system

Abstract

An object of a system according to an embodiment is to generate and provide a design of an accessory according to a user's request.SOLUTION: A system includes a reception part, a first generation part, a second generation part, and a provision part. The receiving unit receives a design request. The first generating section interprets the request received by the receiving section, and generates an accessory design based on the patient data and the treatment period information. The second generating part generates 3DCAD information based on the design generated by the first generating part. The providing section provides the 3DCAD datum generated by the second generating section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to create dedicated accessories that matched the assistive device, making it difficult to provide designs that met the needs of individual users.

[0005] The system according to the embodiment aims to generate and provide designs of accessories that meet the needs of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a first generating unit, a second generating unit, and a providing unit. The receiving unit receives a design request. The first generating unit interprets the request received by the receiving unit and generates an accessory design based on patient data and treatment period information. The second generating unit generates 3D CAD data based on the design generated by the first generating unit. The providing unit provides the 3D CAD data generated by the second generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide designs of accessories according to the user's requests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention generates accessory designs and 3D CAD data tailored to users for assistive devices designed to overcome disabilities. In this system, a user inputs design requirements. A large-scale language model (LLM) interprets the requirements and generates an optimal accessory design for the user based on information such as patient data and treatment duration. The system then generates 3D CAD data based on the generated design and provides it to the user. This allows the assistive device to be enjoyed and worn. For example, a user inputs a design requirement, such as "I want a cover to be attached to the handle of a wheelchair." This requirement is input into the LLM. The LLM then interprets the input requirement. The LLM understands the user's requirements and generates an optimal accessory design based on information such as patient data and treatment duration. For example, a handle cover design tailored to the patient's hand size and grip strength is generated. 3D CAD data is generated based on the generated design. The LLM then generates CAD data suitable for 3D printing based on the generated design. For example, CAD data detailing the shape and dimensions of the handle cover is generated. Finally, the generated 3D CAD data is provided to the user. The user can then use this data to manufacture the accessory using a 3D printer. This allows users to easily create accessories that suit their needs. This allows the system to make wearing assistive devices more enjoyable. Users can create accessories with designs that suit their preferences, making assistive devices more unique and attractive. For example, colorful wheelchair handle covers can make using a wheelchair more enjoyable. Functionality can also be improved through designs based on patient data and treatment duration. For example, handle covers with an easy-to-grip shape can be provided for patients with weak grip strength.

[0029] A design generation system according to an embodiment includes a receiving unit, a first generating unit, a second generating unit, and a providing unit. The receiving unit receives a user's design request. For example, a user may input a request such as, "I want to create a cover to attach to the handle of a wheelchair." The receiving unit can receive requests in various formats, such as text, image, and audio. The first generating unit uses an LLM to interpret the request received by the receiving unit and generate an accessory design based on patient data and treatment period information. For example, the LLM generates a handle cover design tailored to the patient's hand size and grip strength. The LLM uses natural language processing technology to understand the user's request and generate an optimal design. The second generating unit generates 3D CAD data based on the design generated by the first generating unit. For example, the LLM generates CAD data that can be manufactured using a 3D printer based on the generated design. The LLM generates CAD data that details the shape and dimensions of the design. The providing unit provides the 3D CAD data generated by the second generating unit to a user. For example, the user can manufacture an accessory using a 3D printer based on this data. The providing unit can provide the generated 3D CAD data online or on a physical medium, which allows the design generation system according to the embodiment to generate and provide accessory designs and 3D CAD data that are tailored to the user.

[0030] The reception unit can receive the user's design requests. For example, the user can input a request such as "I want to make a cover to attach to the handle of a wheelchair." The reception unit can receive requests in various formats, such as text, image, and audio. For example, if the user inputs the request in text format, the reception unit can receive the text. Also, if the user inputs the request in image format, the reception unit can receive the image. Furthermore, if the user inputs the request in audio format, the reception unit can receive the audio. This allows the user's design requests to be received appropriately.

[0031] The first generation unit can generate an accessory design based on patient data and treatment period information. The first generation unit generates, for example, a steering wheel cover design that matches the patient's hand size and grip strength. The first generation unit generates an optimal design based on the patient data and treatment period information. For example, a steering wheel cover with an easy-to-grip shape can be designed based on the patient's hand size. The first generation unit can also design a steering wheel cover with appropriate hardness and material based on the patient's grip strength. Furthermore, the first generation unit can generate a durable design that can be used for a long period of time, taking into account the treatment period information. This makes it possible to generate an optimal design based on the patient data and treatment period information.

[0032] The second generation unit can generate 3D CAD data based on the generated design. For example, the second generation unit generates CAD data that can be manufactured using a 3D printer based on the generated design. The second generation unit generates CAD data that describes in detail the shape and dimensions of the design. For example, it can generate CAD data that describes in detail the shape and dimensions of a steering wheel cover. The second generation unit can also generate CAD data in an optimal file format for manufacturing using a 3D printer. For example, it can generate CAD data in a file format commonly used by 3D printers, such as STL format or OBJ format. Furthermore, the second generation unit can generate CAD data for multiple variations based on the generated design. For example, it can generate CAD data for steering wheel covers of different sizes and shapes. This allows 3D CAD data to be generated based on the generated design.

[0033] The providing unit can provide the generated 3D CAD data to a user. For example, the providing unit can provide the generated 3D CAD data online. The providing unit can upload the generated 3D CAD data to cloud storage so that the user can download it. The providing unit can also provide the 3D CAD data on a physical medium. For example, the 3D CAD data can be stored on a USB memory stick or a CD-ROM and provided to the user. Furthermore, the providing unit can send the generated 3D CAD data by email. In this way, the generated 3D CAD data can be provided to the user.

[0034] The first generation unit can generate a design based on the patient's hand size and grip strength. For example, the first generation unit can design a steering wheel cover with an easy-to-grip shape based on the patient's hand size. The first generation unit generates a design with an optimal shape based on data such as the length and width of the patient's hand. The first generation unit can also design a steering wheel cover with an appropriate hardness and material based on the patient's grip strength. For example, a steering wheel cover made of a soft material can be designed for a patient with weak grip strength. Furthermore, the first generation unit can generate multiple variations of designs based on the patient's hand size and grip strength. This makes it possible to generate a design that matches the patient's hand size and grip strength.

[0035] The reception unit can analyze the user's past history of design requests and select an appropriate reception method. For example, the reception unit prioritizes and suggests reception methods (voice, text, etc.) that the user has frequently used in the past. The reception unit analyzes the user's past history of design requests and selects the optimal reception method. For example, if the user has previously input design requests by voice, the reception unit can prioritize and suggest voice input. Also, if the user has previously input design requests by text, the reception unit can prioritize and suggest text input. Furthermore, the reception unit can also accept requests during a specific time period based on the user's past history of design requests. For example, if the user has previously input design requests during a specific time period, the reception unit can suggest and accept requests during that time period. In this way, the user's past history of design requests can be analyzed and the optimal reception method can be selected.

[0036] When receiving design requests, the receiving unit can filter the requests based on the user's current project and areas of interest. For example, the receiving unit can preferentially receive design requests related to the user's current project. The receiving unit filters the design requests based on the user's current project and areas of interest. For example, the receiving unit can preferentially receive design requests related to the user's current project. The receiving unit can also filter related design requests based on the user's areas of interest. For example, if the user inputs a design request related to a specific area of ​​interest, the receiving unit can preferentially receive the request. Furthermore, the receiving unit can also receive highly relevant design requests by referring to the user's past project history. This makes it possible to filter design requests based on the user's current project and areas of interest.

[0037] When accepting a design request, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user inputs the design request by voice, the acceptance unit accepts the request using voice recognition technology. The acceptance unit selects the optimal acceptance means depending on the user's input method (voice, text, image, etc.). For example, if the user inputs the design request by voice, the acceptance unit accepts the request using voice recognition technology. Also, if the user inputs the design request as text, the acceptance unit can accept the request using text analysis technology. Furthermore, if the user inputs the design request as an image, the acceptance unit can also accept the request using image recognition technology. This makes it possible to select the optimal acceptance means depending on the user's input method.

[0038] When accepting design requests, the acceptance unit can prioritize accepting highly relevant requests in consideration of the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting design requests related to that area. The acceptance unit can prioritize accepting highly relevant requests in consideration of the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting design requests related to that area. It can also prioritize accepting design requests related to locations close to the user's current location. Furthermore, it can also prioritize accepting highly relevant design requests by referring to the user's past location information. This makes it possible to prioritize accepting highly relevant requests in consideration of the user's geographical location information.

[0039] When receiving a design request, the reception unit can analyze the user's social media activity and receive related requests. For example, the reception unit can prioritize receiving design requests shared by the user on social media. The reception unit analyzes the user's social media activity and receive related requests. For example, the reception unit can prioritize receiving design requests shared by the user on social media. The reception unit can also analyze the user's social media activity and receive related design requests. Furthermore, the reception unit can refer to the activity of the user's friends on social media to receive related design requests. In this way, the user's social media activity can be analyzed and related requests can be received.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a design request. The reception unit, for example, proposes the optimal reception method based on feedback provided by the user in the past. The reception unit customizes the reception method by reflecting the user's past feedback. For example, the reception unit can propose the optimal reception method based on feedback provided by the user in the past. The reception interface can also be customized by reflecting the user's past feedback. Furthermore, the reception procedure can be optimized by referring to the user's past feedback. In this way, the reception method can be customized by reflecting the user's past feedback.

[0041] When generating a design, the first generation unit can adjust the level of detail of the design based on the level of detail of the patient data. For example, if the patient data is detailed, the first generation unit generates a design that is precise down to the smallest detail. The first generation unit adjusts the level of detail of the design based on the level of detail of the patient data. For example, if the patient data is detailed, the first generation unit can generate a design that is precise down to the smallest detail. Also, if the patient data is simple, the first generation unit can generate a simple design. Furthermore, if part of the patient data is missing, the first generation unit can also complete the design based on an estimate. This makes it possible to adjust the level of detail of the design based on the level of detail of the patient data.

[0042] The first generation unit can apply different design algorithms depending on the category of the accessory when generating a design. For example, in the case of a wheelchair accessory, the first generation unit applies a design algorithm that emphasizes durability. The first generation unit applies different design algorithms depending on the category of the accessory. For example, in the case of a wheelchair accessory, the first generation unit can apply a design algorithm that emphasizes durability. In addition, in the case of a white cane accessory, the first generation unit can apply a design algorithm that emphasizes lightness. Furthermore, in the case of a cast accessory, the first generation unit can also apply a design algorithm that emphasizes comfort. In this way, different design algorithms can be applied depending on the category of the accessory.

[0043] When generating a design, the first generation unit can improve the accuracy of the design by referring to the user's past design results. The first generation unit, for example, generates a new design by referring to designs created by the user in the past. The first generation unit improves the accuracy of the design by referring to the user's past design results. For example, the first generation unit can generate a new design by referring to designs created by the user in the past. In addition, the first generation unit can extract a preferred style from the user's past design results and reflect it in the new design. Furthermore, the first generation unit can analyze the user's past design results and generate a new design by reflecting improvements. In this way, the first generation unit can improve the accuracy of the design by referring to the user's past design results.

[0044] When generating a design, the first generation unit can determine the priority of the design based on the time of submission of the patient data. For example, if the patient data is submitted early, the first generation unit generates the design with priority. The first generation unit determines the priority of the design based on the time of submission of the patient data. For example, if the patient data is submitted early, the first generation unit can generate the design with priority. Furthermore, if the patient data is submitted late, the first generation unit can generate the design at a later date. Furthermore, the priority of the design can be dynamically adjusted based on the time of submission of the patient data. This makes it possible to determine the priority of the design based on the time of submission of the patient data.

[0045] The first generation unit can adjust the order of designs based on the relevance of patient data when generating designs. For example, if the patient data is highly relevant, the first generation unit generates designs with priority. The first generation unit adjusts the order of designs based on the relevance of patient data. For example, if the patient data is highly relevant, the first generation unit can generate designs with priority. Furthermore, if the patient data is less relevant, the first generation unit can generate designs at a later date. Furthermore, the order of designs can also be dynamically adjusted based on the relevance of patient data. This makes it possible to adjust the order of designs based on the relevance of patient data.

[0046] The first generation unit can adjust the use of technical terminology in the design according to the user's level of expertise when generating the design. For example, if the user has technical expertise, the first generation unit generates a design that makes extensive use of technical terminology. The first generation unit adjusts the use of technical terminology in the design according to the user's level of expertise. For example, if the user has technical expertise, the first generation unit can generate a design that makes extensive use of technical terminology. Furthermore, if the user does not have technical expertise, the first generation unit can generate a design that is explained in simple terms. Furthermore, the use of technical terminology in the design can also be dynamically adjusted based on the user's level of expertise. This makes it possible to adjust the use of technical terminology in the design according to the user's level of expertise.

[0047] When providing 3D CAD data, the providing unit can select the optimal providing method by referring to the user's past data usage history. For example, the providing unit preferentially suggests a providing method (email, cloud storage, etc.) that the user has used in the past. The providing unit selects the optimal providing method by referring to the user's past data usage history. For example, if the user previously received 3D CAD data by email, the providing unit can preferentially suggest providing the data by email. Also, if the user previously received 3D CAD data via cloud storage, the providing unit can preferentially suggest providing the data via cloud storage. Furthermore, the providing unit can also provide the data at a specific time period based on the user's past data usage history. For example, if the user previously used 3D CAD data during a specific time period, the providing unit can suggest providing the data during that time period. In this way, the optimal providing method can be selected by referring to the user's past data usage history.

[0048] When providing 3D CAD data, the providing unit can customize the provided content according to the user's current project. For example, the providing unit can provide 3D CAD data related to the user's current project with priority. The providing unit customizes the provided content according to the user's current project. For example, the providing unit can provide 3D CAD data related to the user's current project with priority. The provided content can also be customized according to the progress of the user's project. Furthermore, the most suitable 3D CAD data can be provided based on the type of the user's project. This makes it possible to customize the provided content according to the user's current project.

[0049] The providing unit can improve the providing method by reflecting user feedback when providing 3D CAD data. For example, the providing unit proposes an optimal providing method based on feedback previously provided by the user. The providing unit improves the providing method by reflecting user feedback. For example, the providing unit can propose an optimal providing method based on feedback previously provided by the user. The providing unit can also customize the providing interface by reflecting user feedback. Furthermore, the providing procedure can be optimized by referring to user feedback. In this way, the providing method can be improved by reflecting user feedback.

[0050] When providing 3D CAD data, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the providing unit can preferentially provide 3D CAD data related to that area. The providing unit selects the optimal providing method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the providing unit can preferentially provide 3D CAD data related to that area. It can also preferentially provide 3D CAD data related to locations close to the user's current location. Furthermore, it can also preferentially provide highly relevant 3D CAD data by referring to the user's past location information. This makes it possible to select the optimal providing method by taking into consideration the user's geographical location information.

[0051] When providing 3D CAD data, the providing unit can analyze the user's social media activity and suggest content to be provided. For example, the providing unit provides 3D CAD data based on design requests shared by the user on social media. The providing unit analyzes the user's social media activity and provides related 3D CAD data. For example, the providing unit can provide 3D CAD data based on design requests shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related 3D CAD data. Furthermore, the providing unit can provide related 3D CAD data by referring to the activity of the user's friends on social media. In this way, the providing unit can analyze the user's social media activity and suggest content to be provided.

[0052] The providing unit can customize the providing method by reflecting the user's past feedback when providing 3D CAD data. The providing unit, for example, proposes an optimal providing method based on feedback provided by the user in the past. The providing unit customizes the providing method by reflecting the user's past feedback. For example, the providing unit can propose an optimal providing method based on feedback provided by the user in the past. The providing unit can also customize the providing interface by reflecting the user's past feedback. Furthermore, the providing procedure can be optimized by referring to the user's past feedback. In this way, the providing method can be customized by reflecting the user's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When accepting a user's design request, the reception unit can refer to the user's past design history and automatically suggest similar design requests. For example, if the user has previously designed a wheelchair handle cover, the reception unit can suggest a similar design request based on that design history. Also, if the user has previously preferred a particular color or material, the reception unit can suggest a design request that is suitable for the user based on that information. Furthermore, the reception unit can analyze the user's past design history, estimate the user's preferred design style, and suggest a design request based on that style. This allows the user to input design requests more efficiently by utilizing their past design history.

[0055] When generating a design, the first generation unit can adjust the functionality of the design based on the user's living environment and usage situation. For example, if the user frequently uses the product outdoors, the first generation unit can generate a design that uses highly durable materials and waterproof functionality. If the user uses the product indoors, the first generation unit can also generate a design that emphasizes comfort. Furthermore, if the user uses the product while performing a specific activity, the first generation unit can also generate a design that is suitable for that activity. This makes it possible to provide an optimal design that suits the user's living environment and usage situation.

[0056] When providing the generated 3D CAD data to a user, the providing unit can select the optimal providing method by referring to the user's past data usage history. For example, if the user previously received 3D CAD data by email, the providing unit can preferentially suggest providing the data by email. Also, if the user previously received 3D CAD data via cloud storage, the providing unit can preferentially suggest providing the data via cloud storage. Furthermore, the providing unit can provide the data at a specific time period based on the user's past data usage history. This makes it possible to select the optimal providing method by referring to the user's past data usage history.

[0057] The first generation unit can adjust the use of technical terminology in the design according to the user's level of expertise when generating the design. For example, if the user has technical expertise, the first generation unit can generate a design that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the first generation unit can generate a design that is explained in simple terms. Furthermore, the first generation unit can dynamically adjust the use of technical terminology in the design based on the user's level of expertise. This makes it possible to provide an optimal design according to the user's level of expertise.

[0058] When generating a design, the first generation unit can improve the accuracy of the design by referring to the user's past design results. For example, a new design can be generated by referring to designs created by the user in the past. Also, a preferred style can be extracted from the user's past design results and reflected in the new design. Furthermore, the first generation unit can analyze the user's past design results and generate a new design by reflecting improvements. In this way, the accuracy of the design can be improved by referring to the user's past design results.

[0059] When generating a design, the first generation unit can apply different design algorithms depending on the category of the accessory. For example, in the case of a wheelchair accessory, the first generation unit can apply a design algorithm that emphasizes durability. In addition, in the case of a white cane accessory, the first generation unit can apply a design algorithm that emphasizes lightness. Furthermore, in the case of a cast accessory, the first generation unit can apply a design algorithm that emphasizes comfort. This makes it possible to provide an optimal design depending on the accessory category.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The user inputs their design requests into the reception unit. For example, a user can input a request such as, "I want to create a cover to attach to the handle of a wheelchair." The reception unit can accept requests in various formats, including text, image, and audio. Step 2: The first generation unit uses LLM to interpret the request received by the reception unit and generate an accessory design based on patient data and treatment period information. For example, LLM generates a steering wheel cover design tailored to the patient's hand size and grip strength. LLM uses natural language processing technology to understand the user's request and generate the optimal design. Step 3: The second generation unit generates 3D CAD data based on the design generated by the first generation unit. For example, the LLM generates CAD data that can be manufactured using a 3D printer based on the generated design. The LLM generates CAD data that describes in detail the shape and dimensions of the design. Step 4: The providing unit provides the 3D CAD data generated by the second generating unit to the user. For example, the user can manufacture an accessory using a 3D printer based on this data. The providing unit can provide the generated 3D CAD data online or on a physical medium.

[0062] (Example 2) A system according to an embodiment of the present invention generates accessory designs and 3D CAD data tailored to users for assistive devices designed to overcome disabilities. In this system, a user inputs design requirements. A large-scale language model (LLM) interprets the requirements and generates an optimal accessory design for the user based on information such as patient data and treatment duration. The system then generates 3D CAD data based on the generated design and provides it to the user. This allows the assistive device to be enjoyed and worn. For example, a user inputs a design requirement, such as "I want a cover to be attached to the handle of a wheelchair." This requirement is input into the LLM. The LLM then interprets the input requirement. The LLM understands the user's requirements and generates an optimal accessory design based on information such as patient data and treatment duration. For example, a handle cover design tailored to the patient's hand size and grip strength is generated. 3D CAD data is generated based on the generated design. The LLM then generates CAD data suitable for 3D printing based on the generated design. For example, CAD data detailing the shape and dimensions of the handle cover is generated. Finally, the generated 3D CAD data is provided to the user. The user can then use this data to manufacture the accessory using a 3D printer. This allows users to easily create accessories that suit their needs. This allows the system to make wearing assistive devices more enjoyable. Users can create accessories with designs that suit their preferences, making assistive devices more unique and attractive. For example, colorful wheelchair handle covers can make using a wheelchair more enjoyable. Functionality can also be improved through designs based on patient data and treatment duration. For example, handle covers with an easy-to-grip shape can be provided for patients with weak grip strength.

[0063] A design generation system according to an embodiment includes a receiving unit, a first generating unit, a second generating unit, and a providing unit. The receiving unit receives a user's design request. For example, a user may input a request such as, "I want to create a cover to attach to the handle of a wheelchair." The receiving unit can receive requests in various formats, such as text, image, and audio. The first generating unit uses an LLM to interpret the request received by the receiving unit and generate an accessory design based on patient data and treatment period information. For example, the LLM generates a handle cover design tailored to the patient's hand size and grip strength. The LLM uses natural language processing technology to understand the user's request and generate an optimal design. The second generating unit generates 3D CAD data based on the design generated by the first generating unit. For example, the LLM generates CAD data that can be manufactured using a 3D printer based on the generated design. The LLM generates CAD data that details the shape and dimensions of the design. The providing unit provides the 3D CAD data generated by the second generating unit to a user. For example, the user can manufacture an accessory using a 3D printer based on this data. The providing unit can provide the generated 3D CAD data online or on a physical medium, which allows the design generation system according to the embodiment to generate and provide accessory designs and 3D CAD data that are tailored to the user.

[0064] The reception unit can receive the user's design requests. For example, the user can input a request such as "I want to make a cover to attach to the handle of a wheelchair." The reception unit can receive requests in various formats, such as text, image, and audio. For example, if the user inputs the request in text format, the reception unit can receive the text. Also, if the user inputs the request in image format, the reception unit can receive the image. Furthermore, if the user inputs the request in audio format, the reception unit can receive the audio. This allows the user's design requests to be received appropriately.

[0065] The first generation unit can generate an accessory design based on patient data and treatment period information. The first generation unit generates, for example, a steering wheel cover design that matches the patient's hand size and grip strength. The first generation unit generates an optimal design based on the patient data and treatment period information. For example, a steering wheel cover with an easy-to-grip shape can be designed based on the patient's hand size. The first generation unit can also design a steering wheel cover with appropriate hardness and material based on the patient's grip strength. Furthermore, the first generation unit can generate a durable design that can be used for a long period of time, taking into account the treatment period information. This makes it possible to generate an optimal design based on the patient data and treatment period information.

[0066] The second generation unit can generate 3D CAD data based on the generated design. For example, the second generation unit generates CAD data that can be manufactured using a 3D printer based on the generated design. The second generation unit generates CAD data that describes in detail the shape and dimensions of the design. For example, it can generate CAD data that describes in detail the shape and dimensions of a steering wheel cover. The second generation unit can also generate CAD data in an optimal file format for manufacturing using a 3D printer. For example, it can generate CAD data in a file format commonly used by 3D printers, such as STL format or OBJ format. Furthermore, the second generation unit can generate CAD data for multiple variations based on the generated design. For example, it can generate CAD data for steering wheel covers of different sizes and shapes. This allows 3D CAD data to be generated based on the generated design.

[0067] The providing unit can provide the generated 3D CAD data to a user. For example, the providing unit can provide the generated 3D CAD data online. The providing unit can upload the generated 3D CAD data to cloud storage so that the user can download it. The providing unit can also provide the 3D CAD data on a physical medium. For example, the 3D CAD data can be stored on a USB memory stick or a CD-ROM and provided to the user. Furthermore, the providing unit can send the generated 3D CAD data by email. In this way, the generated 3D CAD data can be provided to the user.

[0068] The first generation unit can generate a design based on the patient's hand size and grip strength. For example, the first generation unit can design a steering wheel cover with an easy-to-grip shape based on the patient's hand size. The first generation unit generates a design with an optimal shape based on data such as the length and width of the patient's hand. The first generation unit can also design a steering wheel cover with an appropriate hardness and material based on the patient's grip strength. For example, a steering wheel cover made of a soft material can be designed for a patient with weak grip strength. Furthermore, the first generation unit can generate multiple variations of designs based on the patient's hand size and grip strength. This makes it possible to generate a design that matches the patient's hand size and grip strength.

[0069] The reception unit can estimate the user's emotions and adjust the timing of receiving design requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can receive design requests during times when the user is able to relax. The reception unit can analyze the user's facial expressions and voice to estimate emotions. For example, facial expression recognition technology can be used to estimate emotions from the user's facial expressions. Voice analysis technology can also be used to estimate emotions from the tone and speed of the user's voice. Furthermore, if the user is excited, the reception unit can immediately receive design requests, allowing ideas to be collected while they are fresh. For example, if the user is excited, the reception unit can immediately receive design requests. Also, if the user is tired, the reception unit can receive design requests after the user has rested. This makes it possible to adjust the timing of receiving design requests according to the user's emotions.

[0070] The reception unit can analyze the user's past history of design requests and select an appropriate reception method. For example, the reception unit prioritizes and suggests reception methods (voice, text, etc.) that the user has frequently used in the past. The reception unit analyzes the user's past history of design requests and selects the optimal reception method. For example, if the user has previously input design requests by voice, the reception unit can prioritize and suggest voice input. Also, if the user has previously input design requests by text, the reception unit can prioritize and suggest text input. Furthermore, the reception unit can also accept requests during a specific time period based on the user's past history of design requests. For example, if the user has previously input design requests during a specific time period, the reception unit can suggest and accept requests during that time period. In this way, the user's past history of design requests can be analyzed and the optimal reception method can be selected.

[0071] When receiving design requests, the receiving unit can filter the requests based on the user's current project and areas of interest. For example, the receiving unit can preferentially receive design requests related to the user's current project. The receiving unit filters the design requests based on the user's current project and areas of interest. For example, the receiving unit can preferentially receive design requests related to the user's current project. The receiving unit can also filter related design requests based on the user's areas of interest. For example, if the user inputs a design request related to a specific area of ​​interest, the receiving unit can preferentially receive the request. Furthermore, the receiving unit can also receive highly relevant design requests by referring to the user's past project history. This makes it possible to filter design requests based on the user's current project and areas of interest.

[0072] When accepting a design request, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user inputs the design request by voice, the acceptance unit accepts the request using voice recognition technology. The acceptance unit selects the optimal acceptance means depending on the user's input method (voice, text, image, etc.). For example, if the user inputs the design request by voice, the acceptance unit accepts the request using voice recognition technology. Also, if the user inputs the design request as text, the acceptance unit can accept the request using text analysis technology. Furthermore, if the user inputs the design request as an image, the acceptance unit can also accept the request using image recognition technology. This makes it possible to select the optimal acceptance means depending on the user's input method.

[0073] The reception unit can estimate the user's emotions and determine the priority of the design requests to be received based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit prioritizes receiving design requests with a high level of urgency. The reception unit can analyze the user's facial expressions and voice to estimate the emotions. For example, facial expression recognition technology can be used to estimate the emotions from the user's facial expressions. Also, voice analysis technology can be used to estimate the emotions from the tone and speed of the user's voice. Furthermore, when the user is relaxed, the reception unit can accept design requests with a normal priority. For example, when the user is relaxed, the reception unit can accept design requests with a normal priority. Also, when the user is excited, it can prioritize receiving design requests with a high level of creativity. In this way, the priority of the design requests can be determined according to the user's emotions.

[0074] When accepting design requests, the acceptance unit can prioritize accepting highly relevant requests in consideration of the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting design requests related to that area. The acceptance unit can prioritize accepting highly relevant requests in consideration of the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting design requests related to that area. It can also prioritize accepting design requests related to locations close to the user's current location. Furthermore, it can also prioritize accepting highly relevant design requests by referring to the user's past location information. This makes it possible to prioritize accepting highly relevant requests in consideration of the user's geographical location information.

[0075] When receiving a design request, the reception unit can analyze the user's social media activity and receive related requests. For example, the reception unit can prioritize receiving design requests shared by the user on social media. The reception unit analyzes the user's social media activity and receive related requests. For example, the reception unit can prioritize receiving design requests shared by the user on social media. The reception unit can also analyze the user's social media activity and receive related design requests. Furthermore, the reception unit can refer to the activity of the user's friends on social media to receive related design requests. In this way, the user's social media activity can be analyzed and related requests can be received.

[0076] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a design request. The reception unit, for example, proposes the optimal reception method based on feedback provided by the user in the past. The reception unit customizes the reception method by reflecting the user's past feedback. For example, the reception unit can propose the optimal reception method based on feedback provided by the user in the past. The reception interface can also be customized by reflecting the user's past feedback. Furthermore, the reception procedure can be optimized by referring to the user's past feedback. In this way, the reception method can be customized by reflecting the user's past feedback.

[0077] The first generation unit can estimate the user's emotions and adjust the way the design is expressed based on the estimated user's emotions. For example, if the user is relaxed, the first generation unit generates a design with soft colors. The first generation unit can analyze the user's facial expressions and voice to estimate the emotions. For example, facial expression recognition technology can be used to estimate the emotions from the user's facial expressions. Also, voice analysis technology can be used to estimate the emotions from the tone and speed of the user's voice. Furthermore, the first generation unit can generate a design with vivid colors if the user is excited. For example, if the user is excited, the first generation unit generates a design with vivid colors. Also, if the user is stressed, the first generation unit can generate a design with calm colors. This makes it possible to adjust the way the design is expressed according to the user's emotions.

[0078] When generating a design, the first generation unit can adjust the level of detail of the design based on the level of detail of the patient data. For example, if the patient data is detailed, the first generation unit generates a design that is precise down to the smallest detail. The first generation unit adjusts the level of detail of the design based on the level of detail of the patient data. For example, if the patient data is detailed, the first generation unit can generate a design that is precise down to the smallest detail. Also, if the patient data is simple, the first generation unit can generate a simple design. Furthermore, if part of the patient data is missing, the first generation unit can also complete the design based on an estimate. This makes it possible to adjust the level of detail of the design based on the level of detail of the patient data.

[0079] The first generation unit can apply different design algorithms depending on the category of the accessory when generating a design. For example, in the case of a wheelchair accessory, the first generation unit applies a design algorithm that emphasizes durability. The first generation unit applies different design algorithms depending on the category of the accessory. For example, in the case of a wheelchair accessory, the first generation unit can apply a design algorithm that emphasizes durability. In addition, in the case of a white cane accessory, the first generation unit can apply a design algorithm that emphasizes lightness. Furthermore, in the case of a cast accessory, the first generation unit can also apply a design algorithm that emphasizes comfort. In this way, different design algorithms can be applied depending on the category of the accessory.

[0080] When generating a design, the first generation unit can improve the accuracy of the design by referring to the user's past design results. The first generation unit, for example, generates a new design by referring to designs created by the user in the past. The first generation unit improves the accuracy of the design by referring to the user's past design results. For example, the first generation unit can generate a new design by referring to designs created by the user in the past. In addition, the first generation unit can extract a preferred style from the user's past design results and reflect it in the new design. Furthermore, the first generation unit can analyze the user's past design results and generate a new design by reflecting improvements. In this way, the first generation unit can improve the accuracy of the design by referring to the user's past design results.

[0081] The first generation unit can estimate the user's emotions and adjust the length of the design based on the estimated user's emotions. For example, if the user is in a hurry, the first generation unit generates a design that can be completed in a short time. The first generation unit can analyze the user's facial expressions and voice to estimate the emotions. For example, facial expression recognition technology can be used to estimate the emotions from the user's facial expressions. Also, voice analysis technology can be used to estimate the emotions from the tone and speed of the user's voice. Furthermore, the first generation unit can generate a detailed design when the user is relaxed. For example, when the user is relaxed, the first generation unit generates a detailed design. Also, when the user is excited, it can generate a visually stimulating design. This makes it possible to adjust the length of the design according to the user's emotions.

[0082] When generating a design, the first generation unit can determine the priority of the design based on the time of submission of the patient data. For example, if the patient data is submitted early, the first generation unit generates the design with priority. The first generation unit determines the priority of the design based on the time of submission of the patient data. For example, if the patient data is submitted early, the first generation unit can generate the design with priority. Furthermore, if the patient data is submitted late, the first generation unit can generate the design at a later date. Furthermore, the priority of the design can be dynamically adjusted based on the time of submission of the patient data. This makes it possible to determine the priority of the design based on the time of submission of the patient data.

[0083] The first generation unit can adjust the order of designs based on the relevance of patient data when generating designs. For example, if the patient data is highly relevant, the first generation unit generates designs with priority. The first generation unit adjusts the order of designs based on the relevance of patient data. For example, if the patient data is highly relevant, the first generation unit can generate designs with priority. Furthermore, if the patient data is less relevant, the first generation unit can generate designs at a later date. Furthermore, the order of designs can also be dynamically adjusted based on the relevance of patient data. This makes it possible to adjust the order of designs based on the relevance of patient data.

[0084] The first generation unit can adjust the use of technical terminology in the design according to the user's level of expertise when generating the design. For example, if the user has technical expertise, the first generation unit generates a design that makes extensive use of technical terminology. The first generation unit adjusts the use of technical terminology in the design according to the user's level of expertise. For example, if the user has technical expertise, the first generation unit can generate a design that makes extensive use of technical terminology. Furthermore, if the user does not have technical expertise, the first generation unit can generate a design that is explained in simple terms. Furthermore, the use of technical terminology in the design can also be dynamically adjusted based on the user's level of expertise. This makes it possible to adjust the use of technical terminology in the design according to the user's level of expertise.

[0085] The providing unit can estimate the user's emotions and adjust the method of providing 3D CAD data based on the estimated user's emotions. For example, if the user is relaxed, the providing unit provides 3D CAD data including detailed descriptions. The providing unit can analyze the user's facial expressions and voice to estimate emotions. For example, facial expression recognition technology can be used to estimate emotions from the user's facial expressions. Voice analysis technology can also be used to estimate emotions from the tone and speed of the user's voice. Furthermore, if the user is in a hurry, the providing unit can provide 3D CAD data including concise descriptions. For example, if the user is in a hurry, the providing unit can provide 3D CAD data including concise descriptions. Furthermore, if the user is excited, the providing unit can provide visually appealing 3D CAD data. This makes it possible to adjust the method of providing 3D CAD data according to the user's emotions.

[0086] When providing 3D CAD data, the providing unit can select the optimal providing method by referring to the user's past data usage history. For example, the providing unit preferentially suggests a providing method (email, cloud storage, etc.) that the user has used in the past. The providing unit selects the optimal providing method by referring to the user's past data usage history. For example, if the user previously received 3D CAD data by email, the providing unit can preferentially suggest providing the data by email. Also, if the user previously received 3D CAD data via cloud storage, the providing unit can preferentially suggest providing the data via cloud storage. Furthermore, the providing unit can also provide the data at a specific time period based on the user's past data usage history. For example, if the user previously used 3D CAD data during a specific time period, the providing unit can suggest providing the data during that time period. In this way, the optimal providing method can be selected by referring to the user's past data usage history.

[0087] When providing 3D CAD data, the providing unit can customize the provided content according to the user's current project. For example, the providing unit can provide 3D CAD data related to the user's current project with priority. The providing unit customizes the provided content according to the user's current project. For example, the providing unit can provide 3D CAD data related to the user's current project with priority. The provided content can also be customized according to the progress of the user's project. Furthermore, the most suitable 3D CAD data can be provided based on the type of the user's project. This makes it possible to customize the provided content according to the user's current project.

[0088] The providing unit can improve the providing method by reflecting user feedback when providing 3D CAD data. For example, the providing unit proposes an optimal providing method based on feedback previously provided by the user. The providing unit improves the providing method by reflecting user feedback. For example, the providing unit can propose an optimal providing method based on feedback previously provided by the user. The providing unit can also customize the providing interface by reflecting user feedback. Furthermore, the providing procedure can be optimized by referring to user feedback. In this way, the providing method can be improved by reflecting user feedback.

[0089] The providing unit can estimate the user's emotions and determine the priority of providing 3D CAD data based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing 3D CAD data that is highly urgent. The providing unit can analyze the user's facial expressions and voice to estimate emotions. For example, facial expression recognition technology can be used to estimate emotions from the user's facial expressions. Voice analysis technology can also be used to estimate emotions from the tone and speed of the user's voice. Furthermore, if the user is relaxed, the providing unit can provide 3D CAD data with normal priority. For example, if the user is relaxed, the providing unit can provide 3D CAD data with normal priority. Furthermore, if the user is excited, the providing unit can prioritize providing 3D CAD data that is highly creative. This makes it possible to determine the priority of providing 3D CAD data according to the user's emotions.

[0090] When providing 3D CAD data, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the providing unit can preferentially provide 3D CAD data related to that area. The providing unit selects the optimal providing method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the providing unit can preferentially provide 3D CAD data related to that area. It can also preferentially provide 3D CAD data related to locations close to the user's current location. Furthermore, it can also preferentially provide highly relevant 3D CAD data by referring to the user's past location information. This makes it possible to select the optimal providing method by taking into consideration the user's geographical location information.

[0091] When providing 3D CAD data, the providing unit can analyze the user's social media activity and suggest content to be provided. For example, the providing unit provides 3D CAD data based on design requests shared by the user on social media. The providing unit analyzes the user's social media activity and provides related 3D CAD data. For example, the providing unit can provide 3D CAD data based on design requests shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related 3D CAD data. Furthermore, the providing unit can provide related 3D CAD data by referring to the activity of the user's friends on social media. In this way, the providing unit can analyze the user's social media activity and suggest content to be provided.

[0092] The providing unit can customize the providing method by reflecting the user's past feedback when providing 3D CAD data. The providing unit, for example, proposes an optimal providing method based on feedback provided by the user in the past. The providing unit customizes the providing method by reflecting the user's past feedback. For example, the providing unit can propose an optimal providing method based on feedback provided by the user in the past. The providing unit can also customize the providing interface by reflecting the user's past feedback. Furthermore, the providing procedure can be optimized by referring to the user's past feedback. In this way, the providing method can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, first generation unit, second generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, allowing the user to input design requests. The first generation unit is realized by the specific processing unit 290 of the data processing device 12, interprets the user's requests using the LLM, and generates an optimal accessory design. The second generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates 3D CAD data based on the generated design. The provision unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides the generated 3D CAD data to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, first generation unit, second generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input design requests by voice. The first generation unit is realized by the specific processing unit 290 of the data processing device 12, interprets the user's requests using LLM, and generates an optimal accessory design. The second generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates 3D CAD data based on the generated design. The provision unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides the generated 3D CAD data to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, first generation unit, second generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input design requests by voice. The first generation unit is realized by the specific processing unit 290 of the data processing device 12, interprets the user's requests using the LLM, and generates an optimal accessory design. The second generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates 3D CAD data based on the generated design. The provision unit is realized by the display 343 of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the generated 3D CAD data to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, first generation unit, second generation unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing the user to input design requests by voice. The first generation unit is realized by the specific processing unit 290 of the data processing device 12, interprets the user's requests using the LLM, and generates an optimal accessory design. The second generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates 3D CAD data based on the generated design. The provision unit is realized by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides the generated 3D CAD data to the user.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When accepting a user's design request, the reception unit can refer to the user's past design history and automatically suggest similar design requests. For example, if the user has previously designed a wheelchair handle cover, the reception unit can suggest a similar design request based on that design history. Also, if the user has previously preferred a particular color or material, the reception unit can suggest a design request that is suitable for the user based on that information. Furthermore, the reception unit can analyze the user's past design history, estimate the user's preferred design style, and suggest a design request based on that style. This allows the user to input design requests more efficiently by utilizing their past design history.

[0095] When receiving the user's design request, the first generation unit can adjust the color and shape of the design based on the user's current mood and emotions. For example, if the user is relaxed, the first generation unit can generate a design with soft colors and curved shapes. If the user is excited, the first generation unit can generate a design with vivid colors and bold shapes. Furthermore, if the user is stressed, the first generation unit can generate a design with muted colors and simple shapes. This makes it possible to provide a design that matches the user's emotions, thereby improving user satisfaction.

[0096] When generating a design, the first generation unit can adjust the functionality of the design based on the user's living environment and usage situation. For example, if the user frequently uses the product outdoors, the first generation unit can generate a design that uses highly durable materials and waterproof functionality. If the user uses the product indoors, the first generation unit can also generate a design that emphasizes comfort. Furthermore, if the user uses the product while performing a specific activity, the first generation unit can also generate a design that is suitable for that activity. This makes it possible to provide an optimal design that suits the user's living environment and usage situation.

[0097] When providing the generated 3D CAD data to the user, the providing unit can adjust the providing method based on the user's emotions. For example, if the user is relaxed, the providing unit can provide 3D CAD data including detailed descriptions. If the user is in a hurry, the providing unit can provide 3D CAD data including concise descriptions. Furthermore, if the user is excited, the providing unit can provide visually appealing 3D CAD data. This makes it possible to select the optimal providing method according to the user's emotions.

[0098] When providing the generated 3D CAD data to a user, the providing unit can select the optimal providing method by referring to the user's past data usage history. For example, if the user previously received 3D CAD data by email, the providing unit can preferentially suggest providing the data by email. Also, if the user previously received 3D CAD data via cloud storage, the providing unit can preferentially suggest providing the data via cloud storage. Furthermore, the providing unit can provide the data at a specific time period based on the user's past data usage history. This makes it possible to select the optimal providing method by referring to the user's past data usage history.

[0099] The first generation unit can adjust the use of technical terminology in the design according to the user's level of expertise when generating the design. For example, if the user has technical expertise, the first generation unit can generate a design that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the first generation unit can generate a design that is explained in simple terms. Furthermore, the first generation unit can dynamically adjust the use of technical terminology in the design based on the user's level of expertise. This makes it possible to provide an optimal design according to the user's level of expertise.

[0100] The reception unit can estimate the user's emotions and adjust the timing of receiving design requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception of design requests can be scheduled during a time when the user is able to relax. Also, if the user is excited, the reception of design requests can be scheduled immediately, allowing ideas to be collected while they are fresh. Furthermore, if the user is tired, the reception of design requests can be scheduled after the user has rested. In this way, the reception timing of design requests can be adjusted according to the user's emotions.

[0101] When generating a design, the first generation unit can improve the accuracy of the design by referring to the user's past design results. For example, a new design can be generated by referring to designs created by the user in the past. Also, a preferred style can be extracted from the user's past design results and reflected in the new design. Furthermore, the first generation unit can analyze the user's past design results and generate a new design by reflecting improvements. In this way, the accuracy of the design can be improved by referring to the user's past design results.

[0102] The providing unit can estimate the user's emotions and determine the priority of providing 3D CAD data based on the estimated user's emotions. For example, if the user is feeling stressed, 3D CAD data with high urgency can be provided with priority. If the user is relaxed, 3D CAD data can be provided with normal priority. Furthermore, if the user is excited, 3D CAD data with high creativity can be provided with priority. In this way, the priority of providing 3D CAD data can be determined according to the user's emotions.

[0103] When generating a design, the first generation unit can apply different design algorithms depending on the category of the accessory. For example, in the case of a wheelchair accessory, the first generation unit can apply a design algorithm that emphasizes durability. In addition, in the case of a white cane accessory, the first generation unit can apply a design algorithm that emphasizes lightness. Furthermore, in the case of a cast accessory, the first generation unit can apply a design algorithm that emphasizes comfort. This makes it possible to provide an optimal design depending on the accessory category.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The user inputs their design requests into the reception unit. For example, a user can input a request such as, "I want to create a cover to attach to the handle of a wheelchair." The reception unit can accept requests in various formats, including text, image, and audio. Step 2: The first generation unit uses LLM to interpret the request received by the reception unit and generate an accessory design based on patient data and treatment period information. For example, LLM generates a steering wheel cover design tailored to the patient's hand size and grip strength. LLM uses natural language processing technology to understand the user's request and generate the optimal design. Step 3: The second generation unit generates 3D CAD data based on the design generated by the first generation unit. For example, the LLM generates CAD data that can be manufactured using a 3D printer based on the generated design. The LLM generates CAD data that describes in detail the shape and dimensions of the design. Step 4: The providing unit provides the 3D CAD data generated by the second generating unit to the user. For example, the user can manufacture an accessory using a 3D printer based on this data. The providing unit can provide the generated 3D CAD data online or on a physical medium.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] 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.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] 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.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] 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.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts design requests, a first generation unit that interprets the request received by the reception unit and generates an accessory design based on patient data and treatment period information; a second generation unit that generates 3D CAD data based on the design generated by the first generation unit; a providing unit that provides the 3D CAD data generated by the second generating unit. A system characterized by:

2. The reception unit Accepting user design requests 2. The system of claim 1.

3. The first generation unit Generate accessory designs based on patient data and treatment duration information 2. The system of claim 1.

4. The first generation unit Generate 3D CAD data based on the generated design 2. The system of claim 1.

5. The providing unit Provide the generated 3D CAD data to the user 2. The system of claim 1.

6. The first generation unit Generate designs based on the patient's hand size and grip strength 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the timing of accepting design requests based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past design requests and select the appropriate reception method 2. The system of claim 1.

Images