System

Abstract

An object of the system according to the embodiment is to propose a design based on the constraints and rules of the sewing factory while reflecting the preferences and demands of the consumer.SOLUTION: A system according to an embodiment includes a design proposal unit, a constraint consideration unit, and a preference reflection unit. The design suggester uses the generated AI to suggest designs based on the consumers' preferences and desires. The constraint consideration section considers sewing plant constraints and rules. The preference reflection unit reflects the preferences and demands of the consumer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to propose designs that reflect consumer preferences and requests while respecting the restrictions and rules of sewing factories.

[0005] The system according to the embodiment aims to propose designs that reflect the preferences and desires of consumers while also being based on the restrictions and rules of sewing factories. [Means for solving the problem]

[0006] The system according to the embodiment includes a design proposal unit, a constraint consideration unit, and a preference reflection unit. The design proposal unit uses a generative AI to propose designs based on consumer preferences and requests. The constraint consideration unit considers restrictions and rules of the sewing factory. The preference reflection unit reflects consumer preferences and requests. [Effects of the Invention]

[0007] The system according to the embodiment can propose designs that reflect the preferences and requests of consumers while also being based on the restrictions and rules of sewing factories. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A fashion design platform according to an embodiment of the present invention is a system that uses generative AI to enable consumers to design fashion items that suit their preferences and needs, thereby enabling consumers to easily design unique and original fashion items.

[0029] A fashion design platform according to an embodiment includes a design suggestion unit, a constraint consideration unit, and a preference reflection unit. The design suggestion unit proposes designs based on consumer preferences and requests. For example, if a consumer inputs, "I want a red dress with an off-the-shoulder design," the generation AI generates multiple design proposals based on that request. The design suggestion unit generates designs based on consumer input. The constraint consideration unit considers the constraints and rules of the sewing factory. For example, if there are constraints on specific materials or sewing techniques, the generation AI proposes designs that reflect those constraints. The constraint consideration unit generates designs based on the constraints and rules of the sewing factory. The preference reflection unit reflects consumer preferences and requests. For example, if a consumer inputs, "I want a casual style with a blue-based design," the generation AI proposes a casual style with a blue-based design based on that request. The preference reflection unit generates designs that reflect the consumer preferences and requests. This allows the fashion design platform to propose designs that consider the constraints and rules of the sewing factory based on the consumer preferences and requests.

[0030] The design suggestion unit can learn a consumer's past purchase history or preference trends and generate more personalized designs. For example, the design suggestion unit stores a consumer's past purchase history in a database, and the generation AI proposes designs based on that data. For example, it analyzes the color and style of previously purchased items to generate a design that suits the consumer's preferences. The design suggestion unit also learns designs that the consumer has previously rated and their saved favorite lists, and the generation AI proposes new designs based on that information. For example, it extracts features of highly rated designs and generates designs that reflect those features. The design suggestion unit also analyzes data on the cloud to learn a consumer's past purchase history and preference trends and proposes personalized designs. For example, it generates designs based on the brands and styles that the consumer frequently purchases. This allows the unit to learn a consumer's past purchase history and preference trends and generate more personalized designs.

[0031] The design proposal unit can generate designs that take fit into consideration by reflecting the consumer's body data. For example, the design proposal unit inputs the consumer's body data, and the generation AI proposes designs that take fit into consideration based on that data. For example, the optimal design is generated based on data such as height, weight, and waist size. The design proposal unit also converts the body data provided by the consumer into a 3D model, and the generation AI proposes designs that take fit into consideration based on that model. For example, the 3D model is used to perform a try-on simulation and generate the optimal design. The design proposal unit also stores the consumer's body data on the cloud, and the generation AI proposes designs that take fit into consideration based on that data. For example, the design proposal unit provides customization options tailored to body type, allowing the consumer to select a design that suits them best. This makes it possible to generate designs that reflect the consumer's body data and take fit into consideration.

[0032] The constraint consideration unit updates the sewing factory's constraints and rules in real time, making it possible to generate designs that are compatible with the latest technologies and materials. For example, the constraint consideration unit manages the sewing factory's constraints and rules on the cloud and builds a system that updates them in real time. For example, the database is updated immediately when new materials or technologies are introduced. The constraint consideration unit also uses a generation AI to automatically obtain the sewing factory's constraints and rules, and based on that data, proposes designs that are compatible with the latest technologies and materials. For example, it generates designs that are compatible with new sewing technologies. The constraint consideration unit also updates the sewing factory's constraints and rules in real time, and based on that data, the generation AI proposes designs that are compatible with the latest technologies and materials. For example, it generates designs that reflect the characteristics of new materials. This makes it possible to update the sewing factory's constraints and rules in real time and generate designs that are compatible with the latest technologies and materials.

[0033] The constraint consideration unit can customize the design to suit local regulations or cultural backgrounds and generate region-specific designs. For example, the constraint consideration unit registers local regulations and cultural backgrounds in a database, and the generation AI proposes region-specific designs based on that data. For example, it generates designs that reflect styles and materials popular in a particular region. The constraint consideration unit also acquires local regulations and cultural backgrounds in real time, and the generation AI proposes customized designs based on that data. For example, it generates designs that incorporate traditional region patterns and colors. The constraint consideration unit also analyzes local regulations and cultural backgrounds on the cloud, and the generation AI proposes region-specific designs based on that data. For example, it generates designs that match local festivals and events. This makes it possible to customize the design to suit local regulations and cultural backgrounds and generate region-specific designs.

[0034] The preference reflection unit can automatically collect information from social media posts or reviews and reflect it in the design. For example, the preference reflection unit analyzes social media posts and builds a system that automatically collects consumer preferences and trends. For example, designs are generated based on popular hashtags and keywords. The preference reflection unit also automatically collects consumer reviews, and the generation AI proposes designs based on that data. For example, design features are extracted based on highly rated reviews and a design is generated that reflects them. The preference reflection unit also analyzes social media posts and reviews on the cloud, and the generation AI proposes designs that reflect consumer preferences and requests based on that data. For example, it performs trend analysis and generates designs that incorporate the latest fashions. This makes it possible to automatically collect information from social media posts and reviews and reflect it in the design.

[0035] The preference reflection unit can reflect the opinions of multiple people, including those of family or friends. For example, the preference reflection unit provides a function that allows consumers to input the opinions of family and friends, and the generation AI proposes designs based on that data. For example, a design that reflects the opinions of multiple people is generated. The preference reflection unit also collects the opinions of family and friends in real time, and the generation AI proposes designs based on that data. For example, a design that takes the preferences of family and friends into consideration is generated. The preference reflection unit also analyzes the opinions of family and friends on the cloud, and the generation AI proposes designs that reflect the perspectives of multiple people based on that data. For example, it provides customization options that incorporate the opinions of family and friends. This makes it possible to reflect the perspectives of multiple people, including those of family and friends.

[0036] The preference reflection unit can combine trends from different cultures and regions to propose designs from a global perspective. For example, the preference reflection unit collects trend data from different cultures and regions, and the generation AI proposes designs from a global perspective based on that data. For example, it generates designs that incorporate styles and colors that are popular in each region. The preference reflection unit also analyzes fashion magazines and blogs from different cultures and regions, and the generation AI proposes designs based on that data. For example, it generates designs that reflect the trends of each region. The preference reflection unit also analyzes trend data from different cultures and regions on the cloud, and the generation AI proposes designs from a global perspective based on that data. For example, it generates designs that incorporate trends from international fashion shows. This makes it possible to combine trends from different cultures and regions to propose designs from a global perspective.

[0037] The preference reflection unit can combine designs from other consumers to generate a collaborative design. For example, the preference reflection unit stores designs created by other consumers in a database, and the generation AI proposes collaborative designs based on that data. For example, a design is generated that combines design elements from multiple consumers. The preference reflection unit also acquires designs from other consumers in real time, and the generation AI proposes collaborative designs based on that data. For example, it provides customization options that reference the designs of other consumers. The preference reflection unit also analyzes designs from other consumers on the cloud, and the generation AI proposes collaborative designs based on that data. For example, a design that incorporates the opinions of multiple consumers is generated. This makes it possible to combine designs from other consumers to generate collaborative designs.

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

[0039] The fashion design platform can further include an environmental impact assessment unit. The environmental impact assessment unit can evaluate the impact of a design on the environment and propose eco-friendly designs. For example, it can evaluate the environmental burden of the materials used and propose designs that prioritize the use of recyclable materials. It can also evaluate the energy consumption of the manufacturing process and propose designs that can be manufactured with low energy. It can also propose designs that take waste reduction into consideration, thereby realizing sustainable fashion.

[0040] Fashion design platforms can also include a virtual fitting area, which allows consumers to simulate trying on designs to check fit and appearance. For example, a 3D model can be generated based on the consumer's body data, and designs can be applied to the model to simulate trying on the design. A 360-degree view can also be provided so that consumers can view the design from different angles. Furthermore, a function can be provided to display multiple designs simultaneously, making it easier for consumers to compare different designs.

[0041] The fashion design platform may further include a trend forecasting unit. The trend forecasting unit may analyze past data and current market trends to predict future fashion trends. For example, it may analyze past sales data and social media posts to predict the styles and colors that will be popular in the next season. It may also collect information from fashion shows and industry events to propose designs that reflect the latest trends. It may also analyze consumer purchasing behavior to propose trend designs that are optimal for individual consumers.

[0042] The fashion design platform may further include a design sharing section. The design sharing section allows consumers to share designs they have created with other users and receive feedback. For example, consumers may publish their designs on social media or a dedicated platform and receive comments and ratings from other users. The design sharing section may also provide a function that allows consumers to view other users' designs and get inspiration. Furthermore, the design sharing section may provide a function that allows consumers to collaborate with other users to create designs.

[0043] The fashion design platform may further include a design history management unit. The design history management unit stores designs created by consumers in the past and designs proposed to them, allowing them to be referenced at any time. For example, designs created by consumers in the past may be stored in a database and reused as needed. The design history management unit may also provide a function that allows consumers to store designs proposed to them in the past and compare them. Furthermore, consumers may refer to the past design history when creating a new design.

[0044] The fashion design platform may further include a design education section. The design education section may provide educational content for consumers to learn the basics and trends of design. For example, the design education section may provide online courses and tutorials for learning basic design knowledge and techniques. The design education section may also provide information on the latest fashion trends and styles so that consumers can incorporate them into their own designs. The design education section may also provide a community where consumers can interact with other designers and experts and receive advice.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The design proposal unit proposes designs based on the consumer's preferences and requests. For example, if a consumer inputs, "I want a red dress with an open shoulder design," the generation AI will generate multiple design proposals based on that request. In the design proposal unit, the generation AI generates designs based on the consumer's input information. Step 2: The constraint consideration unit considers the constraints and rules of the sewing factory. For example, if there are constraints on specific materials or sewing techniques, the unit proposes a design that reflects those constraints. The constraint consideration unit allows the generation AI to generate a design by taking into account the constraints and rules of the sewing factory. Step 3: The preference reflection unit reflects the consumer's preferences and requests. For example, if a consumer inputs, "I want a casual style with a blue-based design," the generation AI will propose a casual style with a blue-based design based on that request. The preference reflection unit generates a design that reflects the consumer's preferences and requests.

[0047] (Example 2) A fashion design platform according to an embodiment of the present invention is a system that uses generative AI to enable consumers to design fashion items that suit their preferences and needs, thereby enabling consumers to easily design unique and original fashion items.

[0048] A fashion design platform according to an embodiment includes a design suggestion unit, a constraint consideration unit, and a preference reflection unit. The design suggestion unit proposes designs based on consumer preferences and requests. For example, if a consumer inputs, "I want a red dress with an off-the-shoulder design," the generation AI generates multiple design proposals based on that request. The design suggestion unit generates designs based on consumer input. The constraint consideration unit considers the constraints and rules of the sewing factory. For example, if there are constraints on specific materials or sewing techniques, the generation AI proposes designs that reflect those constraints. The constraint consideration unit generates designs based on the constraints and rules of the sewing factory. The preference reflection unit reflects consumer preferences and requests. For example, if a consumer inputs, "I want a casual style with a blue-based design," the generation AI proposes a casual style with a blue-based design based on that request. The preference reflection unit generates designs that reflect the consumer preferences and requests. This allows the fashion design platform to propose designs that consider the constraints and rules of the sewing factory based on the consumer preferences and requests.

[0049] The design suggestion unit can learn a consumer's past purchase history or preference trends and generate more personalized designs. For example, the design suggestion unit stores a consumer's past purchase history in a database, and the generation AI proposes designs based on that data. For example, it analyzes the color and style of previously purchased items to generate a design that suits the consumer's preferences. The design suggestion unit also learns designs that the consumer has previously rated and their saved favorite lists, and the generation AI proposes new designs based on that information. For example, it extracts features of highly rated designs and generates designs that reflect those features. The design suggestion unit also analyzes data on the cloud to learn a consumer's past purchase history and preference trends and proposes personalized designs. For example, it generates designs based on the brands and styles that the consumer frequently purchases. This allows the unit to learn a consumer's past purchase history and preference trends and generate more personalized designs.

[0050] The design proposal unit can generate designs that take fit into consideration by reflecting the consumer's body data. For example, the design proposal unit inputs the consumer's body data, and the generation AI proposes designs that take fit into consideration based on that data. For example, the optimal design is generated based on data such as height, weight, and waist size. The design proposal unit also converts the body data provided by the consumer into a 3D model, and the generation AI proposes designs that take fit into consideration based on that model. For example, the 3D model is used to perform a try-on simulation and generate the optimal design. The design proposal unit also stores the consumer's body data on the cloud, and the generation AI proposes designs that take fit into consideration based on that data. For example, the design proposal unit provides customization options tailored to body type, allowing the consumer to select a design that suits them best. This makes it possible to generate designs that reflect the consumer's body data and take fit into consideration.

[0051] The design proposal unit uses the emotion estimation function to analyze consumers' emotional responses in real time when they view a design proposal, and can prioritize suggesting designs that elicit positive emotions. For example, the design proposal unit uses a camera to capture the consumer's facial expression when they view a design proposal, and the generation AI analyzes the facial expression data to estimate their emotional response. For example, it detects smiling or surprised expressions and proposes designs that elicit positive emotions. The design proposal unit also analyzes the consumer's voice tone when they view a design proposal, and the generation AI estimates their emotional response based on the voice data. For example, it detects excited or happy voices and proposes designs that elicit positive emotions. The design proposal unit also uses sensors to acquire biometric data (such as heart rate and electrodermal response) when the consumer views a design proposal, and the generation AI analyzes the data to estimate their emotional response. For example, it detects an increase in heart rate or a change in electrodermal response and proposes designs that elicit positive emotions. This allows the design proposal unit to analyze consumers' emotional responses in real time when they view a design proposal, and prioritize suggesting designs that elicit positive emotions.

[0052] The constraint consideration unit updates the sewing factory's constraints and rules in real time, making it possible to generate designs that are compatible with the latest technologies and materials. For example, the constraint consideration unit manages the sewing factory's constraints and rules on the cloud and builds a system that updates them in real time. For example, the database is updated immediately when new materials or technologies are introduced. The constraint consideration unit also uses a generation AI to automatically obtain the sewing factory's constraints and rules, and based on that data, proposes designs that are compatible with the latest technologies and materials. For example, it generates designs that are compatible with new sewing technologies. The constraint consideration unit also updates the sewing factory's constraints and rules in real time, and based on that data, the generation AI proposes designs that are compatible with the latest technologies and materials. For example, it generates designs that reflect the characteristics of new materials. This makes it possible to update the sewing factory's constraints and rules in real time and generate designs that are compatible with the latest technologies and materials.

[0053] The constraint consideration unit can customize the design to suit local regulations or cultural backgrounds and generate region-specific designs. For example, the constraint consideration unit registers local regulations and cultural backgrounds in a database, and the generation AI proposes region-specific designs based on that data. For example, it generates designs that reflect styles and materials popular in a particular region. The constraint consideration unit also acquires local regulations and cultural backgrounds in real time, and the generation AI proposes customized designs based on that data. For example, it generates designs that incorporate traditional region patterns and colors. The constraint consideration unit also analyzes local regulations and cultural backgrounds on the cloud, and the generation AI proposes region-specific designs based on that data. For example, it generates designs that match local festivals and events. This makes it possible to customize the design to suit local regulations and cultural backgrounds and generate region-specific designs.

[0054] The constraint consideration unit can use the emotion estimation function to analyze the emotions of sewing factory employees and propose designs that improve work efficiency and motivation. For example, the constraint consideration unit acquires emotion data of sewing factory employees in real time, and the generation AI proposes designs that improve work efficiency and motivation based on that data. For example, it generates designs that reduce employee stress. The constraint consideration unit also uses the emotion estimation function to analyze the emotions of sewing factory employees and proposes designs that improve work efficiency and motivation based on the results. For example, it prioritizes the proposal of designs with high employee emotion scores. The constraint consideration unit also analyzes emotion data of sewing factory employees on the cloud, and the generation AI proposes designs that improve work efficiency and motivation based on that data. For example, it adjusts the design according to changes in employees' emotions. In this way, it is possible to analyze the emotions of sewing factory employees and propose designs that improve work efficiency and motivation.

[0055] The preference reflection unit can automatically collect information from social media posts or reviews and reflect it in the design. For example, the preference reflection unit analyzes social media posts and builds a system that automatically collects consumer preferences and trends. For example, designs are generated based on popular hashtags and keywords. The preference reflection unit also automatically collects consumer reviews, and the generation AI proposes designs based on that data. For example, design features are extracted based on highly rated reviews and a design is generated that reflects them. The preference reflection unit also analyzes social media posts and reviews on the cloud, and the generation AI proposes designs that reflect consumer preferences and requests based on that data. For example, it performs trend analysis and generates designs that incorporate the latest fashions. This makes it possible to automatically collect information from social media posts and reviews and reflect it in the design.

[0056] The preference reflection unit can reflect the opinions of multiple people, including those of family or friends. For example, the preference reflection unit provides a function that allows consumers to input the opinions of family and friends, and the generation AI proposes designs based on that data. For example, a design that reflects the opinions of multiple people is generated. The preference reflection unit also collects the opinions of family and friends in real time, and the generation AI proposes designs based on that data. For example, a design that takes the preferences of family and friends into consideration is generated. The preference reflection unit also analyzes the opinions of family and friends on the cloud, and the generation AI proposes designs that reflect the perspectives of multiple people based on that data. For example, it provides customization options that incorporate the opinions of family and friends. This makes it possible to reflect the perspectives of multiple people, including those of family and friends.

[0057] The preference reflection unit uses the emotion estimation function to analyze the emotional response of consumers when they view a design and generate a design that emphasizes elements that evoke positive emotions. For example, the preference reflection unit uses a camera to capture the consumer's facial expression when they view a design, and the generation AI analyzes the facial expression data to estimate the emotional response. For example, it detects smiling or surprised expressions and generates a design that emphasizes elements that evoke positive emotions. The preference reflection unit also analyzes the tone of the consumer's voice when they view a design, and the generation AI estimates the emotional response based on the voice data. For example, it detects excited or happy voices and generates a design that emphasizes elements that evoke positive emotions. The preference reflection unit also uses sensors to acquire biometric data (such as heart rate and electrodermal response) when the consumer views the design, and the generation AI analyzes the data to estimate the emotional response. For example, it detects an increase in heart rate or a change in electrodermal response and generates a design that emphasizes elements that evoke positive emotions. This makes it possible to analyze the emotional response of consumers when they view a design and generate a design that emphasizes elements that evoke positive emotions.

[0058] The preference reflection unit can combine trends from different cultures and regions to propose designs from a global perspective. For example, the preference reflection unit collects trend data from different cultures and regions, and the generation AI proposes designs from a global perspective based on that data. For example, it generates designs that incorporate styles and colors that are popular in each region. The preference reflection unit also analyzes fashion magazines and blogs from different cultures and regions, and the generation AI proposes designs based on that data. For example, it generates designs that reflect the trends of each region. The preference reflection unit also analyzes trend data from different cultures and regions on the cloud, and the generation AI proposes designs from a global perspective based on that data. For example, it generates designs that incorporate trends from international fashion shows. This makes it possible to combine trends from different cultures and regions to propose designs from a global perspective.

[0059] The preference reflection unit can combine designs from other consumers to generate a collaborative design. For example, the preference reflection unit stores designs created by other consumers in a database, and the generation AI proposes collaborative designs based on that data. For example, a design is generated that combines design elements from multiple consumers. The preference reflection unit also acquires designs from other consumers in real time, and the generation AI proposes collaborative designs based on that data. For example, it provides customization options that reference the designs of other consumers. The preference reflection unit also analyzes designs from other consumers on the cloud, and the generation AI proposes collaborative designs based on that data. For example, a design that incorporates the opinions of multiple consumers is generated. This makes it possible to combine designs from other consumers to generate collaborative designs.

[0060] The preference reflection unit uses the emotion estimation function to analyze the emotional reactions of consumers when they view a design and shares the data with other users, thereby enabling the unit to propose designs that evoke empathy. The preference reflection unit, for example, analyzes the emotional reactions of consumers when they view a design in real time and shares the data with other users. For example, designs that receive a high number of positive emotional reactions are shared on social media. The preference reflection unit also uses the emotion estimation function to analyze the emotional reactions of consumers when they view a design and, based on the results, proposes designs that evoke empathy. For example, designs with high emotional scores are preferentially proposed. The preference reflection unit also analyzes consumer emotional reaction data on the cloud and shares the data with other users to propose designs that evoke empathy. For example, designs with high emotional scores are shared within the community. In this way, the emotional reactions of consumers when they view a design are analyzed and shared with other users, enabling the unit to propose designs that evoke empathy.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The fashion design platform can further include an environmental impact assessment unit. The environmental impact assessment unit can evaluate the impact of a design on the environment and propose eco-friendly designs. For example, it can evaluate the environmental burden of the materials used and propose designs that prioritize the use of recyclable materials. It can also evaluate the energy consumption of the manufacturing process and propose designs that can be manufactured with low energy. It can also propose designs that take waste reduction into consideration, thereby realizing sustainable fashion.

[0063] Fashion design platforms can also include a virtual fitting area, which allows consumers to simulate trying on designs to check fit and appearance. For example, a 3D model can be generated based on the consumer's body data, and designs can be applied to the model to simulate trying on the design. A 360-degree view can also be provided so that consumers can view the design from different angles. Furthermore, a function can be provided to display multiple designs simultaneously, making it easier for consumers to compare different designs.

[0064] The fashion design platform may further include a trend forecasting unit. The trend forecasting unit may analyze past data and current market trends to predict future fashion trends. For example, it may analyze past sales data and social media posts to predict the styles and colors that will be popular in the next season. It may also collect information from fashion shows and industry events to propose designs that reflect the latest trends. It may also analyze consumer purchasing behavior to propose trend designs that are optimal for individual consumers.

[0065] The fashion design platform can also use its emotion estimation function to analyze the emotional reactions of consumers when they view designs and suggest designs that have a stress-reducing or relaxing effect. For example, it can analyze the consumer's facial expression and voice tone when looking at a design, and if it detects a relaxed expression or calm voice, it can suggest designs that evoke those emotions. It can also analyze the consumer's biometric data (heart rate, skin galvanic response, etc.) to suggest designs that reduce stress. It can also suggest designs that incorporate colors and patterns that relax the consumer.

[0066] The fashion design platform can also use emotion estimation to analyze consumers' emotional reactions when they view designs and suggest designs that match the season or event. For example, if the platform analyzes the consumer's facial expressions and tone of voice when viewing a design and detects seasonal changes in emotion, it can suggest designs that match that emotion. It can also analyze consumers' biometric data to suggest designs that match specific events (such as weddings or parties). It can also prioritize suggestions for designs that consumers have positive emotions about for specific seasons or events.

[0067] The fashion design platform can also use emotion estimation to analyze consumers' emotional reactions when viewing designs and suggest designs that take their health into consideration. For example, if it analyzes a consumer's facial expressions and tone of voice when viewing a design and detects changes in their emotional state related to their health, it can suggest designs that take those emotions into consideration. It can also analyze consumers' biometric data to suggest designs that are tailored to their health. It can also prioritize suggestions for designs that help consumers live a healthy lifestyle (for example, designs that are breathable and easy to move in).

[0068] The fashion design platform can further use emotion estimation functions to analyze consumers' emotional reactions when viewing designs and suggest designs that reflect their feelings toward a particular brand or designer. For example, if the platform analyzes a consumer's facial expressions and tone of voice when viewing a design by a particular brand or designer and detects positive feelings toward that brand or designer, it can suggest designs that reflect those emotions. It can also analyze consumers' biometric data to suggest designs that reflect their feelings toward a particular brand or designer. It can also prioritize suggestions for designs that consumers have positive feelings toward a particular brand or designer.

[0069] The fashion design platform may further include a design sharing section. The design sharing section allows consumers to share designs they have created with other users and receive feedback. For example, consumers may publish their designs on social media or a dedicated platform and receive comments and ratings from other users. The design sharing section may also provide a function that allows consumers to view other users' designs and get inspiration. Furthermore, the design sharing section may provide a function that allows consumers to collaborate with other users to create designs.

[0070] The fashion design platform may further include a design history management unit. The design history management unit stores designs created by consumers in the past and designs proposed to them, allowing them to be referenced at any time. For example, designs created by consumers in the past may be stored in a database and reused as needed. The design history management unit may also provide a function that allows consumers to store designs proposed to them in the past and compare them. Furthermore, consumers may refer to the past design history when creating a new design.

[0071] The fashion design platform may further include a design education section. The design education section may provide educational content for consumers to learn the basics and trends of design. For example, the design education section may provide online courses and tutorials for learning basic design knowledge and techniques. The design education section may also provide information on the latest fashion trends and styles so that consumers can incorporate them into their own designs. The design education section may also provide a community where consumers can interact with other designers and experts and receive advice.

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

[0073] Step 1: The design proposal unit proposes designs based on the consumer's preferences and requests. For example, if a consumer inputs, "I want a red dress with an open shoulder design," the generation AI will generate multiple design proposals based on that request. In the design proposal unit, the generation AI generates designs based on the consumer's input information. Step 2: The constraint consideration unit considers the constraints and rules of the sewing factory. For example, if there are constraints on specific materials or sewing techniques, the unit proposes a design that reflects those constraints. The constraint consideration unit allows the generation AI to generate a design by taking into account the constraints and rules of the sewing factory. Step 3: The preference reflection unit reflects the consumer's preferences and requests. For example, if a consumer inputs, "I want a casual style with a blue-based design," the generation AI will propose a casual style with a blue-based design based on that request. The preference reflection unit generates a design that reflects the consumer's preferences and requests.

[0074] 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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. Using generative AI, The Design Proposal Department proposes designs based on consumer preferences and requests, and a constraint consideration unit that considers constraints and rules of a sewing factory; A preference reflection unit that reflects the preferences and requests of the consumer. A system characterized by:

2. The design proposal section Learn about the consumer's past purchase history or preferences to generate more personalized designs 2. The system of claim 1.

3. The design proposal section Generate designs that take fit into consideration by reflecting the consumer's body shape data 2. The system of claim 1.

4. The design proposal section Analyze the emotional reactions of the consumers when they view the design proposals in real time, and prioritize proposals that evoke positive emotions.

2. The system of claim 1.

5. The constraint consideration unit Update the sewing factory's constraints and rules in real time to generate designs that are compatible with the latest technologies and materials 2. The system of claim 1.

6. The constraint consideration unit Generate localized designs tailored to local regulations or cultural contexts 2. The system of claim 1.

7. The constraint consideration unit Analyze the emotions of the sewing factory employees and propose designs that will improve work efficiency and motivation.

2. The system of claim 1.

8. The preference reflection unit Automatically collect social media posts or reviews and incorporate them into your design 2. The system of claim 1.

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