System

Abstract

To provide a system that generates an optimal prosthetic orthotic design based on a user's physical data.SOLUTION: A system according to the embodiment comprises an acquisition unit, an analysis unit, and a generation unit. The acquisition unit acquires user's physical data. The analysis unit analyzes the data acquired by the acquisition unit. The generation unit generates a prosthetic orthotic design based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to design prosthetics and orthotics that were optimal for each individual user, and there was a risk of discomfort or injury when wearing them.

[0005] The system according to the embodiment aims to generate a design plan for an optimal prosthetic orthotic based on a user's physical data. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a generation unit. The acquisition unit acquires body data of a user. The analysis unit analyzes the data acquired by the acquisition unit. The generation unit generates a design drawing of a prosthetic device based on the analysis result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can generate a design plan for an optimal prosthetic orthotic based on the user's physical data. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The custom-made prosthetics and orthotics design system according to the embodiment of the present invention is a system that uses AI to generate a blueprint of a custom-made prosthetics and orthotics for people who have congenital or acquired limb defects. This system acquires the user's body data, and the AI ​​performs analysis to generate a blueprint of the prosthetics and orthotics that is optimal for the individual. Furthermore, by generating a skin that suits the individual, the orthotics are realized to be less noticeable. For example, when acquiring the user's body data, a device that acquires body data is used to collect detailed data of the user's body. For example, data such as the shape and size of the user's limbs and the color of the skin is acquired. This provides basic data for designing the prosthetics and orthotics that are optimal for each individual user. Next, the AI ​​performs analysis based on the acquired data. The AI ​​analyzes the collected data and generates a blueprint of the prosthetics and orthotics that are optimal for the user. For example, the shape and size of the prosthetics and orthotics are adjusted to match the shape and size of the user's limbs. In addition, the skin of the prosthetics and orthotics is generated to match the color of the user's skin. This allows the prosthetics and orthotics that are optimal for each individual user to be designed. Furthermore, the prosthetics and orthotics are manufactured based on the generated blueprint. The manufactured prosthetic orthotic is worn by the user to check the fit and usability. If necessary, fine adjustments are made to provide the optimal prosthetic orthotic. This reduces the occurrence of slippage, abrasions, and blisters of the prosthetic orthotic. This mechanism makes it possible to provide custom-made prosthetic orthotics tailored to the individual needs of people with congenital or acquired limb defects. This reduces the occurrence of slippage, abrasions, and blisters of the prosthetic orthotic, making it more comfortable to use. In addition, by generating skin that suits each individual user, it is possible to realize a less noticeable prosthetic orthotic. For example, by generating skin that matches the user's skin color, the prosthetic orthotic will look natural. As a result, the custom-made prosthetic orthotic design system can provide the optimal prosthetic orthotic for each individual user by acquiring and analyzing the user's physical data and generating a design drawing for the prosthetic orthotic.

[0029] The custom-made prosthetics and orthotics design system according to the embodiment includes an acquisition unit, an analysis unit, and a generation unit. The acquisition unit acquires the user's body data. The user's body data includes, for example, height, weight, body fat percentage, muscle mass, and the like, but is not limited to such examples. The acquisition unit acquires detailed shape data of the user's body, for example, using a 3D scanner. The acquisition unit can also acquire the user's skin color using a camera. Furthermore, the acquisition unit can measure the user's muscle mass and body fat percentage using a sensor. For example, the 3D scanner scans the user's body and generates a detailed 3D model. The camera acquires the user's skin color with high accuracy and stores it as digital data. The sensor measures the user's muscle mass and body fat percentage and acquires the data. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data, for example, using a machine learning algorithm, and determines the shape and size of the prosthetics and orthotics that are optimal for the user. The analysis unit performs, for example, statistical analysis of the data, and derives design parameters for the optimal prosthetics and orthotics based on the user's body data. The analysis unit can also use a machine learning algorithm to analyze the user's body data and predict the optimal shape and size of the prosthetic orthosis. For example, the analysis unit performs a regression analysis based on the user's body data as a statistical analysis of the data, and derives optimal design parameters. The machine learning algorithm learns past data and predicts optimal design parameters based on new data. The generation unit generates a design drawing of the prosthetic orthosis based on the analysis result obtained by the analysis unit. The generation unit generates a detailed design drawing of the prosthetic orthosis using, for example, CAD software. The generation unit can also generate a 3D model of the prosthetic orthosis using, for example, 3D modeling software. The generation unit can also generate a skin that matches the skin color of the user. For example, the generation unit uses CAD software to create a detailed design drawing of the prosthetic orthosis. The 3D modeling software generates a 3D model of the prosthetic orthosis, which can be visually confirmed. The generation unit generates a skin that matches the skin color of the user and applies it to the prosthetic orthosis.As a result, the custom-made prosthetics and orthotics design system according to the embodiment can provide an optimal prosthetics and orthotics for each user by acquiring and analyzing the user's body data and generating a design drawing for the prosthetics and orthotics. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can generate a design drawing using an AI model that inputs the analysis results obtained by the analysis unit and outputs a design drawing.

[0030] The acquisition unit acquires the user's body data. The user's body data includes, for example, height, weight, body fat percentage, muscle mass, etc., but is not limited to such examples. The acquisition unit acquires detailed shape data of the user's body using, for example, a 3D scanner. Specifically, the 3D scanner scans the user's body from multiple angles to generate a detailed 3D model. This 3D model accurately reflects the dimensions and shape of the user's body, and serves as important basic data in the design of prosthetics and orthotics. The acquisition unit can also acquire the user's skin color using a camera. The camera takes high-resolution images and saves the color and texture of the user's skin as digital data. This allows the appearance of the prosthetics and orthotics to be closer to the user's natural skin color. Furthermore, the acquisition unit can also measure the user's muscle mass and body fat percentage using a sensor. For example, a sensor using a bioimpedance method measures the amount of water in the body by passing a weak current and estimates the muscle mass and body fat percentage. These data are important for improving the fit and functionality of prosthetics and orthotics. The acquisition unit combines these various devices to comprehensively acquire the user's body data and transmit it to a central database in real time, allowing the acquisition unit to accurately and quickly collect detailed information about the user's body and provide it to the analysis unit and generation unit.

[0031] The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data using, for example, a machine learning algorithm, and determines the shape and size of the prosthetic device that is optimal for the user. Specifically, the analysis unit performs statistical analysis of the data and derives the design parameters of the optimal prosthetic device based on the user's physical data. For example, regression analysis is used to predict the optimal dimensions of the prosthetic device from data such as the user's height, weight, and muscle mass. The analysis unit can also analyze the user's physical data and predict the optimal shape and size of the prosthetic device using a machine learning algorithm. For example, a model using deep learning learns past data and predicts optimal design parameters based on new data. This model receives the user's physical data as input and outputs the optimal shape and size of the prosthetic device. Furthermore, the analysis unit can analyze the user's motion data and derive design parameters for improving the functionality of the prosthetic device. For example, the user's walking data is analyzed to optimize the range of motion of the joints of the prosthetic device and the force transmission efficiency. This allows the analysis unit to analyze the user's physical data from multiple angles and derive the optimal design parameters of the prosthetic device.

[0032] The generation unit generates a design drawing of the prosthetic orthosis based on the analysis result obtained by the analysis unit. The generation unit generates a detailed design drawing of the prosthetic orthosis using, for example, CAD software. Specifically, the CAD software designs the dimensions and shapes of each part of the prosthetic orthosis in detail based on the design parameters provided by the analysis unit. The generation unit can also generate a 3D model of the prosthetic orthosis using 3D modeling software. The 3D modeling software generates a three-dimensional model of the prosthetic orthosis based on the design drawing and can visually confirm it. Furthermore, the generation unit can generate a skin that matches the skin color of the user. For example, based on the color data of the user's skin acquired by a camera, the appearance of the prosthetic orthosis is customized to match the skin color of the user. This makes the prosthetic orthosis look natural and have a design that fits the user's body. Some or all of the above-mentioned 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 generate a design drawing using an AI model that inputs the analysis result obtained by the analysis unit and outputs a design drawing. This AI model learns from past design data and generates optimal designs based on new data. This enables the generator to efficiently and accurately generate designs for prosthetics and orthotics, and provide users with optimal prosthetics and orthotics.

[0033] The acquisition unit can acquire the user's body data with high accuracy. High accuracy includes, for example, a small range of measurement error and a high accuracy of the type of sensor used, but is not limited to such examples. The acquisition unit acquires detailed shape data of the user's body using, for example, a 3D scanner. For example, the 3D scanner scans the user's body and generates a detailed 3D model. The acquisition unit can also acquire the user's skin color using a camera. For example, the camera acquires the user's skin color with high accuracy and stores it as digital data. Furthermore, the acquisition unit can also measure the user's muscle mass and body fat percentage using a sensor. For example, the sensor measures the user's muscle mass and body fat percentage and acquires data. This allows the user's body data to be acquired in detail, making it possible to design a prosthetic device with higher accuracy. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired by the 3D scanner to the generation AI and cause the generation AI to execute analysis of the data.

[0034] The analysis unit can analyze the shape and size of the prosthetic device suitable for the user based on the acquired data. The shape and size include, for example, a measurement method of dimensions and a definition of the shape, but are not limited to such examples. The analysis unit, for example, performs a statistical analysis of the data and derives optimal design parameters for the prosthetic device based on the user's physical data. For example, the analysis unit performs a regression analysis based on the user's physical data as a statistical analysis of the data to derive optimal design parameters. The analysis unit can also analyze the user's physical data and predict the optimal shape and size of the prosthetic device using a machine learning algorithm. For example, the machine learning algorithm learns past data and predicts optimal design parameters based on new data. As a result, an improvement in fit can be expected by analyzing the shape and size of the prosthetic device suitable for the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquired data to a generation AI and have the generation AI execute the analysis of the data.

[0035] The generating unit can generate a design drawing of the prosthetic orthosis based on the analysis result. The generation includes, for example, details of the software used and the generation process, but is not limited to such examples. The generating unit generates a detailed design drawing of the prosthetic orthosis using, for example, CAD software. For example, the generating unit creates a detailed design drawing of the prosthetic orthosis using CAD software. The generating unit can also generate a 3D model of the prosthetic orthosis using 3D modeling software. For example, the 3D modeling software can generate a 3D model of the prosthetic orthosis and visually confirm it. This allows the optimal prosthetic orthosis to be provided to each individual user by generating a design drawing of the prosthetic orthosis based on the analysis result. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the analysis result to the generating AI and cause the generating AI to generate the design drawing.

[0036] The generating unit can generate a skin based on the skin color of the user. The skin color includes, for example, a color measurement method and a color classification standard, but is not limited to such examples. The generating unit, for example, acquires the skin color of the user using a camera, and generates a skin based on the data. For example, the generating unit generates a skin of the prosthetic device based on the skin color data acquired by the camera. The generating unit can also use a color correction algorithm to generate a skin that matches the skin color of the user. For example, the generating unit uses a color correction algorithm to generate a skin that matches the skin color of the user. As a result, a prosthetic device that is less noticeable can be provided by generating a skin that matches the skin color of the user. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input skin color data to the generating AI and cause the generating AI to generate the skin.

[0037] The custom-made prosthetics and orthotics design system includes a production unit that produces prosthetics and orthotics based on the generated design drawing. The production includes, for example, details of the materials to be used and the production process, but is not limited to such examples. The production unit produces prosthetics and orthotics using, for example, a 3D printer. For example, the production unit produces prosthetics and orthotics based on the generated design drawing using a 3D printer. The production unit can also produce prosthetics and orthotics using a CNC (computer numerical control) machine. For example, the production unit produces prosthetics and orthotics based on the generated design drawing using a CNC machine. Furthermore, the production unit can also produce prosthetics and orthotics manually. For example, the production unit produces prosthetics and orthotics manually by a craftsman and adjusts the details. As a result, prosthetics and orthotics that are optimal for individual users can be provided by producing prosthetics and orthotics based on the generated design drawing. Some or all of the above-mentioned processing in the production unit may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input design drawings into a generation AI and have the generation AI optimize the production process.

[0038] The acquisition unit can analyze the user's past physical data acquisition history and select an appropriate acquisition method. The past physical data acquisition history includes, for example, the acquisition date and time and the type of acquired data, but is not limited to such examples. The acquisition unit, for example, proposes an optimal method based on an acquisition method used by the user in the past. For example, the acquisition unit analyzes the user's past data acquisition history and proposes an optimal acquisition method. The acquisition unit can also select the most efficient acquisition method from the user's past data acquisition history. For example, the acquisition unit selects an efficient acquisition method based on the past data acquisition history. The acquisition unit can also analyze the user's past data acquisition history and optimize the acquisition time. For example, the acquisition unit optimizes the acquisition time based on the past data acquisition history. As a result, data can be acquired by the optimal method by analyzing the past data acquisition history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the past data acquisition history to the generation AI and cause the generation AI to select the optimal acquisition method.

[0039] When acquiring physical data, the acquisition unit can select data based on the user's current health condition and activity level. The health condition and activity level include, for example, heart rate, number of steps, and exercise intensity, but are not limited to such examples. For example, when the user is in good health, the acquisition unit acquires detailed data. For example, the acquisition unit acquires detailed data based on the user's heart rate and exercise intensity. In addition, the acquisition unit can also perform simplified data acquisition when the user is tired. For example, the acquisition unit acquires simplified data based on the user's heart rate and fatigue level. In addition, the acquisition unit can also select a data acquisition method suitable for exercise when the user is exercising. For example, the acquisition unit selects a data acquisition method suitable for exercise based on the user's exercise intensity and activity level. As a result, more appropriate data can be acquired by filtering data according to the user's health condition and activity level. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the health condition and activity level to the generation AI and cause the generation AI to select the data.

[0040] When acquiring the body data, the acquisition unit can preferentially acquire data with high relevance based on the geographical location information of the user. The geographical location information includes, for example, GPS data and the accuracy of the location information, but is not limited to such examples. For example, when the user is at home, the acquisition unit preferentially acquires data in a stationary state. For example, the acquisition unit preferentially acquires data in a stationary state based on the geographical location information of the user. In addition, when the user is out, the acquisition unit can also preferentially acquire data while moving. For example, the acquisition unit preferentially acquires data while moving based on the geographical location information of the user. In addition, when the user is in a specific facility, the acquisition unit can also preferentially acquire data related to the facility. For example, the acquisition unit preferentially acquires data related to the facility based on the geographical location information of the user. As a result, by acquiring data based on the geographical location information of the user, more relevant data can be acquired. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0041] The acquisition unit can analyze the user's social media activity and acquire related data when acquiring the physical data. The social media activity includes, for example, the content of the post, the number of likes, and comments, but is not limited to such examples. For example, when the user posts about exercise on social media, the acquisition unit preferentially acquires the exercise data. For example, the acquisition unit analyzes the user's social media activity and preferentially acquires the exercise data. In addition, when the user posts about health, the acquisition unit can also preferentially acquire the health data. For example, the acquisition unit analyzes the user's social media activity and preferentially acquires the health data. In addition, when the user participates in a specific event, the acquisition unit can also preferentially acquire data related to the event. For example, the acquisition unit analyzes the user's social media activity and preferentially acquires data related to the event. In this way, by analyzing the social media activity, data based on the user's interests can be acquired. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input data of the social media activity to the generation AI and cause the generation AI to select related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the physical data during the analysis. The level of detail includes, for example, the depth of the analysis and the granularity of the data to be displayed, but is not limited to such examples. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a detailed analysis based on important data. The analysis unit can also perform a simplified analysis on basic data. For example, the analysis unit performs a simplified analysis based on basic data. The analysis unit can also adjust the level of detail for specific data as necessary. For example, the analysis unit adjusts the level of detail based on specific data. This allows efficient analysis by adjusting the level of detail of the analysis based on the importance of the physical data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the body data during analysis. The categories include, for example, classification by type of data or by use, but are not limited to such examples. The analysis unit applies a shape analysis algorithm to, for example, the shape data of the limbs. For example, the analysis unit applies a shape analysis algorithm based on the shape data of the limbs. The analysis unit can also apply a color analysis algorithm to the skin color data. For example, the analysis unit applies a color analysis algorithm based on the skin color data. The analysis unit can also apply a size analysis algorithm to the size data. For example, the analysis unit applies a size analysis algorithm based on the size data. This enables more accurate analysis by applying an analysis algorithm according to the category of the body data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the acquisition time of the physical data during the analysis. The acquisition time includes, for example, the priority of the latest data and the importance of past data, but is not limited to such examples. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit prioritizes the analysis based on the latest data. The analysis unit can also perform the analysis while referring to past data. For example, the analysis unit performs the analysis based on past data. The analysis unit can also prioritize the analysis of data from a specific period. For example, the analysis unit prioritizes the analysis based on data from a specific period. As a result, the analysis priority is determined based on the acquisition time of the physical data, so that the latest data can be analyzed with priority. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquisition time of the data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the physical data during analysis. The relevance includes, for example, correlation of data and scoring of relevance, but is not limited to such examples. For example, the analysis unit preferentially analyzes highly relevant data. For example, the analysis unit preferentially performs analysis based on highly relevant data. The analysis unit can also postpone data with low relevance. For example, the analysis unit performs analysis based on data with low relevance. The analysis unit can also adjust the order of analysis based on the relevance of specific data. For example, the analysis unit adjusts the order of analysis based on the relevance of specific data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the physical data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The generation unit can adjust the level of detail of the design drawing based on the importance of the analysis result when generating the design drawing. The level of detail includes, for example, the accuracy of the design drawing and the granularity of the information to be displayed, but is not limited to such examples. The generation unit generates, for example, a detailed design drawing based on an important analysis result. For example, the generation unit generates a detailed design drawing based on an important analysis result. The generation unit can also generate a simplified design drawing based on a basic analysis result. For example, the generation unit generates a simplified design drawing based on a basic analysis result. The generation unit can also adjust the level of detail as necessary based on a specific analysis result. For example, the generation unit adjusts the level of detail based on a specific analysis result. This enables efficient generation of the design drawing by adjusting the level of detail of the design drawing based on the importance of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the design drawing.

[0047] The generating unit can apply different generating algorithms according to the category of the prosthetic device when generating the design drawing. The category includes, for example, classification according to the type of prosthetic device and its use, but is not limited to such examples. For example, the generating unit applies a generating algorithm dedicated to a prosthetic hand to a prosthetic hand. For example, the generating unit applies a generating algorithm dedicated to a prosthetic hand when generating a design drawing for a prosthetic hand. The generating unit can also apply a generating algorithm dedicated to a prosthetic leg to a prosthetic leg. For example, the generating unit applies a generating algorithm dedicated to a prosthetic leg when generating a design drawing for a prosthetic leg. The generating unit can also apply a generating algorithm according to the category to a specific prosthetic device. For example, the generating unit applies a generating algorithm according to the category when generating a design drawing for a specific prosthetic device. As a result, a more accurate design drawing is generated by applying a generating algorithm according to the category of the prosthetic device. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the category of the prosthetic device to the generating AI and cause the generating AI to apply the generating algorithm.

[0048] The generation unit can determine the priority of the design drawing based on the acquisition time of the analysis result when generating the design drawing. The acquisition time includes, for example, the priority of the latest data and the importance of past data, but is not limited to such examples. The generation unit generates the design drawing based on, for example, the latest analysis result. For example, the generation unit generates the design drawing based on the latest analysis result. The generation unit can also generate the design drawing while referring to the past analysis result. For example, the generation unit generates the design drawing based on the past analysis result. The generation unit can also generate the design drawing based on the analysis result of a specific period. For example, the generation unit generates the design drawing based on the analysis result of a specific period. As a result, the priority of the design drawing can be determined based on the acquisition time of the analysis result, so that the latest data can be preferentially reflected in the design drawing. A part or all of the above-mentioned 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 acquisition time of the analysis result to the generation AI and cause the generation AI to execute the determination of the priority of the design drawing.

[0049] The generation unit can adjust the order of the blueprints based on the relevance of the analysis results when generating the blueprints. The relevance includes, for example, data correlation and relevance scoring, but is not limited to such examples. The generation unit generates the blueprints based on, for example, highly relevant analysis results. For example, the generation unit generates the blueprints based on highly relevant analysis results. The generation unit can also postpone the generation of less relevant analysis results. For example, the generation unit generates the blueprints based on less relevant analysis results. The generation unit can also adjust the order of the blueprints based on the relevance of a specific analysis result. For example, the generation unit adjusts the order of the blueprints based on the relevance of a specific analysis result. This enables efficient blueprint generation by adjusting the order of the blueprints based on the relevance of the analysis results. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of the blueprints.

[0050] The production department can adjust the level of detail of the production based on the importance of the blueprint during production. The level of detail includes, for example, the accuracy of production and the granularity of the information to be displayed, but is not limited to such examples. The production department performs detailed production based on, for example, an important blueprint. For example, the production department performs detailed production based on an important blueprint. The production department can also perform simplified production based on a basic blueprint. For example, the production department performs simplified production based on a basic blueprint. The production department can also adjust the level of detail as necessary based on a specific blueprint. For example, the production department adjusts the level of detail based on a specific blueprint. This allows efficient production by adjusting the level of detail of production based on the importance of the blueprint. Part 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 importance of the blueprint to the generation AI and cause the generation AI to adjust the level of detail of production.

[0051] The production unit can apply different production methods depending on the category of the prosthetic orthotic during production. The categories include, for example, classification by type of prosthetic orthotic or by use, but are not limited to such examples. For example, the production unit applies a production method dedicated to prosthetic hands to prosthetic hands. For example, the production unit applies a production method dedicated to prosthetic hands when producing prosthetic hands. The production unit can also apply a production method dedicated to prosthetic legs to prosthetic legs. For example, the production unit applies a production method dedicated to prosthetic legs when producing prosthetic legs. The production unit can also apply a production method according to the category to a specific prosthetic orthotic. For example, the production unit applies a production method according to the category when producing a specific prosthetic orthotic. As a result, by applying a production method according to the category of the prosthetic orthotic, more accurate production is possible. Part or all of the above-mentioned 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 input the category of the prosthetic orthotic to the generation AI and cause the generation AI to apply the production method.

[0052] The production department can adjust the order of production based on the acquisition time of the blueprint during production. The acquisition time includes, for example, the priority of the latest data and the importance of past data, but is not limited to such examples. The production department, for example, prioritizes production based on the latest blueprint. For example, the production department prioritizes production based on the latest blueprint. The production department can also perform production while referring to past blueprints. For example, the production department performs production based on past blueprints. The production department can also adjust the order of production based on blueprints of a specific period. For example, the production department adjusts the order of production based on blueprints of a specific period. As a result, the latest blueprint can be produced preferentially by adjusting the order of production based on the acquisition time of the blueprint. Part 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 acquisition time of the blueprint to the generation AI and cause the generation AI to execute the adjustment of the order of production.

[0053] The production department can adjust the production procedure based on the relevance of the blueprints during production. The relevance includes, for example, data correlation and relevance scoring, but is not limited to such examples. For example, the production department prioritizes production based on highly relevant blueprints. For example, the production department prioritizes production based on highly relevant blueprints. The production department can also postpone production based on less relevant blueprints. For example, the production department postpones production based on less relevant blueprints. The production department can also adjust the production procedure based on the relevance of a specific blueprint. For example, the production department adjusts the production procedure based on the relevance of a specific blueprint. As a result, efficient production is possible by adjusting the production procedure based on the relevance of the blueprints. Part 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 relevance of the blueprints to the generation AI and cause the generation AI to execute the adjustment of the production procedure.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When acquiring the user's physical data, the acquisition unit can also acquire the user's lifestyle data. For example, by acquiring data such as the user's diet, exercise habits, and sleep patterns, more comprehensive physical data can be collected. The acquisition unit can also reflect the user's lifestyle data in the design of the prosthetic device. For example, for a user who has an exercise habit, a prosthetic device suitable for exercise can be designed. Furthermore, the acquisition unit can provide the user's lifestyle data to the analysis unit, and the analysis unit can derive design parameters for the optimal prosthetic device based on this data. This makes it possible to provide a prosthetic device that matches the user's lifestyle.

[0056] The generation unit can use environmentally friendly materials when generating a design drawing for a prosthetic orthotic. For example, by using renewable or recycled materials, the environmental impact can be reduced. The generation unit can also adopt an environmentally friendly manufacturing process. For example, an energy-efficient manufacturing method or a low-emission manufacturing technique can be used. Furthermore, the generation unit can provide the user with options for environmentally friendly prosthetic orthotics. This makes it possible to provide an environmentally friendly prosthetic orthotic.

[0057] The production department can reflect the user's feedback in real time when producing the prosthetics and orthotics. For example, by immediately reflecting any defects or improvements that the user feels during production, it is possible to provide a more accurate prosthetics and orthotics. The production department can also optimize the production process based on the user's feedback. For example, the production department can analyze the user's feedback and adjust the production procedure and materials used. Furthermore, the production department can continuously collect the user's feedback and use it to improve the production process. This makes it possible to provide a prosthetics and orthotics that reflects the user's feedback.

[0058] The production department can provide an adjustment function for responding to changes in the user's body when producing the prosthetics. For example, the size and shape of the prosthetics can be adjusted according to changes in the user's weight and muscle mass. The production department can also monitor changes in the user's body and adjust the prosthetics as necessary. Furthermore, the production department can provide customization options for responding to changes in the user's body. This makes it possible to provide prosthetics that respond to changes in the user's body.

[0059] When manufacturing the prosthetics and orthotics, the production department can add functions to adapt the prosthetics and orthotics to the user's living environment. For example, a prosthetics and orthotics with a heat retention function can be provided to a user living in a cold region. Also, a prosthetics and orthotics with a waterproof function can be provided to a user living in a humid region. Furthermore, the production department can provide customization options according to the user's living environment. In this way, a prosthetics and orthotics adapted to the user's living environment can be provided.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The acquisition unit acquires the user's physical data. The user's physical data includes, for example, height, weight, body fat percentage, muscle mass, etc. The acquisition unit acquires detailed shape data of the user's body using a 3D scanner, acquires the user's skin color using a camera, and measures the user's muscle mass and body fat percentage using a sensor. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data using a machine learning algorithm and determines the shape and size of the prosthetic device that is optimal for the user. The analysis unit performs statistical analysis of the data and derives design parameters for the optimal prosthetic device based on the user's physical data. Step 3: The generator generates a design drawing of the prosthetic orthotic based on the analysis results obtained by the analyzer. The generator generates detailed design drawings and 3D models of the prosthetic orthotic using CAD software or 3D modeling software, and can also generate skin that matches the user's skin color.

[0062] (Example 2) The custom-made prosthetics and orthotics design system according to the embodiment of the present invention is a system that uses AI to generate a blueprint of a custom-made prosthetics and orthotics for people who have congenital or acquired limb defects. This system acquires the user's body data, and the AI ​​performs analysis to generate a blueprint of the prosthetics and orthotics that is optimal for the individual. Furthermore, by generating a skin that suits the individual, the orthotics are realized to be less noticeable. For example, when acquiring the user's body data, a device that acquires body data is used to collect detailed data of the user's body. For example, data such as the shape and size of the user's limbs and the color of the skin is acquired. This provides basic data for designing the prosthetics and orthotics that are optimal for each individual user. Next, the AI ​​performs analysis based on the acquired data. The AI ​​analyzes the collected data and generates a blueprint of the prosthetics and orthotics that are optimal for the user. For example, the shape and size of the prosthetics and orthotics are adjusted to match the shape and size of the user's limbs. In addition, the skin of the prosthetics and orthotics is generated to match the color of the user's skin. This allows the prosthetics and orthotics that are optimal for each individual user to be designed. Furthermore, the prosthetics and orthotics are manufactured based on the generated blueprint. The manufactured prosthetic orthotic is worn by the user to check the fit and usability. If necessary, fine adjustments are made to provide the optimal prosthetic orthotic. This reduces the occurrence of slippage, abrasions, and blisters of the prosthetic orthotic. This mechanism makes it possible to provide custom-made prosthetic orthotics tailored to the individual needs of people with congenital or acquired limb defects. This reduces the occurrence of slippage, abrasions, and blisters of the prosthetic orthotic, making it more comfortable to use. In addition, by generating skin that suits each individual user, it is possible to realize a less noticeable prosthetic orthotic. For example, by generating skin that matches the user's skin color, the prosthetic orthotic will look natural. As a result, the custom-made prosthetic orthotic design system can provide the optimal prosthetic orthotic for each individual user by acquiring and analyzing the user's physical data and generating a design drawing for the prosthetic orthotic.

[0063] The custom-made prosthetics and orthotics design system according to the embodiment includes an acquisition unit, an analysis unit, and a generation unit. The acquisition unit acquires the user's body data. The user's body data includes, for example, height, weight, body fat percentage, muscle mass, and the like, but is not limited to such examples. The acquisition unit acquires detailed shape data of the user's body, for example, using a 3D scanner. The acquisition unit can also acquire the user's skin color using a camera. Furthermore, the acquisition unit can measure the user's muscle mass and body fat percentage using a sensor. For example, the 3D scanner scans the user's body and generates a detailed 3D model. The camera acquires the user's skin color with high accuracy and stores it as digital data. The sensor measures the user's muscle mass and body fat percentage and acquires the data. The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data, for example, using a machine learning algorithm, and determines the shape and size of the prosthetics and orthotics that are optimal for the user. The analysis unit performs, for example, statistical analysis of the data, and derives design parameters for the optimal prosthetics and orthotics based on the user's body data. The analysis unit can also use a machine learning algorithm to analyze the user's body data and predict the optimal shape and size of the prosthetic orthosis. For example, the analysis unit performs a regression analysis based on the user's body data as a statistical analysis of the data, and derives optimal design parameters. The machine learning algorithm learns past data and predicts optimal design parameters based on new data. The generation unit generates a design drawing of the prosthetic orthosis based on the analysis result obtained by the analysis unit. The generation unit generates a detailed design drawing of the prosthetic orthosis using, for example, CAD software. The generation unit can also generate a 3D model of the prosthetic orthosis using, for example, 3D modeling software. The generation unit can also generate a skin that matches the skin color of the user. For example, the generation unit uses CAD software to create a detailed design drawing of the prosthetic orthosis. The 3D modeling software generates a 3D model of the prosthetic orthosis, which can be visually confirmed. The generation unit generates a skin that matches the skin color of the user and applies it to the prosthetic orthosis.As a result, the custom-made prosthetics and orthotics design system according to the embodiment can provide an optimal prosthetics and orthotics for each user by acquiring and analyzing the user's body data and generating a design drawing for the prosthetics and orthotics. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can generate a design drawing using an AI model that inputs the analysis results obtained by the analysis unit and outputs a design drawing.

[0064] The acquisition unit acquires the user's body data. The user's body data includes, for example, height, weight, body fat percentage, muscle mass, etc., but is not limited to such examples. The acquisition unit acquires detailed shape data of the user's body using, for example, a 3D scanner. Specifically, the 3D scanner scans the user's body from multiple angles to generate a detailed 3D model. This 3D model accurately reflects the dimensions and shape of the user's body, and serves as important basic data in the design of prosthetics and orthotics. The acquisition unit can also acquire the user's skin color using a camera. The camera takes high-resolution images and saves the color and texture of the user's skin as digital data. This allows the appearance of the prosthetics and orthotics to be closer to the user's natural skin color. Furthermore, the acquisition unit can also measure the user's muscle mass and body fat percentage using a sensor. For example, a sensor using a bioimpedance method measures the amount of water in the body by passing a weak current and estimates the muscle mass and body fat percentage. These data are important for improving the fit and functionality of prosthetics and orthotics. The acquisition unit combines these various devices to comprehensively acquire the user's body data and transmit it to a central database in real time, allowing the acquisition unit to accurately and quickly collect detailed information about the user's body and provide it to the analysis unit and generation unit.

[0065] The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data using, for example, a machine learning algorithm, and determines the shape and size of the prosthetic device that is optimal for the user. Specifically, the analysis unit performs statistical analysis of the data and derives the design parameters of the optimal prosthetic device based on the user's physical data. For example, regression analysis is used to predict the optimal dimensions of the prosthetic device from data such as the user's height, weight, and muscle mass. The analysis unit can also analyze the user's physical data and predict the optimal shape and size of the prosthetic device using a machine learning algorithm. For example, a model using deep learning learns past data and predicts optimal design parameters based on new data. This model receives the user's physical data as input and outputs the optimal shape and size of the prosthetic device. Furthermore, the analysis unit can analyze the user's motion data and derive design parameters for improving the functionality of the prosthetic device. For example, the user's walking data is analyzed to optimize the range of motion of the joints of the prosthetic device and the force transmission efficiency. This allows the analysis unit to analyze the user's physical data from multiple angles and derive the optimal design parameters of the prosthetic device.

[0066] The generation unit generates a design drawing of the prosthetic orthosis based on the analysis result obtained by the analysis unit. The generation unit generates a detailed design drawing of the prosthetic orthosis using, for example, CAD software. Specifically, the CAD software designs the dimensions and shapes of each part of the prosthetic orthosis in detail based on the design parameters provided by the analysis unit. The generation unit can also generate a 3D model of the prosthetic orthosis using 3D modeling software. The 3D modeling software generates a three-dimensional model of the prosthetic orthosis based on the design drawing and can visually confirm it. Furthermore, the generation unit can generate a skin that matches the skin color of the user. For example, based on the color data of the user's skin acquired by a camera, the appearance of the prosthetic orthosis is customized to match the skin color of the user. This makes the prosthetic orthosis look natural and have a design that fits the user's body. Some or all of the above-mentioned 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 generate a design drawing using an AI model that inputs the analysis result obtained by the analysis unit and outputs a design drawing. This AI model learns from past design data and generates optimal designs based on new data. This enables the generator to efficiently and accurately generate designs for prosthetics and orthotics, and provide users with optimal prosthetics and orthotics.

[0067] The acquisition unit can acquire the user's body data with high accuracy. High accuracy includes, for example, a small range of measurement error and a high accuracy of the type of sensor used, but is not limited to such examples. The acquisition unit acquires detailed shape data of the user's body using, for example, a 3D scanner. For example, the 3D scanner scans the user's body and generates a detailed 3D model. The acquisition unit can also acquire the user's skin color using a camera. For example, the camera acquires the user's skin color with high accuracy and stores it as digital data. Furthermore, the acquisition unit can also measure the user's muscle mass and body fat percentage using a sensor. For example, the sensor measures the user's muscle mass and body fat percentage and acquires data. This allows the user's body data to be acquired in detail, making it possible to design a prosthetic device with higher accuracy. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired by the 3D scanner to the generation AI and cause the generation AI to execute analysis of the data.

[0068] The analysis unit can analyze the shape and size of the prosthetic device suitable for the user based on the acquired data. The shape and size include, for example, a measurement method of dimensions and a definition of the shape, but are not limited to such examples. The analysis unit, for example, performs a statistical analysis of the data and derives optimal design parameters for the prosthetic device based on the user's physical data. For example, the analysis unit performs a regression analysis based on the user's physical data as a statistical analysis of the data to derive optimal design parameters. The analysis unit can also analyze the user's physical data and predict the optimal shape and size of the prosthetic device using a machine learning algorithm. For example, the machine learning algorithm learns past data and predicts optimal design parameters based on new data. As a result, an improvement in fit can be expected by analyzing the shape and size of the prosthetic device suitable for the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquired data to a generation AI and have the generation AI execute the analysis of the data.

[0069] The generating unit can generate a design drawing of the prosthetic orthosis based on the analysis result. The generation includes, for example, details of the software used and the generation process, but is not limited to such examples. The generating unit generates a detailed design drawing of the prosthetic orthosis using, for example, CAD software. For example, the generating unit creates a detailed design drawing of the prosthetic orthosis using CAD software. The generating unit can also generate a 3D model of the prosthetic orthosis using 3D modeling software. For example, the 3D modeling software can generate a 3D model of the prosthetic orthosis and visually confirm it. This allows the optimal prosthetic orthosis to be provided to each individual user by generating a design drawing of the prosthetic orthosis based on the analysis result. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the analysis result to the generating AI and cause the generating AI to generate the design drawing.

[0070] The generating unit can generate a skin based on the skin color of the user. The skin color includes, for example, a color measurement method and a color classification standard, but is not limited to such examples. The generating unit, for example, acquires the skin color of the user using a camera, and generates a skin based on the data. For example, the generating unit generates a skin of the prosthetic device based on the skin color data acquired by the camera. The generating unit can also use a color correction algorithm to generate a skin that matches the skin color of the user. For example, the generating unit uses a color correction algorithm to generate a skin that matches the skin color of the user. As a result, a prosthetic device that is less noticeable can be provided by generating a skin that matches the skin color of the user. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input skin color data to the generating AI and cause the generating AI to generate the skin.

[0071] The custom-made prosthetics and orthotics design system includes a production unit that produces prosthetics and orthotics based on the generated design drawing. The production includes, for example, details of the materials to be used and the production process, but is not limited to such examples. The production unit produces prosthetics and orthotics using, for example, a 3D printer. For example, the production unit produces prosthetics and orthotics based on the generated design drawing using a 3D printer. The production unit can also produce prosthetics and orthotics using a CNC (computer numerical control) machine. For example, the production unit produces prosthetics and orthotics based on the generated design drawing using a CNC machine. Furthermore, the production unit can also produce prosthetics and orthotics manually. For example, the production unit produces prosthetics and orthotics manually by a craftsman and adjusts the details. As a result, prosthetics and orthotics that are optimal for individual users can be provided by producing prosthetics and orthotics based on the generated design drawing. Some or all of the above-mentioned processing in the production unit may be performed using, for example, AI, or may be performed without using AI. For example, the production department can input design drawings into a generation AI and have the generation AI optimize the production process.

[0072] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the physical data based on the estimated emotion of the user. For example, when the user is relaxed, the acquisition unit immediately starts acquiring the physical data. For example, the acquisition unit detects that the user is relaxed using an emotion estimation algorithm and starts acquiring the physical data. In addition, when the user is tense, the acquisition unit can wait until the user is relaxed and then acquire the physical data. For example, the acquisition unit detects that the user is tense using an emotion estimation algorithm and waits until the user is relaxed and then acquires the data. In addition, when the user is in a hurry, the acquisition unit can use a simplified procedure for quickly acquiring the physical data. For example, the acquisition unit detects that the user is in a hurry using an emotion estimation algorithm and acquires the data by a simplified procedure. In this way, by adjusting the timing of acquiring the physical data according to the user's emotion, more accurate data can be acquired. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can cause the generation AI to execute an emotion estimation algorithm and adjust the timing of data acquisition based on the emotion.

[0073] The acquisition unit can analyze the user's past physical data acquisition history and select an appropriate acquisition method. The past physical data acquisition history includes, for example, the acquisition date and time and the type of acquired data, but is not limited to such examples. The acquisition unit, for example, proposes an optimal method based on an acquisition method used by the user in the past. For example, the acquisition unit analyzes the user's past data acquisition history and proposes an optimal acquisition method. The acquisition unit can also select the most efficient acquisition method from the user's past data acquisition history. For example, the acquisition unit selects an efficient acquisition method based on the past data acquisition history. The acquisition unit can also analyze the user's past data acquisition history and optimize the acquisition time. For example, the acquisition unit optimizes the acquisition time based on the past data acquisition history. As a result, data can be acquired by the optimal method by analyzing the past data acquisition history. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the past data acquisition history to the generation AI and cause the generation AI to select the optimal acquisition method.

[0074] When acquiring physical data, the acquisition unit can select data based on the user's current health condition and activity level. The health condition and activity level include, for example, heart rate, number of steps, and exercise intensity, but are not limited to such examples. For example, when the user is in good health, the acquisition unit acquires detailed data. For example, the acquisition unit acquires detailed data based on the user's heart rate and exercise intensity. In addition, the acquisition unit can also perform simplified data acquisition when the user is tired. For example, the acquisition unit acquires simplified data based on the user's heart rate and fatigue level. In addition, the acquisition unit can also select a data acquisition method suitable for exercise when the user is exercising. For example, the acquisition unit selects a data acquisition method suitable for exercise based on the user's exercise intensity and activity level. As a result, more appropriate data can be acquired by filtering data according to the user's health condition and activity level. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the health condition and activity level to the generation AI and cause the generation AI to select the data.

[0075] The acquisition unit can estimate the user's emotion and determine the priority of the physical data to be acquired based on the estimated emotion of the user. For example, when the user is relaxed, the acquisition unit preferentially acquires detailed data. For example, the acquisition unit detects that the user is relaxed using an emotion estimation algorithm and preferentially acquires detailed data. In addition, the acquisition unit can also preferentially acquire basic data when the user is nervous. For example, the acquisition unit detects that the user is nervous using an emotion estimation algorithm and preferentially acquires basic data. In addition, the acquisition unit can also preferentially acquire the most important data when the user is in a hurry. For example, the acquisition unit detects that the user is in a hurry using an emotion estimation algorithm and preferentially acquires the most important data. In this way, important data can be preferentially acquired by determining the priority of data according to the emotion of the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can have the generation AI run an emotion estimation algorithm to prioritize data based on emotion.

[0076] When acquiring the body data, the acquisition unit can preferentially acquire data with high relevance based on the geographical location information of the user. The geographical location information includes, for example, GPS data and the accuracy of the location information, but is not limited to such examples. For example, when the user is at home, the acquisition unit preferentially acquires data in a stationary state. For example, the acquisition unit preferentially acquires data in a stationary state based on the geographical location information of the user. In addition, when the user is out, the acquisition unit can also preferentially acquire data while moving. For example, the acquisition unit preferentially acquires data while moving based on the geographical location information of the user. In addition, when the user is in a specific facility, the acquisition unit can also preferentially acquire data related to the facility. For example, the acquisition unit preferentially acquires data related to the facility based on the geographical location information of the user. As a result, by acquiring data based on the geographical location information of the user, more relevant data can be acquired. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0077] The acquisition unit can analyze the user's social media activity and acquire related data when acquiring the physical data. The social media activity includes, for example, the content of the post, the number of likes, and comments, but is not limited to such examples. For example, when the user posts about exercise on social media, the acquisition unit preferentially acquires the exercise data. For example, the acquisition unit analyzes the user's social media activity and preferentially acquires the exercise data. In addition, when the user posts about health, the acquisition unit can also preferentially acquire the health data. For example, the acquisition unit analyzes the user's social media activity and preferentially acquires the health data. In addition, when the user participates in a specific event, the acquisition unit can also preferentially acquire data related to the event. For example, the acquisition unit analyzes the user's social media activity and preferentially acquires data related to the event. In this way, by analyzing the social media activity, data based on the user's interests can be acquired. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input data of the social media activity to the generation AI and cause the generation AI to select related data.

[0078] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. For example, the analysis unit detects that the user is relaxed using an emotion estimation algorithm and provides a detailed analysis result. In addition, the analysis unit can also provide a concise analysis result when the user is nervous. For example, the analysis unit detects that the user is nervous using an emotion estimation algorithm and provides a concise analysis result. In addition, the analysis unit can also provide an analysis result with the main points when the user is in a hurry. For example, the analysis unit detects that the user is in a hurry using an emotion estimation algorithm and provides an analysis result with the main points. In this way, by adjusting the expression method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easier to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can have the generation AI run an emotion estimation algorithm and adjust how the analysis is presented based on the emotion.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the physical data during the analysis. The level of detail includes, for example, the depth of the analysis and the granularity of the data to be displayed, but is not limited to such examples. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a detailed analysis based on important data. The analysis unit can also perform a simplified analysis on basic data. For example, the analysis unit performs a simplified analysis based on basic data. The analysis unit can also adjust the level of detail for specific data as necessary. For example, the analysis unit adjusts the level of detail based on specific data. This allows efficient analysis by adjusting the level of detail of the analysis based on the importance of the physical data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the body data during analysis. The categories include, for example, classification by type of data or by use, but are not limited to such examples. The analysis unit applies a shape analysis algorithm to, for example, the shape data of the limbs. For example, the analysis unit applies a shape analysis algorithm based on the shape data of the limbs. The analysis unit can also apply a color analysis algorithm to the skin color data. For example, the analysis unit applies a color analysis algorithm based on the skin color data. The analysis unit can also apply a size analysis algorithm to the size data. For example, the analysis unit applies a size analysis algorithm based on the size data. This enables more accurate analysis by applying an analysis algorithm according to the category of the body data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis. For example, the analysis unit detects that the user is relaxed using an emotion estimation algorithm and performs a detailed analysis. In addition, the analysis unit can perform a concise analysis when the user is nervous. For example, the analysis unit detects that the user is nervous using an emotion estimation algorithm and performs a concise analysis. In addition, the analysis unit can perform an analysis that focuses on the main points when the user is in a hurry. For example, the analysis unit detects that the user is in a hurry using an emotion estimation algorithm and performs an analysis that focuses on the main points. In this way, the length of the analysis can be adjusted according to the user's emotion, thereby providing an optimal analysis result for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can have the generation AI run an emotion estimation algorithm and adjust the length of the analysis based on the emotion.

[0082] The analysis unit can determine the priority of the analysis based on the acquisition time of the physical data during the analysis. The acquisition time includes, for example, the priority of the latest data and the importance of past data, but is not limited to such examples. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit prioritizes the analysis based on the latest data. The analysis unit can also perform the analysis while referring to past data. For example, the analysis unit performs the analysis based on past data. The analysis unit can also prioritize the analysis of data from a specific period. For example, the analysis unit prioritizes the analysis based on data from a specific period. As a result, the analysis priority is determined based on the acquisition time of the physical data, so that the latest data can be analyzed with priority. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the acquisition time of the data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the physical data during analysis. The relevance includes, for example, correlation of data and scoring of relevance, but is not limited to such examples. For example, the analysis unit preferentially analyzes highly relevant data. For example, the analysis unit preferentially performs analysis based on highly relevant data. The analysis unit can also postpone data with low relevance. For example, the analysis unit performs analysis based on data with low relevance. The analysis unit can also adjust the order of analysis based on the relevance of specific data. For example, the analysis unit adjusts the order of analysis based on the relevance of specific data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the physical data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0084] The generation unit can estimate the user's emotion and adjust the method of generating the blueprint based on the estimated emotion of the user. For example, when the user is relaxed, the generation unit generates a detailed blueprint. For example, the generation unit detects that the user is relaxed using an emotion estimation algorithm and generates a detailed blueprint. In addition, the generation unit can also generate a concise blueprint when the user is nervous. For example, the generation unit detects that the user is nervous using an emotion estimation algorithm and generates a concise blueprint. In addition, the generation unit can also generate a blueprint that focuses on the main points when the user is in a hurry. For example, the generation unit detects that the user is in a hurry using an emotion estimation algorithm and generates a blueprint that focuses on the main points. In this way, by adjusting the method of generating the blueprint according to the emotion of the user, it is possible to provide an optimal blueprint for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can have the generation AI run an emotion estimation algorithm and adjust the way in which the blueprint is generated based on the emotion.

[0085] The generation unit can adjust the level of detail of the design drawing based on the importance of the analysis result when generating the design drawing. The level of detail includes, for example, the accuracy of the design drawing and the granularity of the information to be displayed, but is not limited to such examples. The generation unit generates, for example, a detailed design drawing based on an important analysis result. For example, the generation unit generates a detailed design drawing based on an important analysis result. The generation unit can also generate a simplified design drawing based on a basic analysis result. For example, the generation unit generates a simplified design drawing based on a basic analysis result. The generation unit can also adjust the level of detail as necessary based on a specific analysis result. For example, the generation unit adjusts the level of detail based on a specific analysis result. This enables efficient generation of the design drawing by adjusting the level of detail of the design drawing based on the importance of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the design drawing.

[0086] The generating unit can apply different generating algorithms according to the category of the prosthetic device when generating the design drawing. The category includes, for example, classification according to the type of prosthetic device and its use, but is not limited to such examples. For example, the generating unit applies a generating algorithm dedicated to a prosthetic hand to a prosthetic hand. For example, the generating unit applies a generating algorithm dedicated to a prosthetic hand when generating a design drawing for a prosthetic hand. The generating unit can also apply a generating algorithm dedicated to a prosthetic leg to a prosthetic leg. For example, the generating unit applies a generating algorithm dedicated to a prosthetic leg when generating a design drawing for a prosthetic leg. The generating unit can also apply a generating algorithm according to the category to a specific prosthetic device. For example, the generating unit applies a generating algorithm according to the category when generating a design drawing for a specific prosthetic device. As a result, a more accurate design drawing is generated by applying a generating algorithm according to the category of the prosthetic device. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the category of the prosthetic device to the generating AI and cause the generating AI to apply the generating algorithm.

[0087] The generation unit can estimate the user's emotion and adjust the length of the blueprint based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a detailed blueprint. For example, the generation unit detects that the user is relaxed using an emotion estimation algorithm and generates a detailed blueprint. In addition, the generation unit can also generate a concise blueprint when the user is nervous. For example, the generation unit detects that the user is nervous using an emotion estimation algorithm and generates a concise blueprint. In addition, the generation unit can also generate a blueprint that covers the main points when the user is in a hurry. For example, the generation unit detects that the user is in a hurry using an emotion estimation algorithm and generates a blueprint that covers the main points. In this way, the length of the blueprint can be adjusted according to the user's emotion, thereby providing an optimal blueprint for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can have the generation AI run an emotion estimation algorithm and adjust the length of the blueprint based on the emotion.

[0088] The generation unit can determine the priority of the design drawing based on the acquisition time of the analysis result when generating the design drawing. The acquisition time includes, for example, the priority of the latest data and the importance of past data, but is not limited to such examples. The generation unit generates the design drawing based on, for example, the latest analysis result. For example, the generation unit generates the design drawing based on the latest analysis result. The generation unit can also generate the design drawing while referring to the past analysis result. For example, the generation unit generates the design drawing based on the past analysis result. The generation unit can also generate the design drawing based on the analysis result of a specific period. For example, the generation unit generates the design drawing based on the analysis result of a specific period. As a result, the priority of the design drawing can be determined based on the acquisition time of the analysis result, so that the latest data can be preferentially reflected in the design drawing. A part or all of the above-mentioned 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 acquisition time of the analysis result to the generation AI and cause the generation AI to execute the determination of the priority of the design drawing.

[0089] The generation unit can adjust the order of the blueprints based on the relevance of the analysis results when generating the blueprints. The relevance includes, for example, data correlation and relevance scoring, but is not limited to such examples. The generation unit generates the blueprints based on, for example, highly relevant analysis results. For example, the generation unit generates the blueprints based on highly relevant analysis results. The generation unit can also postpone the generation of less relevant analysis results. For example, the generation unit generates the blueprints based on less relevant analysis results. The generation unit can also adjust the order of the blueprints based on the relevance of a specific analysis result. For example, the generation unit adjusts the order of the blueprints based on the relevance of a specific analysis result. This enables efficient blueprint generation by adjusting the order of the blueprints based on the relevance of the analysis results. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of the blueprints.

[0090] The production unit can estimate the user's emotion and adjust the production method based on the estimated user's emotion. For example, when the user is relaxed, the production unit uses a detailed production procedure. For example, the production unit detects that the user is relaxed using an emotion estimation algorithm and uses a detailed production procedure. In addition, the production unit can also use a concise production procedure when the user is nervous. For example, the production unit detects that the user is nervous using an emotion estimation algorithm and uses a concise production procedure. In addition, the production unit can also use a quick production procedure when the user is in a hurry. For example, the production unit detects that the user is in a hurry using an emotion estimation algorithm and uses a quick production procedure. In this way, the production method can be adjusted according to the user's emotion, thereby providing an optimal production method for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the production unit may be performed using, for example, an AI, or may be performed without using an AI. For example, production teams can run emotion estimation algorithms on generative AI and adjust production methods based on emotions.

[0091] The production department can adjust the level of detail of the production based on the importance of the blueprint during production. The level of detail includes, for example, the accuracy of production and the granularity of the information to be displayed, but is not limited to such examples. The production department performs detailed production based on, for example, an important blueprint. For example, the production department performs detailed production based on an important blueprint. The production department can also perform simplified production based on a basic blueprint. For example, the production department performs simplified production based on a basic blueprint. The production department can also adjust the level of detail as necessary based on a specific blueprint. For example, the production department adjusts the level of detail based on a specific blueprint. This allows efficient production by adjusting the level of detail of production based on the importance of the blueprint. Part 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 importance of the blueprint to the generation AI and cause the generation AI to adjust the level of detail of production.

[0092] The production unit can apply different production methods depending on the category of the prosthetic orthotic during production. The categories include, for example, classification by type of prosthetic orthotic or by use, but are not limited to such examples. For example, the production unit applies a production method dedicated to prosthetic hands to prosthetic hands. For example, the production unit applies a production method dedicated to prosthetic hands when producing prosthetic hands. The production unit can also apply a production method dedicated to prosthetic legs to prosthetic legs. For example, the production unit applies a production method dedicated to prosthetic legs when producing prosthetic legs. The production unit can also apply a production method according to the category to a specific prosthetic orthotic. For example, the production unit applies a production method according to the category when producing a specific prosthetic orthotic. As a result, by applying a production method according to the category of the prosthetic orthotic, more accurate production is possible. Part or all of the above-mentioned 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 input the category of the prosthetic orthotic to the generation AI and cause the generation AI to apply the production method.

[0093] The production unit can estimate the user's emotion and determine the priority of production based on the estimated user's emotion. For example, when the user is relaxed, the production unit prioritizes detailed production. For example, the production unit detects that the user is relaxed using an emotion estimation algorithm and prioritizes detailed production. In addition, the production unit can also prioritize basic production when the user is nervous. For example, the production unit detects that the user is nervous using an emotion estimation algorithm and prioritizes basic production. In addition, the production unit can also prioritize quick production when the user is in a hurry. For example, the production unit detects that the user is in a hurry using an emotion estimation algorithm and prioritizes quick production. In this way, by determining the priority of production according to the user's emotion, it is possible to provide an optimal production order for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the production unit may be performed using, for example, an AI, or may be performed without using an AI. For example, production teams can run emotion estimation algorithms on generative AI to prioritize production based on emotions.

[0094] The production department can adjust the order of production based on the acquisition time of the blueprint during production. The acquisition time includes, for example, the priority of the latest data and the importance of past data, but is not limited to such examples. The production department, for example, prioritizes production based on the latest blueprint. For example, the production department prioritizes production based on the latest blueprint. The production department can also perform production while referring to past blueprints. For example, the production department performs production based on past blueprints. The production department can also adjust the order of production based on blueprints of a specific period. For example, the production department adjusts the order of production based on blueprints of a specific period. As a result, the latest blueprint can be produced preferentially by adjusting the order of production based on the acquisition time of the blueprint. Part 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 acquisition time of the blueprint to the generation AI and cause the generation AI to execute the adjustment of the order of production.

[0095] The production department can adjust the production procedure based on the relevance of the blueprints during production. The relevance includes, for example, data correlation and relevance scoring, but is not limited to such examples. For example, the production department prioritizes production based on highly relevant blueprints. For example, the production department prioritizes production based on highly relevant blueprints. The production department can also postpone production based on less relevant blueprints. For example, the production department postpones production based on less relevant blueprints. The production department can also adjust the production procedure based on the relevance of a specific blueprint. For example, the production department adjusts the production procedure based on the relevance of a specific blueprint. As a result, efficient production is possible by adjusting the production procedure based on the relevance of the blueprints. Part 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 relevance of the blueprints to the generation AI and cause the generation AI to execute the adjustment of the production procedure.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] When acquiring the user's physical data, the acquisition unit can also acquire the user's lifestyle data. For example, by acquiring data such as the user's diet, exercise habits, and sleep patterns, more comprehensive physical data can be collected. The acquisition unit can also reflect the user's lifestyle data in the design of the prosthetic device. For example, for a user who has an exercise habit, a prosthetic device suitable for exercise can be designed. Furthermore, the acquisition unit can provide the user's lifestyle data to the analysis unit, and the analysis unit can derive design parameters for the optimal prosthetic device based on this data. This makes it possible to provide a prosthetic device that matches the user's lifestyle.

[0098] The analysis unit can take the user's psychological state into account when analyzing the user's physical data. For example, if the user is feeling stressed, the analysis unit can suggest a design for a prosthetic device that helps reduce stress. The analysis unit can also adjust the design and function of the prosthetic device based on the user's psychological state. For example, materials and designs that have a relaxing effect can be adopted. Furthermore, the analysis unit can continuously monitor the user's psychological state and update the design of the prosthetic device as necessary. This makes it possible to provide a prosthetic device that takes into account the user's psychological state.

[0099] The generation unit can reflect the user's hobbies and preferences when generating a design drawing for a prosthetic orthosis. For example, if a user likes a particular sport, the generation unit can design a prosthetic orthosis suitable for that sport. The generation unit can also suggest a prosthetic orthosis incorporating the user's favorite color or design. Furthermore, the generation unit can provide customization options for the prosthetic orthosis based on the user's hobbies and preferences. For example, if a user is interested in art, art works can be incorporated into the prosthetic orthosis. This makes it possible to provide a prosthetic orthosis that matches the user's personality and hobbies.

[0100] The generation unit can use environmentally friendly materials when generating a design drawing for a prosthetic orthotic. For example, by using renewable or recycled materials, the environmental impact can be reduced. The generation unit can also adopt an environmentally friendly manufacturing process. For example, an energy-efficient manufacturing method or a low-emission manufacturing technique can be used. Furthermore, the generation unit can provide the user with options for environmentally friendly prosthetic orthotics. This makes it possible to provide an environmentally friendly prosthetic orthotic.

[0101] The generation unit can take into account the social background of the user when generating a design drawing of the prosthetic orthotic. For example, if the user belongs to a particular culture or religion, designs and functions suitable for the culture or religion can be incorporated. The generation unit can also provide customization options for the prosthetic orthotic based on the social background of the user. For example, specific cultural symbols and designs can be incorporated into the prosthetic orthotic. Furthermore, the generation unit can increase the social acceptability of the user by designing the prosthetic orthotic taking into account the social background of the user. This makes it possible to provide a prosthetic orthotic that takes into account the social background of the user.

[0102] The production department can reflect the user's feedback in real time when producing the prosthetics and orthotics. For example, by immediately reflecting any defects or improvements that the user feels during production, it is possible to provide a more accurate prosthetics and orthotics. The production department can also optimize the production process based on the user's feedback. For example, the production department can analyze the user's feedback and adjust the production procedure and materials used. Furthermore, the production department can continuously collect the user's feedback and use it to improve the production process. This makes it possible to provide a prosthetics and orthotics that reflects the user's feedback.

[0103] The production unit can estimate the user's emotions when producing a prosthetic device, and adjust the speed of production based on the estimated emotions. For example, if the user is relaxed, production can proceed at a normal speed. If the user is nervous, the progress of production can be temporarily delayed. Furthermore, if the user is in a hurry, the progress of production can be expedited. This makes it possible to provide a prosthetic device at a production speed that corresponds to the user's emotions.

[0104] The production department can provide an adjustment function for responding to changes in the user's body when producing the prosthetics. For example, the size and shape of the prosthetics can be adjusted according to changes in the user's weight and muscle mass. The production department can also monitor changes in the user's body and adjust the prosthetics as necessary. Furthermore, the production department can provide customization options for responding to changes in the user's body. This makes it possible to provide prosthetics that respond to changes in the user's body.

[0105] When manufacturing the prosthetics and orthotics, the production department can add functions to adapt the prosthetics and orthotics to the user's living environment. For example, a prosthetics and orthotics with a heat retention function can be provided to a user living in a cold region. Also, a prosthetics and orthotics with a waterproof function can be provided to a user living in a humid region. Furthermore, the production department can provide customization options according to the user's living environment. In this way, a prosthetics and orthotics adapted to the user's living environment can be provided.

[0106] The production department can estimate the user's emotions when producing prosthetics and adjust the quality of production based on the estimated emotions. For example, if the user is relaxed, production can be performed with normal quality. Also, if the user is nervous, additional inspections and adjustments can be performed to improve quality. Furthermore, if the user is in a hurry, production can be performed quickly while maintaining quality. In this way, prosthetics and orthotics can be provided with quality according to the user's emotions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The acquisition unit acquires the user's physical data. The user's physical data includes, for example, height, weight, body fat percentage, muscle mass, etc. The acquisition unit acquires detailed shape data of the user's body using a 3D scanner, acquires the user's skin color using a camera, and measures the user's muscle mass and body fat percentage using a sensor. Step 2: The analysis unit analyzes the data acquired by the acquisition unit. The analysis unit analyzes the data using a machine learning algorithm and determines the shape and size of the prosthetic device that is optimal for the user. The analysis unit performs statistical analysis of the data and derives design parameters for the optimal prosthetic device based on the user's physical data. Step 3: The generator generates a design drawing of the prosthetic orthotic based on the analysis results obtained by the analyzer. The generator generates detailed design drawings and 3D models of the prosthetic orthotic using CAD software or 3D modeling software, and can also generate skin that matches the user's skin color.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and production unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the user's body data using the camera 42 or a sensor of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a design drawing of the prosthetic device based on the analysis result. The production unit is realized, for example, by the control unit 46A of the smart device 14 and produces the prosthetic device based on the generated design drawing. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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, the RAM 30, and the 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and production unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires the user's body data using the camera 42 or a sensor of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a design drawing of the prosthetic device based on the analysis result. The production unit is realized, for example, by the control unit 46A of the smart glasses 214 and produces the prosthetic device based on the generated design drawing. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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, the RAM 30, and the 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned acquisition unit, analysis 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 acquisition unit acquires the user's body data using the camera 42 or a sensor of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a design drawing of the prosthetic orthotic based on the analysis result. The production unit is realized, for example, by the control unit 46A of the headset type terminal 314 and produces the prosthetic orthotic based on the generated design drawing. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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, the RAM 30, and the 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0161] Each of the multiple elements including the above-mentioned acquisition unit, analysis 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 acquisition unit acquires the user's body data using the camera 42 and sensors of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a design drawing of the prosthetic device based on the analysis result. The production unit is realized, for example, by the control unit 46A of the robot 414 and produces the prosthetic device based on the generated design drawing. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) An acquisition unit that acquires physiological data of a user; an analysis unit that analyzes the data acquired by the acquisition unit; A generation unit that generates a design drawing of a prosthetic device based on the analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) The acquisition unit is Acquire user's physical data with high accuracy 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the shape and size of prosthetics and orthotics suitable for each user based on the acquired data. 2. The system of claim 1, (Appendix 4) The generation unit is Generate prosthetic and orthotic designs based on analysis results 2. The system of claim 1, (Appendix 5) The generation unit is Generate a skin based on the user's skin color 2. The system of claim 1, (Appendix 6) A manufacturing department is provided to manufacture prosthetic devices based on the generated design drawings. 2. The system of claim 1, (Appendix 7) The acquisition unit is To estimate a user's emotion and adjust timing of acquiring physical data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The acquisition unit is Analyze the user's past physical data acquisition history and select the appropriate acquisition method 2. The system of claim 1, (Appendix 9) The acquisition unit is When collecting physical data, filter the data based on the user's current health and activity level. 2. The system of claim 1, (Appendix 10) The acquisition unit is A user's emotion is estimated, and a priority order of body data to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The acquisition unit is When acquiring physical data, the system prioritizes acquiring data that is more relevant to the user based on the user's geographic location. 2. The system of claim 1, (Appendix 12) The acquisition unit is When collecting body data, the user's social media activity is analyzed to obtain relevant data. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the body data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of body data. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the physical data was acquired 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of physical data 2. The system of claim 1, (Appendix 19) The generation unit is Inferring user emotions and adjusting the method of generating blueprints based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The generation unit is When generating designs, adjust the level of detail in the designs based on the importance of the analysis results 2. The system of claim 1, (Appendix 21) The generation unit is When generating blueprints, different generation algorithms are applied depending on the category of prosthetic orthotic. 2. The system of claim 1, (Appendix 22) The generation unit is Inferring user emotion and adjusting the length of the blueprint based on the estimated user emotion 2. The system of claim 1, (Appendix 23) The generation unit is During design generation, prioritize designs based on when analysis results are available 2. The system of claim 1, (Appendix 24) The generation unit is During design generation, adjust the order of designs based on the relevance of analysis results 2. The system of claim 1, (Appendix 25) The manufacturing department: Inferring a user's emotions and adjusting the production method based on the estimated user's emotions 3. The system of claim 2, (Appendix 26) The manufacturing department: When crafting, adjust the crafting detail level based on the importance of the blueprint 3. The system of claim 2, (Appendix 27) The manufacturing department: When crafting, different crafting methods are applied depending on the category of prosthetic device. 3. The system of claim 2, (Appendix 28) The manufacturing department: To estimate a user's emotions and determine production priorities based on the estimated user's emotions. 3. The system of claim 2, (Appendix 29) The manufacturing department: When crafting, adjust the order of crafting based on when the blueprints were acquired 3. The system of claim 2, (Appendix 30) The manufacturing department: When crafting, adjust crafting steps based on blueprint relationships 3. The system of claim 2, [Explanation of symbols]

[0181] 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 acquisition unit that acquires physiological data of a user; an analysis unit that analyzes the data acquired by the acquisition unit; A generation unit that generates a design drawing of a prosthetic device based on the analysis result obtained by the analysis unit. A system characterized by:

2. The acquisition unit is Acquire user's physical data with high accuracy 2. The system of claim 1 .

3. The analysis unit is Analyze the shape and size of the prosthetic device that best suits the user based on the acquired data 2. The system of claim 1 .

4. The generation unit is Generate a skin based on the user's skin color 2. The system of claim 1 .

5. A manufacturing department is provided to manufacture prosthetic devices based on the generated design drawings.

2. The system of claim 1 .

6. The acquisition unit is To estimate a user's emotion and adjust timing of acquiring physical data based on the estimated user's emotion.

2. The system of claim 1 .

7. The acquisition unit is Analyze the user's past physical data acquisition history and select the appropriate acquisition method 2. The system of claim 1 .

8. The acquisition unit is When collecting physical data, filter the data based on the user's current health and activity level.

2. The system of claim 1 .

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