a customer's past purchase history and preferences, and is able to predict and make suggestions based on their requests. For example, the request collection unit uses a generative AI to analyze a customer's past purchase history and learn the designs and materials that the customer prefers. For example, a customer who previously purchased a smartphone case with a simple design can be suggested a new accessory with a simple, modern design. The request collection unit can also learn customer preferences based on survey results and past selection history, and is able to predict and make suggestions based on their requests. This makes it possible to predict and make suggestions based on a customer's past purchase history and preferences.

[0031] The requirement collection unit can display a preview of the design in real time when the customer inputs their requirement, thereby supporting the realization of the requirement. For example, the requirement collection unit adds a function in which the generation AI displays a preview of the design in real time when the customer inputs their requirement. For example, if a customer inputs, "I want a smartphone case with a simple and modern design," the generation AI will immediately display a preview of the simple and modern design. The requirement collection unit can also display a preview of the design using streaming technology and real-time rendering technology, thereby supporting the realization of the requirement. This makes it possible to display a preview of the design in real time and support the realization of the requirement.

[0032] The requirement collection unit can enable voice input and gesture input, providing a more intuitive interface. For example, the requirement collection unit adds a function that enables voice input when a customer inputs a requirement. For example, if a customer inputs by voice, "I want a smartphone case with a simple, modern design," the generation AI analyzes the requirement and generates a design. The requirement collection unit can also add a function that enables gesture input. For example, a customer inputs the requirement with hand movements, and the generation AI analyzes the movements and generates a design. This enables voice input and gesture input, providing a more intuitive interface.

[0033] The request collection unit can enable requests to be input through a dedicated app even in places other than mobile phone shops. For example, the request collection unit develops a dedicated app that enables customers to input requests even in places other than mobile phone shops. For example, a customer inputs their request using the app at home or in the office, and the generation AI analyzes the request and generates a design. The request collection unit can also enable requests to be input through a mobile app or a web app. This allows requests to be input through the dedicated app even in places other than mobile phone shops.

[0034] The design generation unit can automatically combine different design styles to propose new designs. For example, when the generative AI generates a design, the design generation unit automatically combines different design styles to propose new designs. For example, it generates a design that combines simple shapes with modern patterns. The design generation unit can also combine different design styles using algorithms and AI. This makes it possible to propose new designs by combining different design styles.

[0035] The design generation unit can generate multiple design proposals based on customer requests and allow the customer to select from them. For example, when the generation AI generates a design, the design generation unit generates multiple design proposals based on the customer's requests and allows the customer to select from them. For example, if a customer wants a smartphone case with a simple and modern design, multiple simple and modern design proposals will be presented. The design generation unit can also generate multiple design proposals using template-based generation or AI generation. This allows multiple design proposals to be generated based on the customer's requests and allow the customer to select from them.

[0036] The design generation unit can propose designs by referring to the requests and trends of other customers. For example, when the generation AI generates a design, the design generation unit proposes a design by referring to the requests and trends of other customers. For example, it could propose a smartphone case that incorporates a minimalist design, which is a recent trend. The design generation unit can also refer to the requests and trends of other customers through the use of databases and trend analysis. This allows it to propose designs by referring to the requests and trends of other customers.

[0037] The design generation unit can propose variations in different materials and colors, allowing customers to choose from. For example, when the generative AI generates a design, the design generation unit proposes variations in different materials and colors, allowing customers to choose from. For example, for a smartphone case with a simple and modern design, it proposes variations in materials such as plastic, silicone, and leather. The design generation unit can also propose variations taking into account the characteristics of the materials and color combinations. This makes it possible to propose variations in different materials and colors, allowing customers to choose from them.

[0038] The manufacturing department can combine multiple materials to manufacture accessories with new textures and functions. For example, when a 3D printer is used to manufacture, the manufacturing department combines multiple materials to manufacture accessories with new textures and functions. For example, a smartphone case is manufactured by combining plastic and silicone. The manufacturing department can also manufacture accessories with new textures and functions by taking into account the properties of the materials and the effects of combinations. This allows the manufacturing of accessories with new textures and functions by combining multiple materials.

[0039] The manufacturing department can monitor the manufacturing process in real time and implement a feedback loop to ensure quality. For example, when a 3D printer is manufacturing, the manufacturing department can monitor the manufacturing process in real time and implement a feedback loop to ensure quality. For example, sensors can be used to monitor the condition of the material during production and automatically correct any abnormalities detected. The manufacturing department can also use real-time data processing technology and monitoring systems to monitor the manufacturing process and ensure quality. This allows the manufacturing department to monitor the manufacturing process in real time and implement a feedback loop to ensure quality.

[0040] The manufacturing department can enable the simultaneous production of accessories of different sizes and shapes. For example, when a 3D printer is used to produce accessories of different sizes and shapes, the manufacturing department can enable the simultaneous production of accessories of different sizes and shapes. For example, a smartphone case and a key chain can be produced at the same time. The manufacturing department can also use multitasking manufacturing or parallel processing technology to simultaneously produce accessories of different sizes and shapes. This allows accessories of different sizes and shapes to be produced at the same time.

[0041] The manufacturing department can use recycled materials and introduce environmentally friendly manufacturing processes. For example, when a 3D printer is used to manufacture products, the manufacturing department can use recycled materials and introduce environmentally friendly manufacturing processes. For example, the manufacturing department can use recycled plastic to manufacture smartphone cases. The manufacturing department can also introduce environmentally friendly manufacturing processes that take into account energy efficiency and waste reduction. This allows the manufacturing department to use recycled materials and introduce environmentally friendly manufacturing processes.

[0042] The system can explain the design process of an accessory and the reasons for selecting materials when providing it to a customer. For example, the system can explain the design process of an accessory and the reasons for selecting materials when providing it to a customer. For example, it can explain how the design of a smartphone case was created and why the materials used were selected. The system can also explain the design process and the reasons for selecting materials through providing instructions or online guides. This can improve customer satisfaction by explaining the design process of an accessory and the reasons for selecting materials when providing it to a customer.

[0043] The system can provide a guide including how to use and maintain the accessory when providing it to the customer. For example, the system can provide a guide including how to use and maintain the accessory when providing it to the customer. For example, the system can provide a guide explaining how to attach and clean a smartphone case. The system can also explain how to use and maintain the accessory through a printed manual or an online guide. This can improve customer satisfaction by providing a guide including how to use and maintain the accessory when providing it to the customer.

[0044] The system can suggest customization options for accessories when providing them to customers, facilitating further personalization. For example, the system can suggest customization options for accessories when providing them to customers. For example, it can suggest options that allow customers to change the color or material of a smartphone case, allowing them to customize it to their preferences. The system can also suggest customization options through a list of options or customization steps. This allows the system to suggest customization options for accessories when providing them to customers, facilitating further personalization.

[0045] The system can improve the experience when opening the accessory by adding ingenuity to the packaging of the accessory when providing it to the customer. For example, the system can improve the experience when opening the accessory by adding ingenuity to the packaging of the accessory when providing it to the customer. For example, the system can include a luxurious package or a special message card. The system can also improve the packaging by adding ingenuity to the design or selecting materials. This can improve the experience when opening the accessory by adding ingenuity to the packaging of the accessory when providing it to the customer.

[0046] The system can suggest related products and services along with accessories when providing them to customers, promoting cross-selling. For example, the system can suggest related products and services along with accessories when providing them to customers. For example, suggesting a screen protector or charging cable along with a smartphone case. The system can also suggest related products and services through a cross-selling algorithm and the timing of suggestions. This makes it possible to suggest related products and services along with accessories when providing them to customers, promoting cross-selling.

[0047] When collecting customer feedback, the system can automatically classify the content of the feedback and identify high-priority areas for improvement. For example, when collecting customer feedback, the system uses generative AI to automatically classify the content of the feedback and identify high-priority areas for improvement. For example, feedback about smartphone cases can be classified into categories such as design, material, and function, and the category that receives the most feedback can be prioritized for improvement. The system can also automatically classify the content of the feedback using data analysis techniques and feedback classification algorithms to identify high-priority areas for improvement. This makes it possible to automatically classify the content of the feedback and identify high-priority areas for improvement when collecting customer feedback.

[0048] The system visualizes the content of customer feedback, allowing the generation AI to reflect it in the next design generation. For example, the system visualizes the content of customer feedback, allowing the generation AI to reflect it in the next design generation. For example, feedback about smartphone cases can be displayed in graphs or charts, and the generation AI can generate a new design based on that data. The system also visualizes the content of the feedback using data visualization technology and feedback analysis technology, allowing the generation AI to reflect it in the next design generation. This allows the content of customer feedback to be visualized, allowing the generation AI to reflect it in the next design generation.

[0049] The system can develop a dedicated app for collecting customer feedback, making it easier to collect feedback. For example, the system can develop a dedicated app for collecting customer feedback, making it easier to collect feedback. For example, the system can provide an interface that allows users to easily enter feedback about smartphone cases. The system can also collect feedback through a mobile app or a web app. This allows the system to develop a dedicated app for collecting customer feedback, making it easier to collect feedback.

[0050] The system can provide incentives when collecting customer feedback to improve the quality and quantity of feedback. For example, the system can provide incentives when collecting customer feedback to improve the quality and quantity of feedback. For example, a discount coupon can be provided to customers who provide feedback on smartphone cases. The system can also provide incentives through a points system or rewards to improve the quality and quantity of feedback. This makes it possible to provide incentives when collecting customer feedback to improve the quality and quantity of feedback.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The request collection unit can predict and suggest requests based on a customer's lifestyle and hobbies. For example, a waterproof and durable smartphone case can be suggested to a customer who likes the outdoors. Also, accessories with music-related designs and functions can be suggested to a customer who likes music. Furthermore, the request collection unit can analyze the customer's social media activity and purchase history to make suggestions based on the customer's lifestyle and hobbies. This makes it possible to predict and suggest requests based on the customer's lifestyle and hobbies.

[0053] The design generation department can incorporate design elements from different cultures and regions to cater to global customers. For example, it can generate smartphone cases that combine traditional Asian patterns with modern European designs. The design generation department can also propose personalized designs taking into account the customer's place of origin and cultural background. Furthermore, the design generation department can analyze international design trends and incorporate the latest design elements. This allows it to incorporate design elements from different cultures and regions to cater to global customers.

[0054] The manufacturing department can use eco-friendly materials in the manufacturing process to produce environmentally friendly accessories. For example, they can use recycled plastic or bioplastic to produce smartphone cases. The manufacturing department can also introduce energy-efficient manufacturing techniques to reduce energy consumption in the manufacturing process. Furthermore, the manufacturing department can promote the recycling and reuse of waste to minimize the environmental impact. This allows them to use eco-friendly materials to produce environmentally friendly accessories.

[0055] The system can suggest customization options for accessories when providing them to customers, facilitating further personalization. For example, it can suggest options that allow customers to change the color or material of a smartphone case, allowing them to customize it to their own preferences. The system can also suggest customization options through a list of options or customization steps. Furthermore, the system can suggest the optimal customization option based on the customer's past selection history and preferences. This allows the system to suggest customization options for accessories when providing them to customers, facilitating further personalization.

[0056] The system can provide incentives when collecting customer feedback to improve the quality and quantity of feedback. For example, a discount coupon can be provided to customers who provide feedback on smartphone cases. The system can also provide incentives through a points system or rewards to improve the quality and quantity of feedback. Furthermore, the system can analyze the content of the feedback and offer special incentives to customers who provide the most useful feedback. This makes it possible to provide incentives when collecting customer feedback to improve the quality and quantity of feedback.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The requirements collection department collects customer requirements. For example, a customer might input a request in natural language, such as "I want a smartphone case with a simple, modern design." The requirements collection department can also collect customer requirements through questionnaires, interviews, or online forms. Step 2: The design generation unit generates a design based on the requests collected by the request collection unit. For example, the generation AI generates a smartphone case with a simple and modern design based on the customer's request. The generation AI can also generate a keychain design that includes the customer's name. Step 3: The manufacturing department manufactures accessories based on the designs generated by the design generation department. For example, a 3D printer reads the design data output by the generation AI and produces smartphone cases and key chains using the specified materials. The manufacturing department can also manufacture accessories such as earphone holders.

[0059] (Example 2) The original accessory manufacturing system according to an embodiment of the present invention utilizes generative AI and a 3D printer to manufacture original accessories for customers who purchase mobile phones at a mobile phone shop. As a result, the original accessory manufacturing system can provide original accessories based on customer requests, thereby improving customer satisfaction.

[0060] An original accessory manufacturing system according to an embodiment includes a request collection unit, a design generation unit, and a manufacturing unit. The request collection unit collects customer requests. For example, a customer may input a request, such as "I want a smartphone case with a simple and modern design," in natural language. The request collection unit may also collect customer requests through questionnaires, interviews, or online forms. The design generation unit generates designs based on the requests collected by the request collection unit. For example, a generation AI generates a smartphone case with a simple and modern design based on the customer's request. The generation AI may also generate a keychain design that includes the customer's name. The manufacturing unit manufactures accessories based on the designs generated by the design generation unit. For example, a 3D printer reads the design data output by the generation AI and manufactures smartphone cases and keychains using the specified materials. The manufacturing unit can also manufacture accessories such as earphone holders. This allows the original accessory manufacturing system according to an embodiment to manufacture original accessories based on customer requests.

[0061] The request collection unit can analyze the customer's facial expressions and tone of voice to infer their emotions and improve the accuracy of the request. For example, when a customer inputs a request, the generation AI uses a camera and microphone to analyze the customer's facial expressions and tone of voice in real time. For example, if a customer says, "I want a smartphone case with a simple, modern design," the generation AI analyzes whether the customer's facial expressions are relaxed and their tone of voice is calm, thereby increasing the specificity of the request. The request collection unit can also use facial recognition technology and voice analysis technology to infer the customer's emotions and improve the accuracy of the request. This makes it possible to improve the accuracy of the request by taking the customer's emotions into consideration.

[0062] The request collection unit learns a customer's past purchase history and preferences, and is able to predict and make suggestions based on their requests. For example, the request collection unit uses a generative AI to analyze a customer's past purchase history and learn the designs and materials that the customer prefers. For example, a customer who previously purchased a smartphone case with a simple design can be suggested a new accessory with a simple, modern design. The request collection unit can also learn customer preferences based on survey results and past selection history, and is able to predict and make suggestions based on their requests. This makes it possible to predict and make suggestions based on a customer's past purchase history and preferences.

[0063] The requirement collection unit can display a preview of the design in real time when the customer inputs their requirement, thereby supporting the realization of the requirement. For example, the requirement collection unit adds a function in which the generation AI displays a preview of the design in real time when the customer inputs their requirement. For example, if a customer inputs, "I want a smartphone case with a simple and modern design," the generation AI will immediately display a preview of the simple and modern design. The requirement collection unit can also display a preview of the design using streaming technology and real-time rendering technology, thereby supporting the realization of the requirement. This makes it possible to display a preview of the design in real time and support the realization of the requirement.

[0064] The requirement collection unit can enable voice input and gesture input, providing a more intuitive interface. For example, the requirement collection unit adds a function that enables voice input when a customer inputs a requirement. For example, if a customer inputs by voice, "I want a smartphone case with a simple, modern design," the generation AI analyzes the requirement and generates a design. The requirement collection unit can also add a function that enables gesture input. For example, a customer inputs the requirement with hand movements, and the generation AI analyzes the movements and generates a design. This enables voice input and gesture input, providing a more intuitive interface.

[0065] The request collection unit can enable requests to be input through a dedicated app even in places other than mobile phone shops. For example, the request collection unit develops a dedicated app that enables customers to input requests even in places other than mobile phone shops. For example, a customer inputs their request using the app at home or in the office, and the generation AI analyzes the request and generates a design. The request collection unit can also enable requests to be input through a mobile app or a web app. This allows requests to be input through the dedicated app even in places other than mobile phone shops.

[0066] The request collection unit can use an emotion estimation function to analyze customer emotions in real time and make suggestions that elicit positive emotions. For example, when a customer inputs a request, the request collection unit has the generation AI use the emotion estimation function to analyze the customer's emotions in real time. For example, when a customer inputs, "I want a smartphone case with a simple, modern design," the generation AI analyzes the customer's facial expression and tone of voice and makes suggestions that elicit positive emotions. The request collection unit can also use emotion recognition algorithms and streaming analysis technology to analyze customer emotions in real time and make suggestions that elicit positive emotions. This makes it possible to analyze customer emotions in real time and make suggestions that elicit positive emotions.

[0067] The design generation unit can take into account the customer's emotions and incorporate design elements that correspond to those emotions. For example, when the generation AI generates a design, the design generation unit takes into account the customer's emotions and incorporates design elements that correspond to those emotions. For example, if the customer is feeling relaxed, the generation AI will generate a simple and calm design. The design generation unit can also take into account the customer's emotions and incorporate design elements by weighting the emotional data and using emotional scores. This makes it possible to provide designs that provide greater satisfaction by incorporating design elements that correspond to the customer's emotions.

[0068] The design generation unit can automatically combine different design styles to propose new designs. For example, when the generative AI generates a design, the design generation unit automatically combines different design styles to propose new designs. For example, it generates a design that combines simple shapes with modern patterns. The design generation unit can also combine different design styles using algorithms and AI. This makes it possible to propose new designs by combining different design styles.

[0069] The design generation unit can generate multiple design proposals based on customer requests and allow the customer to select from them. For example, when the generation AI generates a design, the design generation unit generates multiple design proposals based on the customer's requests and allows the customer to select from them. For example, if a customer wants a smartphone case with a simple and modern design, multiple simple and modern design proposals will be presented. The design generation unit can also generate multiple design proposals using template-based generation or AI generation. This allows multiple design proposals to be generated based on the customer's requests and allow the customer to select from them.

[0070] The design generation unit can propose designs by referring to the requests and trends of other customers. For example, when the generation AI generates a design, the design generation unit proposes a design by referring to the requests and trends of other customers. For example, it could propose a smartphone case that incorporates a minimalist design, which is a recent trend. The design generation unit can also refer to the requests and trends of other customers through the use of databases and trend analysis. This allows it to propose designs by referring to the requests and trends of other customers.

[0071] The design generation unit can propose variations in different materials and colors, allowing customers to choose from. For example, when the generative AI generates a design, the design generation unit proposes variations in different materials and colors, allowing customers to choose from. For example, for a smartphone case with a simple and modern design, it proposes variations in materials such as plastic, silicone, and leather. The design generation unit can also propose variations taking into account the characteristics of the materials and color combinations. This makes it possible to propose variations in different materials and colors, allowing customers to choose from them.

[0072] The design generation unit can analyze customers' emotional reactions to designs generated using the emotion estimation function in real time and propose optimal designs. For example, when the generation AI generates a design, the design generation unit can analyze customers' emotional reactions in real time using the emotion estimation function and propose optimal designs. For example, it can analyze customers' facial expressions and tone of voice when they view a design and propose designs that receive a lot of positive reactions. The design generation unit can also analyze customers' emotional reactions using emotion recognition algorithms and real-time data processing technology and propose optimal designs. This allows customers' emotional reactions to generated designs to be analyzed in real time and optimal designs to be proposed.

[0073] The manufacturing department can consider the customer's emotions and select materials and finishes that correspond to those emotions. For example, when a 3D printer is manufacturing an item, the manufacturing department can consider the customer's emotions and select materials and finishes that correspond to those emotions. For example, if the customer is feeling relaxed, the generative AI will select simple, calming materials. The manufacturing department can also consider the customer's emotions and select materials and finishes by weighting the emotional data and using emotional scores. This allows the manufacturing department to provide accessories that provide greater satisfaction by selecting materials and finishes that correspond to the customer's emotions.

[0074] The manufacturing department can combine multiple materials to manufacture accessories with new textures and functions. For example, when a 3D printer is used to manufacture, the manufacturing department combines multiple materials to manufacture accessories with new textures and functions. For example, a smartphone case is manufactured by combining plastic and silicone. The manufacturing department can also manufacture accessories with new textures and functions by taking into account the properties of the materials and the effects of combinations. This allows the manufacturing of accessories with new textures and functions by combining multiple materials.

[0075] The manufacturing department can monitor the manufacturing process in real time and implement a feedback loop to ensure quality. For example, when a 3D printer is manufacturing, the manufacturing department can monitor the manufacturing process in real time and implement a feedback loop to ensure quality. For example, sensors can be used to monitor the condition of the material during production and automatically correct any abnormalities detected. The manufacturing department can also use real-time data processing technology and monitoring systems to monitor the manufacturing process and ensure quality. This allows the manufacturing department to monitor the manufacturing process in real time and implement a feedback loop to ensure quality.

[0076] The manufacturing department can enable the simultaneous production of accessories of different sizes and shapes. For example, when a 3D printer is used to produce accessories of different sizes and shapes, the manufacturing department can enable the simultaneous production of accessories of different sizes and shapes. For example, a smartphone case and a key chain can be produced at the same time. The manufacturing department can also use multitasking manufacturing or parallel processing technology to simultaneously produce accessories of different sizes and shapes. This allows accessories of different sizes and shapes to be produced at the same time.

[0077] The manufacturing department can use recycled materials and introduce environmentally friendly manufacturing processes. For example, when a 3D printer is used to manufacture products, the manufacturing department can use recycled materials and introduce environmentally friendly manufacturing processes. For example, the manufacturing department can use recycled plastic to manufacture smartphone cases. The manufacturing department can also introduce environmentally friendly manufacturing processes that take into account energy efficiency and waste reduction. This allows the manufacturing department to use recycled materials and introduce environmentally friendly manufacturing processes.

[0078] The manufacturing department can use emotion estimation functions to collect customer emotional responses to manufactured accessories and reflect them in the next production run. For example, the manufacturing department can collect customer emotional responses to accessories manufactured by a 3D printer and reflect them in the next production run. For example, the manufacturing department can analyze the facial expressions and tone of voice of customers when they receive a smartphone case and reflect designs that receive a lot of positive responses in the next production run. The manufacturing department can also use emotion recognition algorithms and data analysis technology to collect customer emotional responses and reflect them in the next production run. This allows the manufacturing department to collect customer emotional responses to manufactured accessories and reflect them in the next production run.

[0079] The system can explain the design process of an accessory and the reasons for selecting materials when providing it to a customer. For example, the system can explain the design process of an accessory and the reasons for selecting materials when providing it to a customer. For example, it can explain how the design of a smartphone case was created and why the materials used were selected. The system can also explain the design process and the reasons for selecting materials through providing instructions or online guides. This can improve customer satisfaction by explaining the design process of an accessory and the reasons for selecting materials when providing it to a customer.

[0080] The system can provide a guide including how to use and maintain the accessory when providing it to the customer. For example, the system can provide a guide including how to use and maintain the accessory when providing it to the customer. For example, the system can provide a guide explaining how to attach and clean a smartphone case. The system can also explain how to use and maintain the accessory through a printed manual or an online guide. This can improve customer satisfaction by providing a guide including how to use and maintain the accessory when providing it to the customer.

[0081] The system can suggest customization options for accessories when providing them to customers, facilitating further personalization. For example, the system can suggest customization options for accessories when providing them to customers. For example, it can suggest options that allow customers to change the color or material of a smartphone case, allowing them to customize it to their preferences. The system can also suggest customization options through a list of options or customization steps. This allows the system to suggest customization options for accessories when providing them to customers, facilitating further personalization.

[0082] The system can improve the experience when opening the accessory by adding ingenuity to the packaging of the accessory when providing it to the customer. For example, the system can improve the experience when opening the accessory by adding ingenuity to the packaging of the accessory when providing it to the customer. For example, the system can include a luxurious package or a special message card. The system can also improve the packaging by adding ingenuity to the design or selecting materials. This can improve the experience when opening the accessory by adding ingenuity to the packaging of the accessory when providing it to the customer.

[0083] The system can suggest related products and services along with accessories when providing them to customers, promoting cross-selling. For example, the system can suggest related products and services along with accessories when providing them to customers. For example, suggesting a screen protector or charging cable along with a smartphone case. The system can also suggest related products and services through a cross-selling algorithm and the timing of suggestions. This makes it possible to suggest related products and services along with accessories when providing them to customers, promoting cross-selling.

[0084] The system uses an emotion estimation function to collect emotional responses from customers when they receive accessories, and can use the collected data to improve the delivery process. For example, when a customer receives an accessory, the system uses the emotion estimation function to collect the customer's emotional responses. For example, the system analyzes the customer's facial expressions and tone of voice when they receive a smartphone case, and if there are many positive reactions, the delivery process is maintained. The system also uses emotion recognition algorithms and data analysis technology to collect the customer's emotional responses, and can use the collected data to improve the delivery process. This allows the system to collect emotional responses from customers when they receive accessories, and can use the collected data to improve the delivery process.

[0085] When collecting customer feedback, the system uses a generation AI to analyze customer emotions and make improvement suggestions based on those emotions. For example, when collecting customer feedback, the system uses a generation AI to analyze customer emotions and make improvement suggestions based on those emotions. For example, if a customer is dissatisfied with a smartphone case, the system analyzes those emotions and makes suggestions to improve the design or materials. The system can also analyze customer emotions using emotion recognition algorithms and suggestion algorithms to make improvement suggestions. This allows the generation AI to analyze customer emotions and make improvement suggestions based on those emotions when collecting customer feedback.

[0086] When collecting customer feedback, the system can automatically classify the content of the feedback and identify high-priority areas for improvement. For example, when collecting customer feedback, the system uses generative AI to automatically classify the content of the feedback and identify high-priority areas for improvement. For example, feedback about smartphone cases can be classified into categories such as design, material, and function, and the category that receives the most feedback can be prioritized for improvement. The system can also automatically classify the content of the feedback using data analysis techniques and feedback classification algorithms to identify high-priority areas for improvement. This makes it possible to automatically classify the content of the feedback and identify high-priority areas for improvement when collecting customer feedback.

[0087] The system visualizes the content of customer feedback, allowing the generation AI to reflect it in the next design generation. For example, the system visualizes the content of customer feedback, allowing the generation AI to reflect it in the next design generation. For example, feedback about smartphone cases can be displayed in graphs or charts, and the generation AI can generate a new design based on that data. The system also visualizes the content of the feedback using data visualization technology and feedback analysis technology, allowing the generation AI to reflect it in the next design generation. This allows the content of customer feedback to be visualized, allowing the generation AI to reflect it in the next design generation.

[0088] The system can develop a dedicated app for collecting customer feedback, making it easier to collect feedback. For example, the system can develop a dedicated app for collecting customer feedback, making it easier to collect feedback. For example, the system can provide an interface that allows users to easily enter feedback about smartphone cases. The system can also collect feedback through a mobile app or a web app. This allows the system to develop a dedicated app for collecting customer feedback, making it easier to collect feedback.

[0089] The system can provide incentives when collecting customer feedback to improve the quality and quantity of feedback. For example, the system can provide incentives when collecting customer feedback to improve the quality and quantity of feedback. For example, a discount coupon can be provided to customers who provide feedback on smartphone cases. The system can also provide incentives through a points system or rewards to improve the quality and quantity of feedback. This makes it possible to provide incentives when collecting customer feedback to improve the quality and quantity of feedback.

[0090] The system can use the emotion estimation function to analyze the emotional aspects of customer feedback and make improvement suggestions based on the emotions. For example, the system can use the emotion estimation function to analyze the emotional aspects of customer feedback and make improvement suggestions based on the emotions. For example, the system can analyze the emotions in feedback about smartphone cases and reflect designs with a high percentage of positive emotions in the next production run. The system can also use emotion recognition algorithms and data analysis techniques to analyze the emotional aspects of customer feedback and make improvement suggestions. This makes it possible to use the emotion estimation function to analyze the emotional aspects of customer feedback and make improvement suggestions based on the emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The request collection unit can predict and suggest requests based on a customer's lifestyle and hobbies. For example, a waterproof and durable smartphone case can be suggested to a customer who likes the outdoors. Also, accessories with music-related designs and functions can be suggested to a customer who likes music. Furthermore, the request collection unit can analyze the customer's social media activity and purchase history to make suggestions based on the customer's lifestyle and hobbies. This makes it possible to predict and suggest requests based on the customer's lifestyle and hobbies.

[0093] The request collection unit can estimate the emotions of the customer and prioritize the requests based on the estimated emotions. For example, if the customer is very excited, the request can be processed with priority. Also, if the customer is feeling anxious, the request can be checked in detail and a proposal can be made to reassure the customer. Furthermore, the request collection unit can accumulate customer emotion data and analyze long-term emotional trends to prioritize the requests. This makes it possible to prioritize requests taking the customer's emotions into consideration.

[0094] The design generation department can incorporate design elements from different cultures and regions to cater to global customers. For example, it can generate smartphone cases that combine traditional Asian patterns with modern European designs. The design generation department can also propose personalized designs taking into account the customer's place of origin and cultural background. Furthermore, the design generation department can analyze international design trends and incorporate the latest design elements. This allows it to incorporate design elements from different cultures and regions to cater to global customers.

[0095] The design generation unit can estimate the customer's emotions and adjust the color and shape of the design based on the estimated emotion. For example, if the customer is feeling relaxed, the generation AI can generate a design with soft colors and curved shapes. On the other hand, if the customer is feeling energetic, it can generate a design with vivid colors and sharp shapes. Furthermore, the design generation unit can fine-tune the design based on the emotion data to provide a design that best suits the customer's emotions. This makes it possible to adjust the color and shape of the design based on the customer's emotions.

[0096] The manufacturing department can use eco-friendly materials in the manufacturing process to produce environmentally friendly accessories. For example, they can use recycled plastic or bioplastic to produce smartphone cases. The manufacturing department can also introduce energy-efficient manufacturing techniques to reduce energy consumption in the manufacturing process. Furthermore, the manufacturing department can promote the recycling and reuse of waste to minimize the environmental impact. This allows them to use eco-friendly materials to produce environmentally friendly accessories.

[0097] The manufacturing department can estimate customer sentiment and optimize the manufacturing process based on the estimated sentiment. For example, if the customer is in a hurry, the manufacturing department can prioritize production speed and quickly produce accessories. Alternatively, if the customer places importance on quality, the manufacturing department can strengthen quality control and provide high-quality accessories. Furthermore, the manufacturing department can identify areas for improvement in the manufacturing process based on customer sentiment data and continuously optimize the manufacturing process. This allows the manufacturing process to be optimized based on customer sentiment.

[0098] The system can suggest customization options for accessories when providing them to customers, facilitating further personalization. For example, it can suggest options that allow customers to change the color or material of a smartphone case, allowing them to customize it to their own preferences. The system can also suggest customization options through a list of options or customization steps. Furthermore, the system can suggest the optimal customization option based on the customer's past selection history and preferences. This allows the system to suggest customization options for accessories when providing them to customers, facilitating further personalization.

[0099] The system can estimate customer emotions and optimize the delivery process based on the estimated emotions. For example, if a customer is excited, the system can quickly deliver accessories to meet the customer's expectations. If a customer is anxious, the system can provide detailed explanations and support to reassure the customer. Furthermore, the system can identify areas for improvement in the delivery process based on customer emotion data and continuously optimize the delivery process. This makes it possible to optimize the delivery process based on customer emotions.

[0100] The system can provide incentives when collecting customer feedback to improve the quality and quantity of feedback. For example, a discount coupon can be provided to customers who provide feedback on smartphone cases. The system can also provide incentives through a points system or rewards to improve the quality and quantity of feedback. Furthermore, the system can analyze the content of the feedback and offer special incentives to customers who provide the most useful feedback. This makes it possible to provide incentives when collecting customer feedback to improve the quality and quantity of feedback.

[0101] The system can use the emotion estimation function to analyze the emotional aspects of customer feedback and make improvement suggestions based on those emotions. For example, it can analyze the emotions in feedback about smartphone cases and incorporate designs with a high percentage of positive emotions into the next production run. The system can also use emotion recognition algorithms and data analysis technology to analyze the emotional aspects of customer feedback and make improvement suggestions. Furthermore, the system can accumulate customer emotion data and analyze long-term emotional trends to make improvement suggestions. This allows the system to analyze the emotional aspects of customer feedback using the emotion estimation function and make improvement suggestions based on those emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The requirements collection department collects customer requirements. For example, a customer might input a request in natural language, such as "I want a smartphone case with a simple, modern design." The requirements collection department can also collect customer requirements through questionnaires, interviews, or online forms. Step 2: The design generation unit generates a design based on the requests collected by the request collection unit. For example, the generation AI generates a smartphone case with a simple and modern design based on the customer's request. The generation AI can also generate a keychain design that includes the customer's name. Step 3: The manufacturing department manufactures accessories based on the designs generated by the design generation department. For example, a 3D printer reads the design data output by the generation AI and produces smartphone cases and key chains using the specified materials. The manufacturing department can also manufacture accessories such as earphone holders.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

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

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0150] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0152] The data processing system 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 request collection unit that collects customer requests; a design generation unit that generates a design based on the requests collected by the request collection unit; a manufacturing unit that manufactures accessories based on the design generated by the design generation unit. A system characterized by:

2. The request collection unit Learn about the customer's past purchase history and preferences, and predict and suggest what they want.

2. The system of claim 1.

3. The design generation unit Automatically combine different design styles to propose new designs 2. The system of claim 1.

4. The manufacturing department To enable the simultaneous production of said accessories in different sizes and shapes 2. The system of claim 1.

5. The request collection unit Analyze the customer's facial expressions and tone of voice to estimate their emotions and improve the accuracy of their requests.

2. The system of claim 1.

Images