system

Abstract

The system according to the embodiment aims to efficiently produce custom-made clothing based on customer body type data. [Solution] A system according to an embodiment includes an input unit, a proposal unit, a generation unit, and a production unit. The input unit inputs customer body type data. The proposal unit proposes a design based on the body type data input by the input unit. The generation unit generates a pattern based on the design proposed by the proposal unit. The production unit sends the pattern generated by the generation unit to a production line.

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 made it difficult to provide custom-made clothing that meets individual customer needs, and production has not been efficient.

[0005] The system according to the embodiment aims to efficiently produce custom-made clothing based on customer body type data. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a proposal unit, a generation unit, and a production unit. The input unit inputs customer body type data. The proposal unit proposes a design based on the body type data input by the input unit. The generation unit generates a pattern based on the design proposed by the proposal unit. The production unit sends the pattern generated by the generation unit to a production line. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently produce custom-made clothing based on the customer's body type data. [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 custom-made clothing service system according to an embodiment of the present invention utilizes AI to meet individual customer needs. In this system, customers register their body type and enter their clothing preferences and intended use. AI then suggests designs and styling. When a customer places an order, AI generates patterns based on the design and automatically sends them to the production line. This system improves customer satisfaction through customization while improving efficiency through automated pattern design. For example, a customer registers their body type, entering detailed body data such as height, weight, chest circumference, waist circumference, and hip circumference. For example, by entering their body type data, the service can suggest optimal designs based on that data. Next, the customer enters their clothing preferences and intended use. For example, they can enter specific requests, such as a preference for casual clothing or a need for a business suit. This information is input into AI. The AI ​​then suggests designs and styling based on the entered body type data, clothing preferences, and intended use. For example, if a customer prefers casual clothing, AI suggests the most suitable casual design for that customer. Similarly, if a customer needs a business suit, AI suggests the most suitable suit design for that customer. When a customer places an order based on a proposed design, AI generates a pattern based on that design. For example, a specific pattern is generated based on the AI's proposed casual design. This pattern is then automatically sent to the production line. This system improves customer satisfaction through custom-made products. Customers are able to obtain designs tailored to their body type and preferences, increasing their satisfaction. Furthermore, automated pattern design improves efficiency and reduces production costs. Furthermore, the shortening of fashion trend cycles and mass production with the assumption of disposal have led to the problem of large amounts of clothing waste. This invention realizes fashion with a low environmental impact by providing fully made-to-order, affordable apparel services. For example, by producing only the amount ordered by the customer, excess inventory is eliminated, which is expected to reduce waste.This allows the custom-made clothing service system to efficiently provide custom-made clothing services by proposing designs based on the customer's body data, generating patterns, and sending them to the production line.

[0029] A custom-made clothing service system according to an embodiment includes an input unit, a proposal unit, a generation unit, and a production unit. The input unit inputs customer body data. The customer body data includes, but is not limited to, height, weight, chest circumference, waist circumference, and hip circumference. The input unit provides, for example, an interface through which the customer inputs their body data. The input unit can also use AI to analyze the customer's body data and generate data for optimal design proposals. The proposal unit proposes designs based on the body data input by the input unit. The proposal unit can, for example, use AI to propose designs and styling based on the customer's body data, clothing preferences, and uses. For example, if a customer prefers casual clothing, the proposal unit can use AI to propose a casual design that is optimal for the customer. For example, if a customer needs a business suit, the proposal unit can use AI to propose a suit design that is optimal for the customer. The generation unit generates a pattern based on the design proposed by the proposal unit. For example, the generation unit can use AI to generate a specific pattern based on the proposed design. For example, the generation unit can generate a specific pattern based on the casual design proposed by AI. The production unit sends the patterns generated by the generation unit to the production line. The production unit can automatically send the generated patterns to the production line using, for example, AI. The production unit can improve production efficiency by automatically sending the generated patterns to the production line. As a result, the custom-made clothing service system according to the embodiment can efficiently provide a custom-made clothing service by proposing designs based on customer body data, generating patterns, and sending them to the production line. Some or all of the above-described processing in the production unit may be performed using, for example, AI, or may be performed without using AI. For example, the production unit can send the generated patterns to the production line using an AI model for sending them to the production line. For example, the output unit provides customers with clothing produced based on the generated patterns. The output unit can provide the customers with the patterns via, for example, a web application or a mobile application.The output unit can also provide feedback in paper form if the customer so desires. This allows the custom-made clothing service system according to the embodiment to efficiently provide a custom-made clothing service by proposing designs based on the customer's body data, generating patterns, and sending them to the production line.

[0030] The input unit can input detailed body type data of a customer, such as height, weight, chest circumference, waist, and hips. Detailed body type data includes, but is not limited to, height, weight, chest circumference, waist, and hips. The input unit, for example, provides an interface through which the customer inputs their body type data. For example, the input unit may provide a form through which the customer inputs their body type data. The input unit can also use AI to analyze the customer's body type data and generate data for optimal design proposals. For example, the input unit generates data for optimal design proposals based on the body type data input by the customer. This allows for more accurate design proposals to be made by inputting detailed body type data. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without AI. For example, the input unit can input the body type data input by the customer into AI, and the AI ​​can generate data for optimal design proposals.

[0031] The suggestion unit can suggest designs and styling based on the input body type data and clothing preferences and uses. The suggestion unit, for example, uses AI to suggest designs and styling based on the input body type data and clothing preferences and uses. For example, if a customer prefers casual clothing, the suggestion unit can suggest the most suitable casual design for that customer. Also, if a business suit is needed, the suggestion unit can suggest the most suitable suit design for that customer. The suggestion unit, for example, uses an algorithm for the AI ​​to suggest designs and styling based on the input body type data and clothing preferences and uses. For example, the suggestion unit uses an algorithm for the AI ​​to suggest designs and styling based on the input body type data and clothing preferences and uses. This allows the AI ​​to suggest the most suitable design based on the customer's body type data and preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the input body type data and clothing preferences and uses into AI, and the AI ​​can suggest the most suitable design.

[0032] The generation unit can generate a specific pattern based on a proposed design. The generation unit, for example, uses AI to generate a specific pattern based on the proposed design. For example, the generation unit can generate a specific pattern based on a casual design proposed by the AI. The generation unit, for example, uses an algorithm for generating a specific pattern based on a design proposed by the AI. For example, the generation unit uses an algorithm for generating a specific pattern based on a design proposed by the AI. This enables efficient production by generating a specific pattern based on the proposed design. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the proposed design into AI, which then generates a specific pattern.

[0033] The production department can automatically send the generated patterns to the production line. The production department can automatically send the generated patterns to the production line using, for example, AI. For example, the production department uses an algorithm for automatically sending the generated patterns to the production line. For example, the production department uses an algorithm for automatically sending the generated patterns to the production line using AI. In this way, by automatically sending the generated patterns to the production line, production efficiency is improved. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the generated patterns to AI, which then automatically sends them to the production line.

[0034] The production department produces only the amount ordered by the customer, which prevents excess inventory and reduces waste. The production department, for example, uses an algorithm using AI to produce only the amount ordered by the customer. For example, the production department uses an algorithm in which AI adjusts production volume based on customer order data. As a result, only the amount ordered by the customer is produced, which prevents excess inventory and is expected to reduce waste. Some or all of the above-mentioned processing in the production department may be performed using AI, for example, or may be performed without using AI. For example, the production department can input customer order data into AI, which can adjust production volume.

[0035] The input unit can analyze the customer's past body type data and select the optimal input method. The input unit, for example, provides an auto-completion function to reduce the effort of input based on body type data previously input by the customer. For example, the input unit provides an auto-completion function to reduce the effort of input based on body type data previously input by the customer. The input unit can also automatically input data with little fluctuation from the customer's past body type data. For example, the input unit can automatically input data with little fluctuation from the customer's past body type data. The input unit can also analyze the customer's past body type data and optimize the input order. For example, the input unit can analyze the customer's past body type data and optimize the input order. In this way, the effort of input can be reduced by analyzing the past body type data. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the customer's past body type data into AI, which can select the optimal input method.

[0036] The input unit can filter the body type data based on the customer's current health condition and lifestyle habits when inputting the body type data. The input unit, for example, inputs only necessary body type data based on the customer's current health condition. For example, the input unit inputs only necessary body type data based on the customer's current health condition. The input unit can also determine the priority of the body type data to be input based on the customer's lifestyle habits. For example, the input unit can also determine the priority of the body type data to be input based on the customer's lifestyle habits. The input unit can also adjust the range of body type data to be input based on the customer's health condition and lifestyle habits. For example, the input unit can adjust the range of body type data to be input based on the customer's health condition and lifestyle habits. This enables efficient data input by inputting only necessary data based on the health condition and lifestyle habits. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input data on the customer's health condition and lifestyle habits into AI, which can then perform filtering.

[0037] When inputting body type data, the input unit can prioritize inputting highly relevant data by taking into account the customer's geographical location information. For example, if the customer lives in a cold region, the input unit prioritizes inputting body type data for winter clothes. For example, if the customer lives in a cold region, the input unit prioritizes inputting body type data for winter clothes. Furthermore, if the customer lives in a warm region, the input unit can also prioritize inputting body type data for summer clothes. For example, if the customer lives in a warm region, the input unit can also prioritize inputting body type data for summer clothes. Furthermore, if the customer lives in an urban area, the input unit can also prioritize inputting body type data for business clothes. For example, if the customer lives in an urban area, the input unit can also prioritize inputting body type data for business clothes. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the customer's geographical location information to AI, which can then prioritize inputting highly relevant data.

[0038] When inputting body type data, the input unit can analyze the customer's social media activities and input related data. The input unit, for example, analyzes photos shared by the customer on social media to complement the body type data. For example, the input unit analyzes photos shared by the customer on social media to complement the body type data. The input unit can also assist in the input of body type data from the content of the customer's social media posts. For example, the input unit can assist in the input of body type data from the content of the customer's social media posts. The input unit can also optimize the input of body type data from the customer's social media activities. In this way, related data can be complemented by analyzing social media activities. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data of the customer's social media activities to AI, and the AI ​​can input related data.

[0039] When proposing a design, the suggestion unit can suggest an optimal design by referring to the customer's past purchase history. For example, the suggestion unit can suggest a similar design based on a design purchased by the customer in the past. For example, the suggestion unit can suggest a similar design based on a design purchased by the customer in the past. The suggestion unit can also analyze preference trends from the customer's past purchase history to suggest a design. For example, the suggestion unit can analyze preference trends from the customer's past purchase history to suggest a design. The suggestion unit can also propose a new design by combining it with a design purchased by the customer in the past. For example, the suggestion unit can propose a new design by combining it with a design purchased by the customer in the past. In this way, by referring to the past purchase history, a design that suits the customer's preferences can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the customer's past purchase history into AI, which can then suggest an optimal design.

[0040] When proposing a design, the proposal unit can customize the proposal content based on the customer's current fashion trends. For example, the proposal unit proposes a design that matches the customer's current fashion trends. The proposal unit can also propose a design that incorporates the latest fashion trends based on the customer's preferences. For example, the proposal unit can propose a design that incorporates the latest fashion trends based on the customer's preferences. The proposal unit can also analyze the customer's current fashion trends and propose an optimal design. For example, the proposal unit can analyze the customer's current fashion trends and propose an optimal design. In this way, by customizing the proposal content based on the current fashion trends, a design that incorporates the latest trends can be proposed. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the customer's current fashion trends into AI, which can then propose an optimal design.

[0041] The suggestion unit can propose an optimal design by taking into consideration the customer's geographical location information when proposing a design. For example, if the customer lives in a cold region, the suggestion unit can preferentially propose winter clothing designs. For example, if the customer lives in a cold region, the suggestion unit can preferentially propose winter clothing designs. The suggestion unit can also preferentially propose summer clothing designs if the customer lives in a warm region. For example, if the customer lives in a warm region, the suggestion unit can also preferentially propose summer clothing designs. The suggestion unit can also preferentially propose business clothing designs if the customer lives in an urban area. For example, the suggestion unit can preferentially propose business clothing designs if the customer lives in an urban area. In this way, by taking the geographical location information into consideration, highly relevant designs can be proposed. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the customer's geographical location information into AI, which can then suggest an optimal design.

[0042] When proposing a design, the suggestion unit can analyze a customer's social media activity and suggest a relevant design. For example, the suggestion unit can analyze photos shared by a customer on social media and suggest a design. For example, the suggestion unit can analyze photos shared by a customer on social media and suggest a design. The suggestion unit can also analyze a customer's design preferences from their social media posts and suggest a design. For example, the suggestion unit can analyze a customer's design preferences from their social media posts and suggest a design. The suggestion unit can also suggest an optimal design from the customer's social media activity. For example, the suggestion unit can suggest an optimal design from the customer's social media activity. In this way, related designs can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on a customer's social media activity into AI, which can suggest related designs.

[0043] When generating a pattern, the generation unit can generate an optimal pattern by referring to the customer's past design history. For example, the generation unit generates a similar pattern based on a pattern used by the customer in the past. The generation unit can also generate a pattern by analyzing preferences from the customer's past design history. For example, the generation unit can generate a pattern by analyzing preferences from the customer's past design history. The generation unit can also generate a new pattern by combining a pattern used by the customer in the past. For example, the generation unit can generate a new pattern by combining a pattern used by the customer in the past. By referring to the past design history, a pattern that matches the customer's preferences can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the customer's past design history into AI, which then generates an optimal pattern.

[0044] The generation unit can customize the pattern based on the customer's current body shape data when generating the pattern. The generation unit, for example, generates an optimal pattern based on the customer's current body shape data. For example, the generation unit generates an optimal pattern based on the customer's current body shape data. The generation unit can also generate a well-fitting pattern based on the customer's body shape data. For example, the generation unit can generate a well-fitting pattern based on the customer's body shape data. The generation unit can also analyze the customer's body shape data and adjust the size of the pattern. For example, the generation unit can analyze the customer's body shape data and adjust the size of the pattern. In this way, a well-fitting pattern can be generated by customizing the pattern based on the current body shape data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's current body shape data into AI, which can customize the pattern.

[0045] The generation unit can generate an optimal pattern by taking into account the customer's geographical location information when generating a pattern. For example, if the customer lives in a cold region, the generation unit preferentially generates a pattern for winter clothing. For example, if the customer lives in a cold region, the generation unit preferentially generates a pattern for winter clothing. The generation unit can also preferentially generate a pattern for summer clothing if the customer lives in a warm region. For example, if the customer lives in a warm region, the generation unit can also preferentially generate a pattern for summer clothing. The generation unit can also preferentially generate a pattern for business clothing if the customer lives in an urban area. For example, if the customer lives in an urban area, the generation unit can also preferentially generate a pattern for business clothing. In this way, by taking the geographical location information into consideration, a highly relevant pattern can be generated. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's geographical location information into AI, which can then generate an optimal pattern.

[0046] When generating a pattern, the generation unit can analyze the customer's social media activity and generate a related pattern. The generation unit, for example, analyzes photos shared by the customer on social media to generate a pattern. For example, the generation unit analyzes photos shared by the customer on social media to generate a pattern. The generation unit can also generate a pattern by analyzing the customer's social media post content. For example, the generation unit can generate a pattern by analyzing the customer's social media post content. The generation unit can also generate an optimal pattern from the customer's social media activity. For example, the generation unit can generate an optimal pattern from the customer's social media activity. In this way, related patterns can be generated by analyzing social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the customer's social media activity into AI, which then generates related patterns.

[0047] During production, the production department can select the optimal production method by referring to the customer's past order history. For example, the production department selects the same production method as for products ordered by the customer in the past. For example, the production department selects the same production method as for products ordered by the customer in the past. The production department can also determine an optimal production schedule from the customer's past order history. For example, the production department can determine an optimal production schedule from the customer's past order history. The production department can also analyze the customer's past order history and select the most efficient production method. For example, the production department can analyze the customer's past order history and select the most efficient production method. In this way, an efficient production method can be selected by referring to the past order history. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the customer's past order history into AI, which selects the optimal production method.

[0048] During production, the production department can customize the production volume based on the customer's current demand. For example, the production department adjusts the production volume based on the customer's current demand. The production department can also optimize the production volume based on the customer's demand forecast. For example, the production department can also optimize the production volume based on the customer's demand forecast. The production department can also analyze the customer's current demand and determine the required production volume. For example, the production department can analyze the customer's current demand and determine the required production volume. This enables efficient production by customizing the production volume based on the current demand. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the customer's current demand data into AI, which can customize the production volume.

[0049] During production, the production department can select the optimal production method by taking into account the customer's geographical location information. For example, if the customer lives in a cold region, the production department prioritizes the production of winter clothing. For example, if the customer lives in a cold region, the production department prioritizes the production of winter clothing. The production department can also prioritize the production of summer clothing if the customer lives in a warm region. For example, if the customer lives in a warm region, the production department can also prioritize the production of summer clothing. The production department can also prioritize the production of business clothing if the customer lives in an urban area. In this way, by taking the geographical location information into account, a highly relevant production method can be selected. Some or all of the above-mentioned processes in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the customer's geographical location information into AI, which can select the optimal production method.

[0050] The production department can analyze customers' social media activities during production and create related production plans. For example, the production department creates production plans based on information shared by customers on social media. For example, the production department creates production plans based on information shared by customers on social media. The production department can also predict demand and create production plans based on the content of customers' social media posts. For example, the production department can predict demand and create production plans based on the content of customers' social media posts. The production department can also analyze customers' social media activities and create optimal production plans. For example, the production department can analyze customers' social media activities and create optimal production plans. In this way, related production plans can be created by analyzing social media activities. Some or all of the above-mentioned processes in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input data on customers' social media activities into AI, which can then create related production plans.

[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 custom-made clothing service system can further include a feedback unit. The feedback unit can collect customer evaluations and impressions of the clothing items they receive and reflect them in the next design proposal. For example, the feedback unit can provide an interface for customers to input their evaluations of the clothing's fit and design. The feedback unit can also analyze customer evaluation data and generate data to further improve the customer's preferences and fit in the next proposal. This makes it possible to utilize customer feedback to provide a more satisfying custom-made clothing service.

[0053] The input unit can provide real-time feedback when a customer enters their body data. For example, the input unit can instantly display a fit prediction based on the body data entered by the customer. The input unit can also provide suggestions for improvement or supplemental information for the data entered by the customer. For example, if the waist size entered by the customer is inconsistent with other data, the input unit can point out the inconsistency and prompt the customer to re-enter. This allows for more accurate body data entry, resulting in optimal design proposals.

[0054] The suggestion unit can present multiple design options based on the customer's body type data and clothing preferences and uses. For example, the suggestion unit can suggest multiple casual designs in different styles and colors to a customer who prefers casual clothing. The suggestion unit can also suggest suit designs with different cuts and materials to a customer who needs a business suit. Furthermore, the suggestion unit can suggest customizable elements (e.g., pocket position or button type) based on the design options selected by the customer. This allows the customer to select the optimal design that suits their preferences.

[0055] The generation unit can select materials that take environmental impact into consideration when generating patterns based on proposed designs. For example, the generation unit generates patterns using renewable materials and eco-friendly dyes. The generation unit can also perform efficient pattern placement to minimize waste. For example, the generation unit can generate patterns with optimal placement to reduce fabric waste. This makes it possible to provide an environmentally friendly custom-made clothing service.

[0056] When the generated patterns are sent to the production line, the production department can monitor the production status in real time and make adjustments as necessary. For example, the production department can monitor the machine operation status and production progress and respond immediately if an abnormality occurs. The production department can also dynamically adjust the work schedule to maximize the efficiency of the production line. For example, the production department can update the production schedule in real time to respond to sudden order changes or additional orders. This enables an efficient and flexible production system.

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

[0058] Step 1: The input unit inputs the customer's body data. This data includes, for example, height, weight, chest circumference, waist circumference, and hip circumference. The input unit provides an interface for customers to input their body data, and can also use AI to analyze the customer's body data and generate data for optimal design proposals. Step 2: The proposal unit proposes designs based on the body type data entered by the input unit. The proposal unit uses AI to suggest designs and styling based on the customer's body type data, clothing preferences, and use. For example, if a customer prefers casual clothing, the AI ​​will suggest the most suitable casual design for that customer, and if a business suit is needed, the AI ​​will suggest the most suitable suit design. Step 3: The generator generates a pattern based on the design proposed by the suggester. The generator generates a specific pattern using AI. For example, the generator can generate a specific pattern based on the casual design proposed by AI. Step 4: The production department sends the patterns generated by the generation department to the production line. The production department can automatically send the patterns generated using AI to the production line, thereby improving production efficiency.

[0059] (Example 2) A custom-made clothing service system according to an embodiment of the present invention utilizes AI to meet individual customer needs. In this system, customers register their body type and enter their clothing preferences and intended use. AI then suggests designs and styling. When a customer places an order, AI generates patterns based on the design and automatically sends them to the production line. This system improves customer satisfaction through customization while improving efficiency through automated pattern design. For example, a customer registers their body type, entering detailed body data such as height, weight, chest circumference, waist circumference, and hip circumference. For example, by entering their body type data, the service can suggest optimal designs based on that data. Next, the customer enters their clothing preferences and intended use. For example, they can enter specific requests, such as a preference for casual clothing or a need for a business suit. This information is input into AI. The AI ​​then suggests designs and styling based on the entered body type data, clothing preferences, and intended use. For example, if a customer prefers casual clothing, AI suggests the most suitable casual design for that customer. Similarly, if a customer needs a business suit, AI suggests the most suitable suit design for that customer. When a customer places an order based on a proposed design, AI generates a pattern based on that design. For example, a specific pattern is generated based on the AI's proposed casual design. This pattern is then automatically sent to the production line. This system improves customer satisfaction through custom-made products. Customers are able to obtain designs tailored to their body type and preferences, increasing their satisfaction. Furthermore, automated pattern design improves efficiency and reduces production costs. Furthermore, the shortening of fashion trend cycles and mass production with the assumption of disposal have led to the problem of large amounts of clothing waste. This invention realizes fashion with a low environmental impact by providing fully made-to-order, affordable apparel services. For example, by producing only the amount ordered by the customer, excess inventory is eliminated, which is expected to reduce waste.This allows the custom-made clothing service system to efficiently provide custom-made clothing services by proposing designs based on the customer's body data, generating patterns, and sending them to the production line.

[0060] A custom-made clothing service system according to an embodiment includes an input unit, a proposal unit, a generation unit, and a production unit. The input unit inputs customer body data. The customer body data includes, but is not limited to, height, weight, chest circumference, waist circumference, and hip circumference. The input unit provides, for example, an interface through which the customer inputs their body data. The input unit can also use AI to analyze the customer's body data and generate data for optimal design proposals. The proposal unit proposes designs based on the body data input by the input unit. The proposal unit can, for example, use AI to propose designs and styling based on the customer's body data, clothing preferences, and uses. For example, if a customer prefers casual clothing, the proposal unit can use AI to propose a casual design that is optimal for the customer. For example, if a customer needs a business suit, the proposal unit can use AI to propose a suit design that is optimal for the customer. The generation unit generates a pattern based on the design proposed by the proposal unit. For example, the generation unit can use AI to generate a specific pattern based on the proposed design. For example, the generation unit can generate a specific pattern based on the casual design proposed by AI. The production unit sends the patterns generated by the generation unit to the production line. The production unit can automatically send the generated patterns to the production line using, for example, AI. The production unit can improve production efficiency by automatically sending the generated patterns to the production line. As a result, the custom-made clothing service system according to the embodiment can efficiently provide a custom-made clothing service by proposing designs based on customer body data, generating patterns, and sending them to the production line. Some or all of the above-described processing in the production unit may be performed using, for example, AI, or may be performed without using AI. For example, the production unit can send the generated patterns to the production line using an AI model for sending them to the production line. For example, the output unit provides customers with clothing produced based on the generated patterns. The output unit can provide the customers with the patterns via, for example, a web application or a mobile application.The output unit can also provide feedback in paper form if the customer so desires. This allows the custom-made clothing service system according to the embodiment to efficiently provide a custom-made clothing service by proposing designs based on the customer's body data, generating patterns, and sending them to the production line.

[0061] The input unit can input detailed body type data of a customer, such as height, weight, chest circumference, waist, and hips. Detailed body type data includes, but is not limited to, height, weight, chest circumference, waist, and hips. The input unit, for example, provides an interface through which the customer inputs their body type data. For example, the input unit may provide a form through which the customer inputs their body type data. The input unit can also use AI to analyze the customer's body type data and generate data for optimal design proposals. For example, the input unit generates data for optimal design proposals based on the body type data input by the customer. This allows for more accurate design proposals to be made by inputting detailed body type data. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without AI. For example, the input unit can input the body type data input by the customer into AI, and the AI ​​can generate data for optimal design proposals.

[0062] The suggestion unit can suggest designs and styling based on the input body type data and clothing preferences and uses. The suggestion unit, for example, uses AI to suggest designs and styling based on the input body type data and clothing preferences and uses. For example, if a customer prefers casual clothing, the suggestion unit can suggest the most suitable casual design for that customer. Also, if a business suit is needed, the suggestion unit can suggest the most suitable suit design for that customer. The suggestion unit, for example, uses an algorithm for the AI ​​to suggest designs and styling based on the input body type data and clothing preferences and uses. For example, the suggestion unit uses an algorithm for the AI ​​to suggest designs and styling based on the input body type data and clothing preferences and uses. This allows the AI ​​to suggest the most suitable design based on the customer's body type data and preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the input body type data and clothing preferences and uses into AI, and the AI ​​can suggest the most suitable design.

[0063] The generation unit can generate a specific pattern based on a proposed design. The generation unit, for example, uses AI to generate a specific pattern based on the proposed design. For example, the generation unit can generate a specific pattern based on a casual design proposed by the AI. The generation unit, for example, uses an algorithm for generating a specific pattern based on a design proposed by the AI. For example, the generation unit uses an algorithm for generating a specific pattern based on a design proposed by the AI. This enables efficient production by generating a specific pattern based on the proposed design. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the proposed design into AI, which then generates a specific pattern.

[0064] The production department can automatically send the generated patterns to the production line. The production department can automatically send the generated patterns to the production line using, for example, AI. For example, the production department uses an algorithm for automatically sending the generated patterns to the production line. For example, the production department uses an algorithm for automatically sending the generated patterns to the production line using AI. In this way, by automatically sending the generated patterns to the production line, production efficiency is improved. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the generated patterns to AI, which then automatically sends them to the production line.

[0065] The production department produces only the amount ordered by the customer, which prevents excess inventory and reduces waste. The production department, for example, uses an algorithm using AI to produce only the amount ordered by the customer. For example, the production department uses an algorithm in which AI adjusts production volume based on customer order data. As a result, only the amount ordered by the customer is produced, which prevents excess inventory and is expected to reduce waste. Some or all of the above-mentioned processing in the production department may be performed using AI, for example, or may be performed without using AI. For example, the production department can input customer order data into AI, which can adjust production volume.

[0066] The input unit can estimate the customer's emotions and adjust the timing of inputting body type data based on the estimated customer emotions. For example, if the customer is relaxed, the input unit prompts the customer to input all body type data at once. For example, if the customer is relaxed, the input unit prompts the customer to input all body type data at once. Furthermore, if the customer is feeling stressed, the input unit can suggest that the customer input body type data in multiple batches. For example, if the customer is feeling stressed, the input unit can suggest that the customer input body type data in multiple batches. Furthermore, if the customer is in a hurry, the input unit can prioritize inputting only the most important body type data. For example, if the customer is in a hurry, the input unit can prioritize inputting only the most important body type data. This allows for more appropriate data input by adjusting the timing of inputting body type data according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input customer emotion data to AI, which may then adjust the timing of inputting body type data.

[0067] The input unit can analyze the customer's past body type data and select the optimal input method. The input unit, for example, provides an auto-completion function to reduce the effort of input based on body type data previously input by the customer. For example, the input unit provides an auto-completion function to reduce the effort of input based on body type data previously input by the customer. The input unit can also automatically input data with little fluctuation from the customer's past body type data. For example, the input unit can automatically input data with little fluctuation from the customer's past body type data. The input unit can also analyze the customer's past body type data and optimize the input order. For example, the input unit can analyze the customer's past body type data and optimize the input order. In this way, the effort of input can be reduced by analyzing the past body type data. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the customer's past body type data into AI, which can select the optimal input method.

[0068] The input unit can filter the body type data based on the customer's current health condition and lifestyle habits when inputting the body type data. The input unit, for example, inputs only necessary body type data based on the customer's current health condition. For example, the input unit inputs only necessary body type data based on the customer's current health condition. The input unit can also determine the priority of the body type data to be input based on the customer's lifestyle habits. For example, the input unit can also determine the priority of the body type data to be input based on the customer's lifestyle habits. The input unit can also adjust the range of body type data to be input based on the customer's health condition and lifestyle habits. For example, the input unit can adjust the range of body type data to be input based on the customer's health condition and lifestyle habits. This enables efficient data input by inputting only necessary data based on the health condition and lifestyle habits. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input data on the customer's health condition and lifestyle habits into AI, which can then perform filtering.

[0069] The input unit can estimate the customer's emotions and determine the priority of body type data to be input based on the estimated customer emotions. For example, when the customer is relaxed, the input unit prioritizes input of detailed body type data. For example, when the customer is relaxed, the input unit prioritizes input of detailed body type data. Furthermore, when the customer is stressed, the input unit can prioritize input of only basic body type data. For example, when the customer is stressed, the input unit can prioritize input of only basic body type data. Furthermore, when the customer is in a hurry, the input unit can prioritize input of only the most important body type data. For example, when the customer is in a hurry, the input unit can prioritize input of only the most important body type data. This enables efficient data input by determining the priority of data to be input according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input customer emotion data into AI, which may then determine the priority of body type data.

[0070] When inputting body type data, the input unit can prioritize inputting highly relevant data by taking into account the customer's geographical location information. For example, if the customer lives in a cold region, the input unit prioritizes inputting body type data for winter clothes. For example, if the customer lives in a cold region, the input unit prioritizes inputting body type data for winter clothes. Furthermore, if the customer lives in a warm region, the input unit can also prioritize inputting body type data for summer clothes. For example, if the customer lives in a warm region, the input unit can also prioritize inputting body type data for summer clothes. Furthermore, if the customer lives in an urban area, the input unit can also prioritize inputting body type data for business clothes. For example, if the customer lives in an urban area, the input unit can also prioritize inputting body type data for business clothes. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the customer's geographical location information to AI, which can then prioritize inputting highly relevant data.

[0071] When inputting body type data, the input unit can analyze the customer's social media activities and input related data. The input unit, for example, analyzes photos shared by the customer on social media to complement the body type data. For example, the input unit analyzes photos shared by the customer on social media to complement the body type data. The input unit can also assist in the input of body type data from the content of the customer's social media posts. For example, the input unit can assist in the input of body type data from the content of the customer's social media posts. The input unit can also optimize the input of body type data from the customer's social media activities. In this way, related data can be complemented by analyzing social media activities. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data of the customer's social media activities to AI, and the AI ​​can input related data.

[0072] The suggestion unit can estimate a customer's emotions and adjust the design presentation method based on the estimated customer emotions. For example, if a customer is relaxed, the suggestion unit can suggest a design with soft colors. For example, if a customer is relaxed, the suggestion unit can suggest a design with soft colors. The suggestion unit can also suggest a simple, calming design if a customer is stressed. For example, if a customer is stressed, the suggestion unit can suggest a simple, calming design. For example, if a customer is excited, the suggestion unit can suggest a design with vivid colors. For example, if a customer is excited, the suggestion unit can suggest a design with vivid colors. This enables more appropriate design proposals by adjusting the design presentation method according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using an AI, or may be performed without an AI. For example, the suggestion unit can input customer emotion data into an AI, which can then adjust the design presentation method.

[0073] When proposing a design, the suggestion unit can suggest an optimal design by referring to the customer's past purchase history. For example, the suggestion unit can suggest a similar design based on a design purchased by the customer in the past. For example, the suggestion unit can suggest a similar design based on a design purchased by the customer in the past. The suggestion unit can also analyze preference trends from the customer's past purchase history to suggest a design. For example, the suggestion unit can analyze preference trends from the customer's past purchase history to suggest a design. The suggestion unit can also propose a new design by combining it with a design purchased by the customer in the past. For example, the suggestion unit can propose a new design by combining it with a design purchased by the customer in the past. In this way, by referring to the past purchase history, a design that suits the customer's preferences can be suggested. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the customer's past purchase history into AI, which can then suggest an optimal design.

[0074] When proposing a design, the proposal unit can customize the proposal content based on the customer's current fashion trends. For example, the proposal unit proposes a design that matches the customer's current fashion trends. The proposal unit can also propose a design that incorporates the latest fashion trends based on the customer's preferences. For example, the proposal unit can propose a design that incorporates the latest fashion trends based on the customer's preferences. The proposal unit can also analyze the customer's current fashion trends and propose an optimal design. For example, the proposal unit can analyze the customer's current fashion trends and propose an optimal design. In this way, by customizing the proposal content based on the current fashion trends, a design that incorporates the latest trends can be proposed. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the customer's current fashion trends into AI, which can then propose an optimal design.

[0075] The suggestion unit can estimate a customer's emotions and determine design priorities based on the estimated customer emotions. For example, if a customer is relaxed, the suggestion unit prioritizes proposing detailed designs. For example, if a customer is relaxed, the suggestion unit prioritizes proposing detailed designs. The suggestion unit can also prioritize proposing simple designs if a customer is stressed. For example, if a customer is stressed, the suggestion unit can also prioritize proposing simple designs. The suggestion unit can also prioritize proposing the most important designs if a customer is in a hurry. For example, if a customer is in a hurry, the suggestion unit can also prioritize proposing the most important designs. This enables efficient design proposals by determining design priorities according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the proposal department can input customer emotional data into AI, which can then determine design priorities.

[0076] The suggestion unit can propose an optimal design by taking into consideration the customer's geographical location information when proposing a design. For example, if the customer lives in a cold region, the suggestion unit can preferentially propose winter clothing designs. For example, if the customer lives in a cold region, the suggestion unit can preferentially propose winter clothing designs. The suggestion unit can also preferentially propose summer clothing designs if the customer lives in a warm region. For example, if the customer lives in a warm region, the suggestion unit can also preferentially propose summer clothing designs. The suggestion unit can also preferentially propose business clothing designs if the customer lives in an urban area. For example, the suggestion unit can preferentially propose business clothing designs if the customer lives in an urban area. In this way, by taking the geographical location information into consideration, highly relevant designs can be proposed. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the customer's geographical location information into AI, which can then suggest an optimal design.

[0077] When proposing a design, the suggestion unit can analyze a customer's social media activity and suggest a relevant design. For example, the suggestion unit can analyze photos shared by a customer on social media and suggest a design. For example, the suggestion unit can analyze photos shared by a customer on social media and suggest a design. The suggestion unit can also analyze a customer's design preferences from their social media posts and suggest a design. For example, the suggestion unit can analyze a customer's design preferences from their social media posts and suggest a design. The suggestion unit can also suggest an optimal design from the customer's social media activity. For example, the suggestion unit can suggest an optimal design from the customer's social media activity. In this way, related designs can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on a customer's social media activity into AI, which can suggest related designs.

[0078] The generation unit can estimate the customer's emotions and adjust the pattern generation method based on the estimated customer's emotions. For example, if the customer is relaxed, the generation unit generates a relaxed pattern. For example, if the customer is relaxed, the generation unit generates a relaxed pattern. The generation unit can also generate a simple and calm pattern if the customer is stressed. For example, if the customer is stressed, the generation unit can generate a simple and calm pattern. For example, if the customer is excited, the generation unit can generate a brightly colored pattern. For example, if the customer is excited, the generation unit can generate a brightly colored pattern. This allows the pattern generation method to be adjusted according to the customer's emotions, resulting in the generation of a more appropriate pattern. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using an AI, or may be performed without using an AI. For example, the generation unit can input customer emotion data into an AI, which then adjusts the pattern generation method.

[0079] When generating a pattern, the generation unit can generate an optimal pattern by referring to the customer's past design history. For example, the generation unit generates a similar pattern based on a pattern used by the customer in the past. The generation unit can also generate a pattern by analyzing preferences from the customer's past design history. For example, the generation unit can generate a pattern by analyzing preferences from the customer's past design history. The generation unit can also generate a new pattern by combining a pattern used by the customer in the past. For example, the generation unit can generate a new pattern by combining a pattern used by the customer in the past. By referring to the past design history, a pattern that matches the customer's preferences can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the customer's past design history into AI, which then generates an optimal pattern.

[0080] The generation unit can customize the pattern based on the customer's current body shape data when generating the pattern. The generation unit, for example, generates an optimal pattern based on the customer's current body shape data. For example, the generation unit generates an optimal pattern based on the customer's current body shape data. The generation unit can also generate a well-fitting pattern based on the customer's body shape data. For example, the generation unit can generate a well-fitting pattern based on the customer's body shape data. The generation unit can also analyze the customer's body shape data and adjust the size of the pattern. For example, the generation unit can analyze the customer's body shape data and adjust the size of the pattern. In this way, a well-fitting pattern can be generated by customizing the pattern based on the current body shape data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's current body shape data into AI, which can customize the pattern.

[0081] The generation unit can estimate the customer's emotions and determine the priority of pattern generation based on the estimated customer emotions. For example, if the customer is relaxed, the generation unit prioritizes generating detailed patterns. For example, if the customer is relaxed, the generation unit prioritizes generating detailed patterns. The generation unit can also prioritize generating simple patterns if the customer is stressed. For example, if the customer is stressed, the generation unit can also prioritize generating simple patterns. The generation unit can also prioritize generating the most important patterns if the customer is in a hurry. For example, if the customer is in a hurry, the generation unit can also prioritize generating the most important patterns. This enables efficient pattern generation by determining the priority of pattern generation according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input customer emotion data into the AI, which can then determine the priority of pattern generation.

[0082] The generation unit can generate an optimal pattern by taking into account the customer's geographical location information when generating a pattern. For example, if the customer lives in a cold region, the generation unit preferentially generates a pattern for winter clothing. For example, if the customer lives in a cold region, the generation unit preferentially generates a pattern for winter clothing. The generation unit can also preferentially generate a pattern for summer clothing if the customer lives in a warm region. For example, if the customer lives in a warm region, the generation unit can also preferentially generate a pattern for summer clothing. The generation unit can also preferentially generate a pattern for business clothing if the customer lives in an urban area. For example, if the customer lives in an urban area, the generation unit can also preferentially generate a pattern for business clothing. In this way, by taking the geographical location information into consideration, a highly relevant pattern can be generated. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's geographical location information into AI, which can then generate an optimal pattern.

[0083] When generating a pattern, the generation unit can analyze the customer's social media activity and generate a related pattern. The generation unit, for example, analyzes photos shared by the customer on social media to generate a pattern. For example, the generation unit analyzes photos shared by the customer on social media to generate a pattern. The generation unit can also generate a pattern by analyzing the customer's social media post content. For example, the generation unit can generate a pattern by analyzing the customer's social media post content. The generation unit can also generate an optimal pattern from the customer's social media activity. For example, the generation unit can generate an optimal pattern from the customer's social media activity. In this way, related patterns can be generated by analyzing social media activity. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the customer's social media activity into AI, which then generates related patterns.

[0084] The production department can estimate the customer's emotions and adjust the operation timing of the production line based on the estimated customer's emotions. For example, if the customer is relaxed, the production department operates according to the normal production schedule. For example, if the customer is relaxed, the production department operates according to the normal production schedule. Furthermore, if the customer is in a hurry, the production department can prioritize the operation of the production line. For example, if the customer is in a hurry, the production department can prioritize the operation of the production line. Furthermore, if the customer is feeling stressed, the production department can quickly operate the production line. For example, if the customer is feeling stressed, the production department can quickly operate the production line. This enables efficient production by adjusting the operation timing of the production line according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or without AI. For example, the production department can input customer emotion data into AI, which can then adjust the timing of production line operations.

[0085] During production, the production department can select the optimal production method by referring to the customer's past order history. For example, the production department selects the same production method as for products ordered by the customer in the past. For example, the production department selects the same production method as for products ordered by the customer in the past. The production department can also determine an optimal production schedule from the customer's past order history. For example, the production department can determine an optimal production schedule from the customer's past order history. The production department can also analyze the customer's past order history and select the most efficient production method. For example, the production department can analyze the customer's past order history and select the most efficient production method. In this way, an efficient production method can be selected by referring to the past order history. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the customer's past order history into AI, which selects the optimal production method.

[0086] During production, the production department can customize the production volume based on the customer's current demand. For example, the production department adjusts the production volume based on the customer's current demand. The production department can also optimize the production volume based on the customer's demand forecast. For example, the production department can also optimize the production volume based on the customer's demand forecast. The production department can also analyze the customer's current demand and determine the required production volume. For example, the production department can analyze the customer's current demand and determine the required production volume. This enables efficient production by customizing the production volume based on the current demand. Some or all of the above-mentioned processing in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the customer's current demand data into AI, which can customize the production volume.

[0087] The production department can estimate the customer's emotions and determine the priority of the production line based on the estimated customer's emotions. For example, if the customer is relaxed, the production department operates according to the normal production schedule. For example, if the customer is relaxed, the production department operates according to the normal production schedule. Furthermore, if the customer is in a hurry, the production department can prioritize the operation of the production line. For example, if the customer is in a hurry, the production department can prioritize the operation of the production line. Furthermore, if the customer is feeling stressed, the production department can quickly operate the production line. For example, if the customer is feeling stressed, the production department can quickly operate the production line. This enables efficient production by determining the priority of the production line according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the production department can be performed using, for example, AI, or without AI. For example, the production department can input customer sentiment data into AI, which can then determine production line priorities.

[0088] During production, the production department can select the optimal production method by taking into account the customer's geographical location information. For example, if the customer lives in a cold region, the production department prioritizes the production of winter clothing. For example, if the customer lives in a cold region, the production department prioritizes the production of winter clothing. The production department can also prioritize the production of summer clothing if the customer lives in a warm region. For example, if the customer lives in a warm region, the production department can also prioritize the production of summer clothing. The production department can also prioritize the production of business clothing if the customer lives in an urban area. In this way, by taking the geographical location information into account, a highly relevant production method can be selected. Some or all of the above-mentioned processes in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input the customer's geographical location information into AI, which can select the optimal production method.

[0089] The production department can analyze customers' social media activities during production and create related production plans. For example, the production department creates production plans based on information shared by customers on social media. For example, the production department creates production plans based on information shared by customers on social media. The production department can also predict demand and create production plans based on the content of customers' social media posts. For example, the production department can predict demand and create production plans based on the content of customers' social media posts. The production department can also analyze customers' social media activities and create optimal production plans. For example, the production department can analyze customers' social media activities and create optimal production plans. In this way, related production plans can be created by analyzing social media activities. Some or all of the above-mentioned processes in the production department may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input data on customers' social media activities into AI, which can then create related production plans. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, suggestion unit, generation unit, and production unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input customer body type data using the reception device 38 of the smart device 14. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to suggest designs and styling based on the customer's body type data and clothing preferences and uses. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific patterns based on the proposed designs. The production unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically sends the generated patterns to a production line. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, suggestion unit, generation unit, and production unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input customer body type data using the microphone 238 of the smart glasses 214. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to suggest designs and styling based on the customer's body type data and clothing preferences and uses. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific patterns based on the proposed designs. The production unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically sends the generated patterns to a production line. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, suggestion unit, generation unit, and production unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit can input customer body type data using the microphone 238 of the headset-type terminal 314. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to suggest designs and styling based on the customer's body type data and clothing preferences and uses. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific patterns based on the proposed designs. The production unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically sends the generated patterns to a production line. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, suggestion unit, generation unit, and production unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input customer body type data using the microphone 238 of the robot 414. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to suggest designs and styling based on the customer's body type data and clothing preferences and uses. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates specific patterns based on the proposed designs. The production unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically sends the generated patterns to a production line.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The custom-made clothing service system can further include a feedback unit. The feedback unit can collect customer evaluations and impressions of the clothing items they receive and reflect them in the next design proposal. For example, the feedback unit can provide an interface for customers to input their evaluations of the clothing's fit and design. The feedback unit can also analyze customer evaluation data and generate data to further improve the customer's preferences and fit in the next proposal. This makes it possible to utilize customer feedback to provide a more satisfying custom-made clothing service.

[0092] The input unit can provide real-time feedback when a customer enters their body data. For example, the input unit can instantly display a fit prediction based on the body data entered by the customer. The input unit can also provide suggestions for improvement or supplemental information for the data entered by the customer. For example, if the waist size entered by the customer is inconsistent with other data, the input unit can point out the inconsistency and prompt the customer to re-enter. This allows for more accurate body data entry, resulting in optimal design proposals.

[0093] The suggestion unit can present multiple design options based on the customer's body type data and clothing preferences and uses. For example, the suggestion unit can suggest multiple casual designs in different styles and colors to a customer who prefers casual clothing. The suggestion unit can also suggest suit designs with different cuts and materials to a customer who needs a business suit. Furthermore, the suggestion unit can suggest customizable elements (e.g., pocket position or button type) based on the design options selected by the customer. This allows the customer to select the optimal design that suits their preferences.

[0094] The generation unit can select materials that take environmental impact into consideration when generating patterns based on proposed designs. For example, the generation unit generates patterns using renewable materials and eco-friendly dyes. The generation unit can also perform efficient pattern placement to minimize waste. For example, the generation unit can generate patterns with optimal placement to reduce fabric waste. This makes it possible to provide an environmentally friendly custom-made clothing service.

[0095] When the generated patterns are sent to the production line, the production department can monitor the production status in real time and make adjustments as necessary. For example, the production department can monitor the machine operation status and production progress and respond immediately if an abnormality occurs. The production department can also dynamically adjust the work schedule to maximize the efficiency of the production line. For example, the production department can update the production schedule in real time to respond to sudden order changes or additional orders. This enables an efficient and flexible production system.

[0096] The input unit can estimate the customer's emotions and customize the body data input interface based on the estimated customer emotions. For example, the input unit can provide a simple and intuitive interface when the customer is relaxed. Alternatively, the input unit can provide a guided step-by-step interface when the customer is stressed. Furthermore, the input unit can provide a quick input mode when the customer is in a hurry to prioritize input of only the most important data. This makes it possible to provide an optimal data input experience according to the customer's emotions.

[0097] The suggestion unit can estimate the customer's emotions and adjust the design suggestion method based on the estimated customer's emotions. For example, if the customer is relaxed, the suggestion unit can present multiple options along with detailed design explanations. If the customer is stressed, the suggestion unit can also make simple and intuitive design suggestions. Furthermore, if the customer is excited, the suggestion unit can suggest vibrant colors and unique designs. This makes it possible to make optimal design suggestions according to the customer's emotions.

[0098] The generation unit can estimate the customer's emotions and adjust the pattern generation process based on the estimated customer's emotions. For example, if the customer is relaxed, the generation unit can generate a pattern with a loose fit. If the customer is stressed, the generation unit can also generate a pattern with a simple and calm design. Furthermore, if the customer is excited, the generation unit can generate a pattern with bright colors and a bold design. In this way, it is possible to generate an optimal pattern according to the customer's emotions.

[0099] The production department can estimate the customer's emotions and adjust the production schedule based on the estimated customer's emotions. For example, if the customer is relaxed, the production department will operate according to the normal production schedule. Also, if the customer is in a hurry, the production department can prioritize the operation of the production line. Furthermore, if the customer is feeling stressed, the production department can quickly operate the production line. This makes it possible to realize an optimal production schedule according to the customer's emotions.

[0100] The suggestion unit can estimate the customer's emotions and determine the priority of designs based on the estimated customer's emotions. For example, if the customer is relaxed, the suggestion unit can prioritize suggesting detailed designs. Also, if the customer is stressed, the suggestion unit can prioritize suggesting simple designs. Furthermore, if the customer is in a hurry, the suggestion unit can prioritize suggesting the most important designs. This makes it possible to suggest optimal designs according to the customer's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The input unit inputs the customer's body data. This data includes, for example, height, weight, chest circumference, waist circumference, and hip circumference. The input unit provides an interface for customers to input their body data, and can also use AI to analyze the customer's body data and generate data for optimal design proposals. Step 2: The proposal unit proposes designs based on the body type data entered by the input unit. The proposal unit uses AI to suggest designs and styling based on the customer's body type data, clothing preferences, and use. For example, if a customer prefers casual clothing, the AI ​​will suggest the most suitable casual design for that customer, and if a business suit is needed, the AI ​​will suggest the most suitable suit design. Step 3: The generator generates a pattern based on the design proposed by the suggester. The generator generates a specific pattern using AI. For example, the generator can generate a specific pattern based on the casual design proposed by AI. Step 4: The production department sends the patterns generated by the generation department to the production line. The production department can automatically send the patterns generated using AI to the production line, thereby improving production efficiency.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[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 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.

[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. 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting customer body type data; a proposal unit that proposes a design based on the body type data input by the input unit; a generation unit that generates a pattern based on the design proposed by the proposal unit; a production unit that sends the pattern generated by the generation unit to a production line. A system characterized by:

2. The input unit Enter detailed body data of the customer including height, weight, chest, waist and hip circumference 2. The system of claim 1.

3. The proposal unit Suggest designs and styling based on input body data, clothing preferences, and usage 2. The system of claim 1.

4. The generation unit Generate specific patterns based on proposed designs 2. The system of claim 1.

5. The production department The generated patterns are automatically sent to the production line.

2. The system of claim 1.

6. The production department Since only the amount ordered by the customer is produced, there is no excess inventory and waste can be reduced.

2. The system of claim 1.

7. The input unit Estimate customer emotions and adjust the timing of body shape data input based on the estimated customer emotions 2. The system of claim 1.

8. The input unit Analyze the customer's past body shape data and select the appropriate input method 2. The system of claim 1.

9. The input unit When entering body data, filtering is performed based on the customer's current health status and lifestyle habits.

2. The system of claim 1.

10. The input unit Estimate customer sentiment and prioritize body shape data to be entered based on the estimated sentiment 2. The system of claim 1.

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