System

Abstract

An object of a system according to an embodiment is to propose an optimum size when purchasing shoes online and to facilitate trial fitting.SOLUTION: A system according to an embodiment includes an imaging unit for feet, a 3D shoe model generation unit, a shoe size suggestion unit, and a 3D printing unit. The foot photographing unit photographs a foot with a smartphone. The 3D leg model generation unit generates a 3D leg model based on the image of the leg captured by the imaging unit. The shoe size suggestion unit suggests an optimal shoe size based on the 3D shoe model generated by the 3D shoe model generation unit. The 3D printing part performs 3D printing of the shoe of the size proposed by the shoe size proposing part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to select the right size when purchasing shoes online, and the inability to try them on can lead to returns and exchanges.

[0005] The system according to the embodiment aims to suggest the optimal size when purchasing shoes online and to make it easy to try them on. [Means for solving the problem]

[0006] The system according to the embodiment includes a foot photographing unit, a 3D foot shape model generating unit, a shoe size suggestion unit, and a 3D printing unit. The foot photographing unit photographs the foot with a smartphone. The 3D foot shape model generating unit generates a 3D foot shape model based on the data photographed by the foot photographing unit. The shoe size suggestion unit suggests an optimal shoe size based on the 3D foot shape model generated by the 3D foot shape model generating unit. The 3D printing unit 3D prints shoes in the size suggested by the shoe size suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the optimal size when purchasing shoes online and make it easy to try them on. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) The online shoe purchasing support system according to an embodiment of the present invention is a system in which a user photographs a foot with a smartphone, a generative AI generates a 3D foot model, suggests the optimal shoe size and style, and creates a try-on model using a 3D printer. This allows the user to try on shoes of multiple sizes at home and select the most suitable shoe.

[0029] An online shoe purchasing support system according to an embodiment includes a foot photographing unit, a 3D foot shape model generating unit, a shoe size suggesting unit, and a 3D printing unit. The foot photographing unit photographs the foot using a smartphone. For example, a user photographs the front, side, and back of the foot using the smartphone. The 3D foot shape model generating unit generates a 3D foot shape model using a generation AI based on the photographed data. For example, the generation AI analyzes the shape and dimensions of the foot using a text generation AI (e.g., LLM) to create an accurate 3D model. The shoe size suggesting unit suggests the optimal shoe size and style based on the generated 3D foot shape model. For example, the generation AI selects the optimal shoe based on the foot shape and dimensions, the user's preferences, past purchase history, and other factors. The 3D printing unit creates shoes of the suggested size using a 3D printer. For example, the 3D printer prints a try-on model based on the shoe design data suggested by the generation AI. This allows the online shoe purchasing support system to allow users to try on shoes of multiple sizes at home and select the best fit.

[0030] The foot capture unit adds a depth sensor to the smartphone camera, allowing it to capture the three-dimensional shape of the foot more accurately.The foot capture unit adds a depth sensor to the smartphone camera, for example, allowing it to capture the three-dimensional shape of the foot more accurately.For example, by using a depth sensor, the height and unevenness of the foot can be measured in detail, allowing it to generate a more precise 3D model.This allows it to capture the three-dimensional shape of the foot more accurately, allowing it to generate a more precise 3D model.

[0031] The foot imaging unit uses a dedicated mat to measure the pressure distribution of the foot when taking the image, and can record the foot shape and pressure distribution simultaneously. For example, a sensor built into the dedicated mat measures the foot pressure and sends the data to a smartphone. This allows for the simultaneous recording of the foot shape and pressure distribution, enabling the generation of a more accurate 3D model.

[0032] The 3D foot shape model generation unit can analyze the skeletal structure of the foot and generate a 3D foot shape model based on the skeleton. For example, the 3D foot shape model generation unit uses a generation AI to analyze the skeletal structure of the foot and generate a 3D foot shape model based on the skeleton. For example, an accurate 3D model is created based on skeletal data of the foot. This allows a more accurate 3D foot shape model to be generated by analyzing the skeletal structure of the foot.

[0033] The 3D foot shape model generation unit can analyze the texture and thickness of the skin of the foot and generate a realistic 3D foot shape model. For example, the 3D foot shape model generation unit uses a generative AI to analyze the texture and thickness of the skin of the foot and generate a more realistic 3D foot shape model. For example, a 3D model that reproduces the texture and thickness is created based on the skin data of the foot. This allows for the generation of a more realistic 3D foot shape model by analyzing the texture and thickness of the skin of the foot.

[0034] The 3D foot shape model generation unit can analyze the movement of the foot muscles and generate a dynamic 3D foot shape model. For example, the 3D foot shape model generation unit uses a generation AI to analyze the movement of the foot muscles and generate a dynamic 3D foot shape model. For example, a dynamic 3D model is created based on foot muscle data. This makes it possible to generate a dynamic 3D foot shape model by analyzing the movement of the foot muscles.

[0035] The 3D foot shape model generation unit can analyze the health condition of the foot and generate a 3D foot shape model according to the health condition. For example, the 3D foot shape model generation unit uses a generation AI to analyze the health condition of the foot (e.g., hallux valgus or flat feet) and generate a 3D foot shape model according to the health condition. For example, an accurate 3D model is created based on foot health data. This makes it possible to generate a 3D foot shape model according to the health condition by analyzing the health condition of the foot.

[0036] The shoe size suggestion unit can analyze the user's walking pattern and suggest the shoe size and style that are best suited to that walking pattern. For example, the shoe size suggestion unit uses a generation AI to analyze the user's walking pattern and suggest the shoe size and style that are best suited to that walking pattern. For example, the unit analyzes the user's foot movement and pressure distribution based on the user's walking data and selects the best shoes. In this way, the system can suggest the best shoe size and style by analyzing the user's walking pattern.

[0037] The shoe size suggestion unit can predict the growth of the user's feet and suggest shoe sizes that will fit in the future. For example, the shoe size suggestion unit uses a generation AI to predict the growth of the user's feet and suggest shoe sizes that will fit in the future. For example, the unit predicts the user's future foot size based on the user's age and growth data and selects the most suitable shoes. In this way, by predicting the growth of the user's feet, it is possible to suggest shoe sizes that will fit in the future.

[0038] The shoe size suggestion unit can suggest shoe sizes and styles according to different climatic conditions. For example, the generation AI in the shoe size suggestion unit suggests shoe sizes and styles according to different climatic conditions. For example, it suggests shoes with high thermal insulation in cold regions and shoes with good breathability in hot regions. This improves user comfort by suggesting shoe sizes and styles according to different climatic conditions.

[0039] The shoe size suggestion unit can suggest shoe sizes and styles according to the user's activity level. For example, the generation AI of the shoe size suggestion unit suggests shoe sizes and styles according to the user's activity level (e.g., sports, daily life). For example, it suggests running shoes or business shoes. This improves the user's comfort by suggesting shoe sizes and styles according to the user's activity level.

[0040] The 3D printing department can create a try-on model that closely resembles the feel of a foot by using different materials. For example, the 3D printer can print a combination of flexible materials and materials with different hardness. By using different materials, it is possible to create a try-on model that closely resembles the feel of a foot.

[0041] The 3D printing department can create flexible try-on models that can simulate foot movement. The 3D printing department can create flexible try-on models that can simulate foot movement, for example, using flexible materials. This allows for the creation of flexible try-on models that can simulate foot movement, providing a more realistic try-on experience.

[0042] The 3D printing unit can create try-on models with different colors and designs and provide visual feedback. The 3D printing unit, for example, uses a 3D printer to create try-on models with different colors and designs and provide visual feedback. For example, the unit can select colors and designs according to the user's preferences. This allows the unit to provide visual feedback by creating try-on models with different colors and designs.

[0043] The 3D printing unit can create special insoles according to the health condition of the feet. For example, the 3D printer can print insoles for bunions or flat feet. This allows the creation of special insoles according to the health condition of the feet, thereby supporting the health of the user.

[0044] The at-home fitting section uses sensors that monitor foot movement when trying on shoes, allowing for real-time evaluation of fit. The at-home fitting section uses sensors that monitor foot movement when trying on shoes, allowing for real-time evaluation of fit. For example, foot movement is measured with a sensor to evaluate fit. Thus, by using sensors that monitor foot movement, fit can be evaluated in real time.

[0045] The at-home fitting section uses a dedicated mat for measuring foot pressure distribution when trying on shoes, allowing for a detailed evaluation of fit. The at-home fitting section uses, for example, a dedicated mat for measuring foot pressure distribution when trying on shoes, allowing for a detailed evaluation of fit. For example, a sensor built into the dedicated mat measures foot pressure and analyzes the data. In this way, by using the dedicated mat that measures foot pressure distribution, fit can be evaluated in detail.

[0046] The at-home fitting section uses virtual reality (VR) technology to simulate the fit in different environments. The at-home fitting section uses virtual reality (VR) technology to simulate the fit in different environments, for example, when trying on clothes. For example, the user can simulate walking around a virtual city or office. This allows the user to simulate the fit in different environments by using virtual reality (VR) technology.

[0047] The at-home fitting unit can use sensors that monitor the health condition of the feet and provide feedback according to the health condition. The at-home fitting unit can use sensors that monitor the health condition of the feet when trying on shoes and provide feedback according to the health condition. For example, the sensors can measure the pressure distribution and movement of the feet to evaluate the health condition. In this way, by using sensors that monitor the health condition of the feet, feedback according to the health condition can be provided.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The online shoe purchasing support system can further include a gait analysis unit that analyzes the user's walking pattern. For example, the gait analysis unit collects walking data using the smartphone's acceleration sensor and gyro sensor when the user walks while holding the smartphone. This allows the user's walking pattern to be analyzed and the foot movement and pressure distribution during walking to be understood in detail. For example, the gait analysis unit can analyze the foot movement and pressure distribution based on the user's walking pattern and suggest the optimal shoe size and style. This makes it possible to suggest shoes based on the user's walking pattern, thereby supporting the user in choosing more comfortable shoes.

[0050] The online shoe purchasing support system may further include a health monitoring unit that monitors the health condition of the user's feet. For example, when a user takes a photo of their foot using a smartphone, the health monitoring unit analyzes changes in the color and shape of the foot and evaluates the health condition. This allows the health condition of the foot to be monitored in real time and issues an alert if an abnormality is detected. For example, the health monitoring unit may analyze changes in the color and shape of the foot to detect symptoms such as hallux valgus and flat feet early. This allows the system to constantly monitor the health condition of the user's feet and support the user in selecting appropriate shoes.

[0051] The online shoe purchasing support system may further include a growth prediction unit that predicts the growth of the user's feet. The growth prediction unit predicts the user's future foot size based on, for example, the user's age and past foot data. This makes it possible to suggest shoe sizes that will fit children and young people who are still growing. For example, the growth prediction unit may analyze the user's age and growth data to predict the user's foot size several months or several years from now and suggest an appropriate shoe size. This makes it possible to select shoes that match the user's foot growth, and to provide comfortable shoes for a long period of time.

[0052] The online shoe purchasing support system can further include a climate adaptation suggestion unit that suggests shoe sizes and styles according to different climate conditions. The climate adaptation suggestion unit suggests appropriate shoe sizes and styles based on, for example, climate data for the user's place of residence or travel destination. This makes it possible to select shoes according to different climate conditions, such as cold or hot regions. For example, the climate adaptation suggestion unit can suggest shoes with high thermal insulation in cold regions and shoes with good ventilation in hot regions. This makes it possible to suggest shoes according to the climate conditions of the user's place of residence or travel destination, thereby supporting the user in selecting comfortable shoes.

[0053] The online shoe purchasing support system can further include an activity level suggestion unit that suggests shoe sizes and styles according to the user's activity level. The activity level suggestion unit suggests appropriate shoe sizes and styles based on, for example, data on the user's sports activities and daily life. This makes it possible to select shoes according to the user's activity level. For example, the activity level suggestion unit can suggest running shoes to a user who runs, and business shoes to a user who wears business attire. This makes it possible to suggest shoes according to the user's activity level, and supports the user in selecting comfortable shoes.

[0054] The online shoe purchasing support system may further include a material selection unit that uses different materials to create a try-on model that closely resembles the feel of a user's foot. For example, when a 3D printer uses different materials to create a try-on model, the material selection unit selects a material that closely resembles the feel of a user's foot. This allows for a more realistic try-on experience. For example, the material selection unit may print a combination of flexible materials and materials with different hardness to create a try-on model that closely resembles the feel of a user's foot. This allows for the use of different materials to create a try-on model that closely resembles the feel of a user's foot, improving user satisfaction.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The foot photography section takes a photo of the foot with a smartphone. The user uses the smartphone to take photos of the front, side, back, etc. of the foot. Step 2: The 3D foot print model generator uses the captured data to generate a 3D foot print model. The generator uses text generation AI (e.g., LLM) to analyze the shape and dimensions of the foot and create an accurate 3D model. Step 3: The shoe size suggestion unit suggests the optimal shoe size and style based on the generated 3D foot model. The generation AI considers the shape and dimensions of the foot, as well as the user's preferences and past purchase history, to select the most suitable shoes. Step 4: The 3D printing department uses a 3D printer to create the shoes in the proposed size. The 3D printer prints a model to try on based on the shoe design data proposed by the generative AI.

[0057] (Example 2) The online shoe purchasing support system according to an embodiment of the present invention is a system in which a user photographs a foot with a smartphone, a generative AI generates a 3D foot model, suggests the optimal shoe size and style, and creates a try-on model using a 3D printer. This allows the user to try on shoes of multiple sizes at home and select the most suitable shoe.

[0058] An online shoe purchasing support system according to an embodiment includes a foot photographing unit, a 3D foot shape model generating unit, a shoe size suggesting unit, and a 3D printing unit. The foot photographing unit photographs the foot using a smartphone. For example, a user photographs the front, side, and back of the foot using the smartphone. The 3D foot shape model generating unit generates a 3D foot shape model using a generation AI based on the photographed data. For example, the generation AI analyzes the shape and dimensions of the foot using a text generation AI (e.g., LLM) to create an accurate 3D model. The shoe size suggesting unit suggests the optimal shoe size and style based on the generated 3D foot shape model. For example, the generation AI selects the optimal shoe based on the foot shape and dimensions, the user's preferences, past purchase history, and other factors. The 3D printing unit creates shoes of the suggested size using a 3D printer. For example, the 3D printer prints a try-on model based on the shoe design data suggested by the generation AI. This allows the online shoe purchasing support system to allow users to try on shoes of multiple sizes at home and select the best fit.

[0059] The foot capture unit adds a depth sensor to the smartphone camera, allowing it to capture the three-dimensional shape of the foot more accurately.The foot capture unit adds a depth sensor to the smartphone camera, for example, allowing it to capture the three-dimensional shape of the foot more accurately.For example, by using a depth sensor, the height and unevenness of the foot can be measured in detail, allowing it to generate a more precise 3D model.This allows it to capture the three-dimensional shape of the foot more accurately, allowing it to generate a more precise 3D model.

[0060] The foot imaging unit uses a dedicated mat to measure the pressure distribution of the foot when taking the image, and can record the foot shape and pressure distribution simultaneously. For example, a sensor built into the dedicated mat measures the foot pressure and sends the data to a smartphone. This allows for the simultaneous recording of the foot shape and pressure distribution, enabling the generation of a more accurate 3D model.

[0061] The foot image capturing unit can use an emotion estimation function to analyze the user's emotion at the time of shooting and guide the user to take the photo in a relaxed state. The foot image capturing unit can, for example, use the emotion estimation function to analyze the user's emotion at the time of shooting and guide the user to take the photo in a relaxed state. For example, the foot image capturing unit can analyze the user's facial expressions and voice and provide advice on how to relax. This can guide the user to take the photo in a relaxed state, thereby obtaining more accurate data.

[0062] The 3D foot shape model generation unit can analyze the skeletal structure of the foot and generate a 3D foot shape model based on the skeleton. For example, the 3D foot shape model generation unit uses a generation AI to analyze the skeletal structure of the foot and generate a 3D foot shape model based on the skeleton. For example, an accurate 3D model is created based on skeletal data of the foot. This allows a more accurate 3D foot shape model to be generated by analyzing the skeletal structure of the foot.

[0063] The 3D foot shape model generation unit can analyze the texture and thickness of the skin of the foot and generate a realistic 3D foot shape model. For example, the 3D foot shape model generation unit uses a generative AI to analyze the texture and thickness of the skin of the foot and generate a more realistic 3D foot shape model. For example, a 3D model that reproduces the texture and thickness is created based on the skin data of the foot. This allows for the generation of a more realistic 3D foot shape model by analyzing the texture and thickness of the skin of the foot.

[0064] The 3D foot shape model generation unit can analyze the movement of the foot muscles and generate a dynamic 3D foot shape model. For example, the 3D foot shape model generation unit uses a generation AI to analyze the movement of the foot muscles and generate a dynamic 3D foot shape model. For example, a dynamic 3D model is created based on foot muscle data. This makes it possible to generate a dynamic 3D foot shape model by analyzing the movement of the foot muscles.

[0065] The 3D foot shape model generation unit can analyze the health condition of the foot and generate a 3D foot shape model according to the health condition. For example, the 3D foot shape model generation unit uses a generation AI to analyze the health condition of the foot (e.g., hallux valgus or flat feet) and generate a 3D foot shape model according to the health condition. For example, an accurate 3D model is created based on foot health data. This makes it possible to generate a 3D foot shape model according to the health condition by analyzing the health condition of the foot.

[0066] The 3D footprint model generation unit can generate a customizable 3D footprint model based on the user's emotions using the emotion estimation function. The 3D footprint model generation unit generates a customizable 3D footprint model based on the user's emotions using the emotion estimation function, for example. For example, a customized 3D model is created based on the user's emotion data. In this way, the customizable 3D footprint model based on the user's emotions is generated, thereby improving user satisfaction.

[0067] The shoe size suggestion unit can analyze the user's walking pattern and suggest the shoe size and style that are best suited to that walking pattern. For example, the shoe size suggestion unit uses a generation AI to analyze the user's walking pattern and suggest the shoe size and style that are best suited to that walking pattern. For example, the unit analyzes the user's foot movement and pressure distribution based on the user's walking data and selects the best shoes. In this way, the system can suggest the best shoe size and style by analyzing the user's walking pattern.

[0068] The shoe size suggestion unit can predict the growth of the user's feet and suggest shoe sizes that will fit in the future. For example, the shoe size suggestion unit uses a generation AI to predict the growth of the user's feet and suggest shoe sizes that will fit in the future. For example, the unit predicts the user's future foot size based on the user's age and growth data and selects the most suitable shoes. In this way, by predicting the growth of the user's feet, it is possible to suggest shoe sizes that will fit in the future.

[0069] The shoe size suggestion unit can use the emotion estimation function to suggest shoe styles based on the user's emotions and provide an emotionally satisfying selection. The shoe size suggestion unit, for example, can use the emotion estimation function to suggest shoe styles based on the user's emotions and provide an emotionally satisfying selection. For example, the shoe size suggestion unit suggests a preferred style based on the user's emotion data. In this way, by suggesting shoe styles based on the user's emotions, an emotionally satisfying selection can be provided.

[0070] The shoe size suggestion unit can suggest shoe sizes and styles according to different climatic conditions. For example, the generation AI in the shoe size suggestion unit suggests shoe sizes and styles according to different climatic conditions. For example, it suggests shoes with high thermal insulation in cold regions and shoes with good breathability in hot regions. This improves user comfort by suggesting shoe sizes and styles according to different climatic conditions.

[0071] The shoe size suggestion unit can suggest shoe sizes and styles according to the user's activity level. For example, the generation AI of the shoe size suggestion unit suggests shoe sizes and styles according to the user's activity level (e.g., sports, daily life). For example, it suggests running shoes or business shoes. This improves the user's comfort by suggesting shoe sizes and styles according to the user's activity level.

[0072] The shoe size suggestion unit can suggest customizable shoe designs based on the user's emotions using the emotion estimation function. The shoe size suggestion unit, for example, suggests customizable shoe designs based on the user's emotions using the emotion estimation function. For example, it suggests a preferred design based on the user's emotion data. In this way, customizable shoe designs based on the user's emotions are suggested, thereby improving user satisfaction.

[0073] The 3D printing department can create a try-on model that closely resembles the feel of a foot by using different materials. For example, the 3D printer can print a combination of flexible materials and materials with different hardness. By using different materials, it is possible to create a try-on model that closely resembles the feel of a foot.

[0074] The 3D printing department can create flexible try-on models that can simulate foot movement. The 3D printing department can create flexible try-on models that can simulate foot movement, for example, using flexible materials. This allows for the creation of flexible try-on models that can simulate foot movement, providing a more realistic try-on experience.

[0075] The 3D printing unit can use the emotion estimation function to customize the try-on model based on the user's emotions. The 3D printing unit, for example, uses the emotion estimation function to customize the try-on model based on the user's emotions. For example, the 3D printing unit selects the user's preferred design and material based on the user's emotion data. This allows the try-on model to be customized based on the user's emotions, thereby improving user satisfaction.

[0076] The 3D printing unit can create try-on models with different colors and designs and provide visual feedback. The 3D printing unit, for example, uses a 3D printer to create try-on models with different colors and designs and provide visual feedback. For example, the unit can select colors and designs according to the user's preferences. This allows the unit to provide visual feedback by creating try-on models with different colors and designs.

[0077] The 3D printing unit can create special insoles according to the health condition of the feet. For example, the 3D printer can print insoles for bunions or flat feet. This allows the creation of special insoles according to the health condition of the feet, thereby supporting the health of the user.

[0078] The 3D printing unit can use the emotion estimation function to propose a design for the try-on model based on the user's emotions. The 3D printing unit, for example, uses the emotion estimation function to propose a design for the try-on model based on the user's emotions. For example, it proposes a preferred design based on the user's emotion data. In this way, by proposing a design for the try-on model based on the user's emotions, user satisfaction is improved.

[0079] The at-home fitting section uses sensors that monitor foot movement when trying on shoes, allowing for real-time evaluation of fit. The at-home fitting section uses sensors that monitor foot movement when trying on shoes, allowing for real-time evaluation of fit. For example, foot movement is measured with a sensor to evaluate fit. Thus, by using sensors that monitor foot movement, fit can be evaluated in real time.

[0080] The at-home fitting section uses a dedicated mat for measuring foot pressure distribution when trying on shoes, allowing for a detailed evaluation of fit. The at-home fitting section uses, for example, a dedicated mat for measuring foot pressure distribution when trying on shoes, allowing for a detailed evaluation of fit. For example, a sensor built into the dedicated mat measures foot pressure and analyzes the data. In this way, by using the dedicated mat that measures foot pressure distribution, fit can be evaluated in detail.

[0081] The at-home fitting unit can use the emotion estimation function to analyze the user's emotions when trying on clothes and provide an optimal fit. The at-home fitting unit can, for example, use the emotion estimation function to analyze the user's emotions when trying on clothes and provide an optimal fit. For example, the emotion estimation function can analyze the user's facial expressions and voice to evaluate their emotions regarding the fit. In this way, the emotion estimation function can provide an optimal fit based on the user's emotions.

[0082] The at-home fitting section uses virtual reality (VR) technology to simulate the fit in different environments. The at-home fitting section uses virtual reality (VR) technology to simulate the fit in different environments, for example, when trying on clothes. For example, the user can simulate walking around a virtual city or office. This allows the user to simulate the fit in different environments by using virtual reality (VR) technology.

[0083] The at-home fitting unit can use sensors that monitor the health condition of the feet and provide feedback according to the health condition. The at-home fitting unit can use sensors that monitor the health condition of the feet when trying on shoes and provide feedback according to the health condition. For example, the sensors can measure the pressure distribution and movement of the feet to evaluate the health condition. In this way, by using sensors that monitor the health condition of the feet, feedback according to the health condition can be provided.

[0084] The at-home fitting unit can use the emotion estimation function to adjust the fit based on the user's emotion when trying on clothes. The at-home fitting unit can, for example, use the emotion estimation function to adjust the fit based on the user's emotion when trying on clothes. For example, the emotion estimation function can analyze the user's facial expressions and voice to evaluate their emotion regarding the fit. In this way, the emotion estimation function can be used to adjust the fit based on the user's emotion.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The online shoe purchasing support system can further include a gait analysis unit that analyzes the user's walking pattern. For example, the gait analysis unit collects walking data using the smartphone's acceleration sensor and gyro sensor when the user walks while holding the smartphone. This allows the user's walking pattern to be analyzed and the foot movement and pressure distribution during walking to be understood in detail. For example, the gait analysis unit can analyze the foot movement and pressure distribution based on the user's walking pattern and suggest the optimal shoe size and style. This makes it possible to suggest shoes based on the user's walking pattern, thereby supporting the user in choosing more comfortable shoes.

[0087] The online shoe purchasing support system may further include a health monitoring unit that monitors the health condition of the user's feet. For example, when a user takes a photo of their foot using a smartphone, the health monitoring unit analyzes changes in the color and shape of the foot and evaluates the health condition. This allows the health condition of the foot to be monitored in real time and issues an alert if an abnormality is detected. For example, the health monitoring unit may analyze changes in the color and shape of the foot to detect symptoms such as hallux valgus and flat feet early. This allows the system to constantly monitor the health condition of the user's feet and support the user in selecting appropriate shoes.

[0088] The online shoe purchasing support system may further include a design customization unit that estimates the user's emotions and customizes the shoe design based on the estimated emotions. For example, when the user takes a photo of their feet using a smartphone, the design customization unit analyzes their facial expressions and voice to estimate the user's emotions. This makes it possible to suggest shoes with a design based on the user's emotions. For example, the design customization unit may suggest shoes with a casual design when the user is relaxed, and shoes with a formal design when the user is nervous. This makes it possible to improve user satisfaction by suggesting shoes with a design based on the user's emotions.

[0089] The online shoe purchasing support system may further include a growth prediction unit that predicts the growth of the user's feet. The growth prediction unit predicts the user's future foot size based on, for example, the user's age and past foot data. This makes it possible to suggest shoe sizes that will fit children and young people who are still growing. For example, the growth prediction unit may analyze the user's age and growth data to predict the user's foot size several months or several years from now and suggest an appropriate shoe size. This makes it possible to select shoes that match the user's foot growth, and to provide comfortable shoes for a long period of time.

[0090] The online shoe purchasing support system may further include a try-on model customization unit that estimates a user's emotions and customizes a try-on model based on the estimated emotions. The try-on model customization unit, for example, analyzes facial expressions and voice when a user takes a photo of their foot using a smartphone to estimate the user's emotions. This makes it possible to create a try-on model based on the user's emotions. For example, the try-on model customization unit may create a try-on model using a soft material when the user is relaxed, and create a try-on model using a harder material when the user is nervous. This allows the user's satisfaction to be improved by creating a try-on model based on the user's emotions.

[0091] The online shoe purchasing support system can further include a climate adaptation suggestion unit that suggests shoe sizes and styles according to different climate conditions. The climate adaptation suggestion unit suggests appropriate shoe sizes and styles based on, for example, climate data for the user's place of residence or travel destination. This makes it possible to select shoes according to different climate conditions, such as cold or hot regions. For example, the climate adaptation suggestion unit can suggest shoes with high thermal insulation in cold regions and shoes with good ventilation in hot regions. This makes it possible to suggest shoes according to the climate conditions of the user's place of residence or travel destination, thereby supporting the user in selecting comfortable shoes.

[0092] The online shoe purchasing support system may further include a style suggestion unit that estimates a user's emotions and suggests a shoe style based on the estimated emotions. For example, when a user takes a photo of their feet using a smartphone, the style suggestion unit analyzes their facial expressions and voice to estimate the user's emotions. This makes it possible to suggest a shoe style based on the user's emotions. For example, the style suggestion unit may suggest a casual style of shoes when the user is relaxed, and a formal style of shoes when the user is nervous. This makes it possible to improve user satisfaction by suggesting a shoe style based on the user's emotions.

[0093] The online shoe purchasing support system can further include an activity level suggestion unit that suggests shoe sizes and styles according to the user's activity level. The activity level suggestion unit suggests appropriate shoe sizes and styles based on, for example, data on the user's sports activities and daily life. This makes it possible to select shoes according to the user's activity level. For example, the activity level suggestion unit can suggest running shoes to a user who runs, and business shoes to a user who wears business attire. This makes it possible to suggest shoes according to the user's activity level, and supports the user in selecting comfortable shoes.

[0094] The online shoe purchasing support system can further include a fit adjustment unit that estimates the user's emotions and adjusts the fit based on the estimated emotions. For example, when the user takes a photo of their feet using a smartphone, the fit adjustment unit analyzes their facial expressions and voice to estimate the user's emotions. This makes it possible to adjust the fit based on the user's emotions. For example, the fit adjustment unit can provide a slightly looser fit when the user is relaxed, and a firmer fit when the user is tense. This makes it possible to adjust the fit based on the user's emotions, thereby supporting the user in choosing comfortable shoes.

[0095] The online shoe purchasing support system may further include a material selection unit that uses different materials to create a try-on model that closely resembles the feel of a user's foot. For example, when a 3D printer uses different materials to create a try-on model, the material selection unit selects a material that closely resembles the feel of a user's foot. This allows for a more realistic try-on experience. For example, the material selection unit may print a combination of flexible materials and materials with different hardness to create a try-on model that closely resembles the feel of a user's foot. This allows for the use of different materials to create a try-on model that closely resembles the feel of a user's foot, improving user satisfaction.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The foot photography section takes a photo of the foot with a smartphone. The user uses the smartphone to take photos of the front, side, back, etc. of the foot. Step 2: The 3D foot print model generator uses the captured data to generate a 3D foot print model. The generator uses text generation AI (e.g., LLM) to analyze the shape and dimensions of the foot and create an accurate 3D model. Step 3: The shoe size suggestion unit suggests the optimal shoe size and style based on the generated 3D foot model. The generation AI considers the shape and dimensions of the foot, as well as the user's preferences and past purchase history, to select the most suitable shoes. Step 4: The 3D printing department uses a 3D printer to create the shoes in the proposed size. The 3D printer prints a model to try on based on the shoe design data proposed by the generative AI.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A foot photography section that takes photos of your feet with your smartphone, a 3D foot shape model generating unit that generates a 3D foot shape model based on the data captured by the foot photographing unit; a shoe size suggestion unit that suggests an optimal shoe size based on the 3D foot shape model generated by the 3D foot shape model generation unit; a 3D printing unit that 3D prints shoes of the size suggested by the shoe size suggestion unit. A system characterized by:

2. The foot imaging unit includes: A depth sensor is added to the smartphone camera to accurately capture the three-dimensional shape of the foot.

2. The system of claim 1.

3. The 3D foot shape model generation unit Analyzing the skeletal structure of the foot and generating the 3D foot model based on the skeletal structure 2. The system of claim 1.

4. The shoe size suggestion unit Analyzes the user's walking pattern and suggests the shoe size and style that best suits the walking pattern 2. The system of claim 1.

5. The 3D printing unit Using the different materials, a fitting model that feels similar to the feel of a foot is created.

2. The system of claim 1.

6. The foot imaging unit includes: Using emotion estimation, the system analyzes the user's emotions when taking a photo and guides them to take a photo in a relaxed state.

2. The system of claim 1.

Images