system

Abstract

The system according to the embodiment aims to easily create custom-made products based on the user's requests. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a control unit. The reception unit inputs a user's request. The analysis unit analyzes the request input by the reception unit. The generation unit generates G-code based on the request analyzed by the analysis unit. The control unit controls a 3D printer based on the G-code generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to easily obtain custom-made products based on their own needs.

[0005] The system according to the embodiment aims to easily create custom-made products based on the user's requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a control unit. The reception unit inputs a user's request. The analysis unit analyzes the request input by the reception unit. The generation unit generates a G-code based on the request analyzed by the analysis unit. The control unit controls the 3D printer based on the G-code generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily create custom-made products based on 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 allows users to easily obtain custom-made products. This system involves inputting a user's requirements, analyzing them with a large-scale language model (LLM), generating G-code for controlling a 3D printer, and then printing the custom-made product based on the generated G-code. This allows users to easily obtain custom-made products that reflect their preferences. For example, a user can input detailed requirements, such as specific shape, size, and material. The LLM analyzes the requirements and generates G-code, and the 3D printer prints the custom-made product based on the G-code. This allows users to easily obtain products that reflect their preferences, thereby increasing their satisfaction. Furthermore, combining a 3D printer with an LLM makes it possible to meet detailed requirements that could not be achieved with conventional mass-produced products. For example, even if a user has specific requirements for a specific shape, size, or material, the LLM can analyze them and the 3D printer can accurately reproduce them. Furthermore, this system is useful not only for individuals but also for small businesses and startups. For example, utilizing an LLM and a 3D printer can efficiently handle situations requiring small-scale production, such as creating prototypes or manufacturing custom parts. This will speed up product development and reduce costs.

[0029] A custom-made product creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a control unit. The reception unit inputs a user's requests. The user's requests include, but are not limited to, shapes, sizes, materials, and functions. The reception unit allows the user to input detailed requests, such as specific shapes, sizes, and materials. The analysis unit uses LLM to analyze the requests input by the reception unit. The analysis may be performed using, but is not limited to, natural language processing, image analysis, or voice analysis. For example, the analysis unit understands the user's requests and generates G-code for controlling a 3D printer based on the analysis. The generation unit generates the G-code based on the requests analyzed by the analysis unit. The G-code is generated based on, for example, but not limited to, the algorithm used and the accuracy of generation. For example, the generation unit generates G-code corresponding to a specific shape. The control unit controls the 3D printer based on the G-code generated by the generation unit. The control is performed based on, for example, but not limited to, the control accuracy and the protocol used. For example, the control unit controls a 3D printer to print a custom-made product based on the generated G-code. This allows the custom-made product generation system according to the embodiment to control the 3D printer based on the user's request, making it easy to obtain a custom-made product. For example, a user can input a request for a pen holder of a specific shape, the LLM can analyze the request and generate G-code, and the 3D printer can print the pen holder based on the G-code.

[0030] The reception unit can input the user's detailed requests for a specific shape or size and material. The detailed requests for a specific shape or size and material include, but are not limited to, units of measurement and types of material. For example, the reception unit can input a request for a pen holder of a specific shape. The reception unit can also input a request for a box of a specific size. Furthermore, the reception unit can also input a request for a cup made of a specific material. This allows a custom-made product that reflects the user's detailed requests to be generated. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the user's requests into AI, which can analyze the requests and extract detailed requests.

[0031] The analysis unit can understand the user's request and generate G-code for controlling the 3D printer based on the user's request. The analysis unit can understand the user's request using, for example, natural language processing. For example, the analysis unit can analyze text entered by the user and understand its content. The analysis unit can also understand the user's request using a machine learning algorithm. For example, the analysis unit can learn from the user's past request data and understand new requests. The analysis unit can also understand the user's request using image analysis. For example, the analysis unit can analyze images uploaded by the user and understand their content. This allows accurate G-code to be generated based on the user's request. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input the user's request into AI, which can analyze the request and generate G-code.

[0032] The generation unit can generate G-code corresponding to a specific shape. Specific shapes include, but are not limited to, circles, squares, triangles, etc. The generation unit can generate G-code corresponding to, for example, a circular pen holder. The generation unit can also generate G-code corresponding to a square box. The generation unit can also generate G-code corresponding to a triangular cup. By generating G-code corresponding to a specific shape, custom-made products can be created according to user requests. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input a request for a specific shape into AI, which then generates G-code corresponding to that shape.

[0033] The control unit can cause the 3D printer to print a custom-made item based on the generated G-code. Custom-made items include, but are not limited to, a pen holder, a box, a cup, and the like. For example, the control unit can cause the 3D printer to print a pen holder based on the generated G-code. The control unit can also cause the 3D printer to print a box based on the generated G-code. Furthermore, the control unit can also cause the 3D printer to print a cup based on the generated G-code. This allows the 3D printer to accurately print a custom-made item based on the generated G-code. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the generated G-code into AI, which then controls the 3D printer to print the custom-made item.

[0034] The reception unit can analyze the user's past request history and suggest the optimal input method. For example, the reception unit can automatically display requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period based on the user's past request history. This makes it possible to suggest the optimal input method based on the user's past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past request history into AI, which can analyze the history and suggest the optimal input method.

[0035] When a request is input, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can prioritize and display requests related to the user's current project. The reception unit can also filter and display related requests based on the user's area of ​​interest. Furthermore, the reception unit can suggest related requests by referring to the user's past project history. This allows related requests to be prioritized and displayed based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into AI, and the AI ​​can filter the input content based on the project or area of ​​interest.

[0036] When a request is input, the reception unit can select the optimal input means depending on the user's input method. For example, when a user inputs a request by voice, the reception unit converts the input content into text using voice recognition technology. Furthermore, when a user inputs a request using an image, the reception unit can also analyze the input content using image recognition technology. Furthermore, when a user inputs a request using text, the reception unit can also analyze the input content using text analysis technology. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to AI, which can select the optimal input means depending on the input method.

[0037] When inputting a request, the reception unit can prioritize inputting highly relevant requests based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying requests related to that area. The reception unit can also filter and display related requests based on the user's current location. Furthermore, the reception unit can also suggest related requests by referring to the user's past location information. This allows highly relevant requests to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information data to AI, which can then prioritize inputting highly relevant requests based on the geographical location information.

[0038] When a request is input, the reception unit can analyze the user's social media activity and input related requests. For example, the reception unit automatically inputs requests shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related requests. Furthermore, the reception unit can input related requests by referring to the activity of the user's friends on social media. This makes it possible to input related requests based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can analyze the social media activity and input related requests.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting a request. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the content of the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by referring to the user's feedback history. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into AI, which can analyze the feedback and customize the input method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis for requests with high importance. The analysis unit can also perform a concise analysis for requests with low importance. Furthermore, the analysis unit can adjust the depth of the analysis based on the importance of the request. This makes it possible to provide optimal analysis results based on the importance of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request importance data into AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the request. For example, the analysis unit can apply a shape analysis algorithm to a request regarding shape. The analysis unit can also apply a size analysis algorithm to a request regarding size. Furthermore, the analysis unit can apply a material analysis algorithm to a request regarding material. This makes it possible to apply the optimal analysis algorithm depending on the category of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request category data into AI, which can then apply different analysis algorithms depending on the category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis history. Furthermore, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. This improves the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis data into AI, which can then improve the accuracy of the analysis by referring to the past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of request submission. The analysis unit determines the priority of analysis based on, for example, the time of request submission. The analysis unit can also prioritize analysis of requests that were submitted earlier. Furthermore, the analysis unit can quickly analyze requests that were submitted more recently. This makes it possible to provide optimal analysis results based on the time of request submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of request submission into AI, and the AI ​​can determine the priority of analysis based on the time of submission.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the requests during analysis. For example, the analysis unit prioritizes analysis of highly relevant requests. The analysis unit can also postpone analysis of less relevant requests. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the requests. This makes it possible to provide optimal analysis results based on the relevance of the requests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of requests into AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can provide analysis results in easy-to-understand language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, and the AI ​​can adjust the use of technical terms in the analysis according to the level of expertise.

[0046] When generating a G-code, the generation unit can adjust the level of detail of the generated G-code based on the importance of the request. For example, the generation unit generates detailed G-code for requests with high importance. The generation unit can also generate concise G-code for requests with low importance. Furthermore, the generation unit can adjust the level of detail of the G-code according to the importance of the request. This makes it possible to generate an optimal G-code according to the importance of the request. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the importance of the request into AI, and the AI ​​can adjust the level of detail of the G-code based on the importance.

[0047] When generating a G-code, the generation unit can apply different generation algorithms depending on the category of the request. For example, the generation unit can apply a shape generation algorithm to a request regarding shape. The generation unit can also apply a size generation algorithm to a request regarding size. The generation unit can also apply a material generation algorithm to a request regarding material. This makes it possible to generate an optimal G-code depending on the category of the request. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input request category data into AI, which can then apply different generation algorithms depending on the category.

[0048] When generating G-code, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can improve the accuracy of current generation based on the user's past generation results. The generation unit can also optimize the generation algorithm by referring to the user's past generation history. Furthermore, the generation unit can analyze the user's past generation results and improve the accuracy of generation. This improves the accuracy of generation based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation data into AI, which can then improve the accuracy of generation by referring to the past generation results.

[0049] When generating G-code, the generation unit can determine the generation priority based on the time of request submission. The generation unit can determine the generation priority based on, for example, the time of request submission. The generation unit can also generate requests that were submitted earlier with priority. Furthermore, the generation unit can quickly generate requests that were submitted more recently. This allows the generation of optimal G-code based on the time of request submission. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on the time of request submission into AI, and the AI ​​can determine the generation priority based on the time of submission.

[0050] When generating G-code, the generation unit can adjust the order of generation based on the relevance of the requests. For example, the generation unit prioritizes the generation of highly relevant requests. The generation unit can also postpone less relevant requests. Furthermore, the generation unit can adjust the order of generation according to the relevance of the requests. This allows the generation of optimal G-code based on the relevance of the requests. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input request relevance data into AI, and the AI ​​can adjust the order of generation based on the relevance.

[0051] When generating G-code, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user's level of expertise is high, the generation unit can generate G-code that uses a lot of technical terminology. Furthermore, if the user's level of expertise is low, the generation unit can also generate G-code in easy-to-understand language. Furthermore, the generation unit can adjust the way the G-code is expressed according to the user's level of expertise. This allows for the generation of optimal G-code according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology in the generation according to the level of expertise.

[0052] The control unit can adjust the level of detail of control based on the importance of the request when controlling the 3D printer. For example, the control unit performs detailed control for requests with high importance. The control unit can also perform simple control for requests with low importance. Furthermore, the control unit can adjust the level of detail of control according to the importance of the request. This makes it possible to provide optimal control of the 3D printer according to the importance of the request. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input data on the importance of the request to AI, and the AI ​​can adjust the level of detail of control based on the importance.

[0053] When controlling the 3D printer, the control unit can apply different control algorithms depending on the category of request. For example, the control unit can apply a shape control algorithm to a request regarding shape. The control unit can also apply a size control algorithm to a request regarding size. Furthermore, the control unit can apply a material control algorithm to a request regarding material. This makes it possible to provide optimal control of the 3D printer depending on the category of request. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input request category data into AI, which can then apply different control algorithms depending on the category.

[0054] When controlling a 3D printer, the control unit can improve the accuracy of control by referring to the user's past control results. The control unit can, for example, improve the accuracy of current control based on the user's past control results. The control unit can also optimize the control algorithm by referring to the user's past control history. Furthermore, the control unit can analyze the user's past control results and improve the accuracy of control. This improves the accuracy of control based on the user's past control results. Some or all of the above-described processing in the control unit can be performed using, for example, AI, or can be performed without using AI. For example, the control unit can input the user's past control data into AI, which can then improve the accuracy of control by referring to the past control results.

[0055] When controlling the 3D printer, the control unit can determine the priority of control based on the time of request submission. The control unit determines the priority of control based on, for example, the time of request submission. The control unit can also prioritize control of requests that were submitted earlier. Furthermore, the control unit can quickly control requests that were submitted more recently. This makes it possible to provide optimal control of the 3D printer based on the time of request submission. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input data on the time of request submission into AI, and the AI ​​can determine the priority of control based on the time of submission.

[0056] When controlling a 3D printer, the control unit can adjust the order of control based on the relevance of requests. For example, the control unit prioritizes control of highly relevant requests. The control unit can also postpone less relevant requests. Furthermore, the control unit can adjust the order of control according to the relevance of requests. This makes it possible to provide optimal control of the 3D printer based on the relevance of requests. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input request relevance data into AI, and the AI ​​can adjust the order of control based on the relevance.

[0057] When controlling a 3D printer, the control unit can adjust the use of technical terminology for control according to the user's level of expertise. For example, if the user has a high level of expertise, the control unit can perform control using a lot of technical terminology. Furthermore, if the user has a low level of expertise, the control unit can also perform control using easy-to-understand language. Furthermore, the control unit can adjust the way in which control is expressed according to the user's level of expertise. This allows for optimal control of the 3D printer according to the user's level of expertise. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology for control according to the user's level of expertise.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When a user inputs a request, the reception unit can refer to the user's past purchase history and suggest related custom-made items. For example, the reception unit can suggest items with similar designs or materials based on data on items the user has previously purchased. The reception unit can also generate a list of frequently purchased items from the user's past purchase history and present it to the user. Furthermore, the reception unit can analyze the user's past purchase history and suggest custom-made items according to the season or event. This allows for more personalized suggestions based on the user's past purchase history.

[0060] When generating G-code, the generator can improve the accuracy of the generation by referring to the user's past feedback. For example, the generator can optimize the generation algorithm based on the user's past feedback. The generator can also adjust the G-code generation method by reflecting the user's past feedback. Furthermore, the generator can improve the accuracy of the generation by referring to the user's feedback history. This allows for the generation of more accurate G-code based on the user's past feedback.

[0061] When controlling the 3D printer, the control unit can select the optimal control method based on the user's geographical location information. For example, if the user is in a hot and humid region, the control unit can strengthen temperature and humidity management of the 3D printer. Also, if the user is in a cold region, the control unit can appropriately adjust the temperature of the 3D printer. Furthermore, if the user is in a high-altitude region, the control unit can optimize the operation of the 3D printer. This makes it possible to provide the optimal 3D printer control method based on the user's geographical location information.

[0062] The reception unit can analyze the user's social media activity and suggest related custom-made products. For example, it can suggest related designs and materials based on images and posts shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest custom-made products based on trends. Furthermore, the reception unit can also suggest related custom-made products based on the activity of the user's friends on social media. This allows for more personalized suggestions based on the user's social media activity.

[0063] When generating G-code, the generator can adjust the generation method based on the user's current project and areas of interest. For example, the generator can prioritize generation of requests related to the user's current project. The generator can also apply a relevant generation algorithm based on the user's areas of interest. Furthermore, the generator can suggest a relevant generation method by referring to the user's past project history. This allows the generator to generate optimal G-code based on the user's current project and areas of interest.

[0064] The reception unit can customize the desired input interface by reflecting the user's past feedback. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also adjust the input interface by reflecting the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by referring to the user's feedback history. This makes it possible to provide an easier-to-use input interface based on the user's past feedback.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit inputs the user's requirements. The user's requirements include shape, size, material, function, etc. For example, the user can input detailed requirements such as specific shape, size, material, etc. Step 2: The analysis unit uses the LLM to analyze the request entered by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. For example, the analysis unit understands the user's request and generates G-code to control a 3D printer based on it. Step 3: The generator generates G-code based on the requirements analyzed by the analyzer. The G-code is generated based on the algorithm used and the accuracy of the generation. For example, the generator generates G-code corresponding to a specific shape. Step 4: The control unit controls the 3D printer based on the G-code generated by the generation unit. The control is performed based on the control precision and the protocol used. For example, the control unit controls the 3D printer to print a custom-made product based on the generated G-code.

[0067] (Example 2) A system according to an embodiment of the present invention allows users to easily obtain custom-made products. This system involves inputting a user's requirements, analyzing them with a large-scale language model (LLM), generating G-code for controlling a 3D printer, and then printing the custom-made product based on the generated G-code. This allows users to easily obtain custom-made products that reflect their preferences. For example, a user can input detailed requirements, such as specific shape, size, and material. The LLM analyzes the requirements and generates G-code, and the 3D printer prints the custom-made product based on the G-code. This allows users to easily obtain products that reflect their preferences, thereby increasing their satisfaction. Furthermore, combining a 3D printer with an LLM makes it possible to meet detailed requirements that could not be achieved with conventional mass-produced products. For example, even if a user has specific requirements for a specific shape, size, or material, the LLM can analyze them and the 3D printer can accurately reproduce them. Furthermore, this system is useful not only for individuals but also for small businesses and startups. For example, utilizing an LLM and a 3D printer can efficiently handle situations requiring small-scale production, such as creating prototypes or manufacturing custom parts. This will speed up product development and reduce costs.

[0068] A custom-made product creation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a control unit. The reception unit inputs a user's requests. The user's requests include, but are not limited to, shapes, sizes, materials, and functions. The reception unit allows the user to input detailed requests, such as specific shapes, sizes, and materials. The analysis unit uses LLM to analyze the requests input by the reception unit. The analysis may be performed using, but is not limited to, natural language processing, image analysis, or voice analysis. For example, the analysis unit understands the user's requests and generates G-code for controlling a 3D printer based on the analysis. The generation unit generates the G-code based on the requests analyzed by the analysis unit. The G-code is generated based on, for example, but not limited to, the algorithm used and the accuracy of generation. For example, the generation unit generates G-code corresponding to a specific shape. The control unit controls the 3D printer based on the G-code generated by the generation unit. The control is performed based on, for example, but not limited to, the control accuracy and the protocol used. For example, the control unit controls a 3D printer to print a custom-made product based on the generated G-code. This allows the custom-made product generation system according to the embodiment to control the 3D printer based on the user's request, making it easy to obtain a custom-made product. For example, a user can input a request for a pen holder of a specific shape, the LLM can analyze the request and generate G-code, and the 3D printer can print the pen holder based on the G-code.

[0069] The reception unit can input the user's detailed requests for a specific shape or size and material. The detailed requests for a specific shape or size and material include, but are not limited to, units of measurement and types of material. For example, the reception unit can input a request for a pen holder of a specific shape. The reception unit can also input a request for a box of a specific size. Furthermore, the reception unit can also input a request for a cup made of a specific material. This allows a custom-made product that reflects the user's detailed requests to be generated. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the user's requests into AI, which can analyze the requests and extract detailed requests.

[0070] The analysis unit can understand the user's request and generate G-code for controlling the 3D printer based on the user's request. The analysis unit can understand the user's request using, for example, natural language processing. For example, the analysis unit can analyze text entered by the user and understand its content. The analysis unit can also understand the user's request using a machine learning algorithm. For example, the analysis unit can learn from the user's past request data and understand new requests. The analysis unit can also understand the user's request using image analysis. For example, the analysis unit can analyze images uploaded by the user and understand their content. This allows accurate G-code to be generated based on the user's request. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input the user's request into AI, which can analyze the request and generate G-code.

[0071] The generation unit can generate G-code corresponding to a specific shape. Specific shapes include, but are not limited to, circles, squares, triangles, etc. The generation unit can generate G-code corresponding to, for example, a circular pen holder. The generation unit can also generate G-code corresponding to a square box. The generation unit can also generate G-code corresponding to a triangular cup. By generating G-code corresponding to a specific shape, custom-made products can be created according to user requests. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input a request for a specific shape into AI, which then generates G-code corresponding to that shape.

[0072] The control unit can cause the 3D printer to print a custom-made item based on the generated G-code. Custom-made items include, but are not limited to, a pen holder, a box, a cup, and the like. For example, the control unit can cause the 3D printer to print a pen holder based on the generated G-code. The control unit can also cause the 3D printer to print a box based on the generated G-code. Furthermore, the control unit can also cause the 3D printer to print a cup based on the generated G-code. This allows the 3D printer to accurately print a custom-made item based on the generated G-code. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the generated G-code into AI, which then controls the 3D printer to print the custom-made item.

[0073] The reception unit can estimate the user's emotions and adjust the input interface for requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input requests. This allows for an optimal input interface to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into an AI, which can then estimate the emotion and adjust the input interface.

[0074] The reception unit can analyze the user's past request history and suggest the optimal input method. For example, the reception unit can automatically display requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period based on the user's past request history. This makes it possible to suggest the optimal input method based on the user's past request history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past request history into AI, which can analyze the history and suggest the optimal input method.

[0075] When a request is input, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit can prioritize and display requests related to the user's current project. The reception unit can also filter and display related requests based on the user's area of ​​interest. Furthermore, the reception unit can suggest related requests by referring to the user's past project history. This allows related requests to be prioritized and displayed based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into AI, and the AI ​​can filter the input content based on the project or area of ​​interest.

[0076] When a request is input, the reception unit can select the optimal input means depending on the user's input method. For example, when a user inputs a request by voice, the reception unit converts the input content into text using voice recognition technology. Furthermore, when a user inputs a request using an image, the reception unit can also analyze the input content using image recognition technology. Furthermore, when a user inputs a request using text, the reception unit can also analyze the input content using text analysis technology. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to AI, which can select the optimal input means depending on the input method.

[0077] The reception unit can estimate the user's emotions and determine the priority of input requests based on the estimated user emotions. For example, if the user inputs an urgent request, the reception unit processes the request with the highest priority. Furthermore, if the user is relaxed, the reception unit can process the request with normal priority. Furthermore, if the user is feeling stressed, the reception unit can also increase the priority of the request and respond quickly. This allows the priority of requests to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into an AI, which can estimate the emotion and determine the priority of the requests.

[0078] When inputting a request, the reception unit can prioritize inputting highly relevant requests based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying requests related to that area. The reception unit can also filter and display related requests based on the user's current location. Furthermore, the reception unit can also suggest related requests by referring to the user's past location information. This allows highly relevant requests to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information data to AI, which can then prioritize inputting highly relevant requests based on the geographical location information.

[0079] When a request is input, the reception unit can analyze the user's social media activity and input related requests. For example, the reception unit automatically inputs requests shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related requests. Furthermore, the reception unit can input related requests by referring to the activity of the user's friends on social media. This makes it possible to input related requests based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into AI, which can analyze the social media activity and input related requests.

[0080] The reception unit can customize the input method by reflecting the user's past feedback when inputting a request. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by reflecting the content of the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by referring to the user's feedback history. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into AI, which can analyze the feedback and customize the input method.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. This makes it possible to provide optimal analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can estimate the emotion and adjust the presentation method of the analysis.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, the analysis unit performs a detailed analysis for requests with high importance. The analysis unit can also perform a concise analysis for requests with low importance. Furthermore, the analysis unit can adjust the depth of the analysis based on the importance of the request. This makes it possible to provide optimal analysis results based on the importance of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request importance data into AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the request. For example, the analysis unit can apply a shape analysis algorithm to a request regarding shape. The analysis unit can also apply a size analysis algorithm to a request regarding size. Furthermore, the analysis unit can apply a material analysis algorithm to a request regarding material. This makes it possible to apply the optimal analysis algorithm depending on the category of the request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input request category data into AI, which can then apply different analysis algorithms depending on the category.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis history. Furthermore, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. This improves the accuracy of the analysis based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis data into AI, which can then improve the accuracy of the analysis by referring to the past analysis results.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. This allows for optimal analysis results to be provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then estimate the emotion and adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time of request submission. The analysis unit determines the priority of analysis based on, for example, the time of request submission. The analysis unit can also prioritize analysis of requests that were submitted earlier. Furthermore, the analysis unit can quickly analyze requests that were submitted more recently. This makes it possible to provide optimal analysis results based on the time of request submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of request submission into AI, and the AI ​​can determine the priority of analysis based on the time of submission.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the requests during analysis. For example, the analysis unit prioritizes analysis of highly relevant requests. The analysis unit can also postpone analysis of less relevant requests. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the requests. This makes it possible to provide optimal analysis results based on the relevance of the requests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of requests into AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0088] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can provide analysis results in easy-to-understand language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, and the AI ​​can adjust the use of technical terms in the analysis according to the level of expertise.

[0089] The generation unit can estimate the user's emotions and adjust the G-code generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate detailed G-code. Furthermore, if the user is in a hurry, the generation unit can generate concise G-code. Furthermore, if the user is stressed, the generation unit can generate visually easy-to-understand G-code. This allows the generation of optimal G-code according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using AI, or without AI. For example, the generation unit can input the user's emotion data into AI, which can estimate the emotion and adjust the G-code generation method.

[0090] When generating a G-code, the generation unit can adjust the level of detail of the generated G-code based on the importance of the request. For example, the generation unit generates detailed G-code for requests with high importance. The generation unit can also generate concise G-code for requests with low importance. Furthermore, the generation unit can adjust the level of detail of the G-code according to the importance of the request. This makes it possible to generate an optimal G-code according to the importance of the request. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the importance of the request into AI, and the AI ​​can adjust the level of detail of the G-code based on the importance.

[0091] When generating a G-code, the generation unit can apply different generation algorithms depending on the category of the request. For example, the generation unit can apply a shape generation algorithm to a request regarding shape. The generation unit can also apply a size generation algorithm to a request regarding size. The generation unit can also apply a material generation algorithm to a request regarding material. This makes it possible to generate an optimal G-code depending on the category of the request. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input request category data into AI, which can then apply different generation algorithms depending on the category.

[0092] When generating G-code, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit can improve the accuracy of current generation based on the user's past generation results. The generation unit can also optimize the generation algorithm by referring to the user's past generation history. Furthermore, the generation unit can analyze the user's past generation results and improve the accuracy of generation. This improves the accuracy of generation based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation data into AI, which can then improve the accuracy of generation by referring to the past generation results.

[0093] The generation unit can estimate the user's emotions and adjust the length of the G-code based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate a short and concise G-code. Furthermore, if the user is relaxed, the generation unit can generate a detailed G-code. Furthermore, if the user is stressed, the generation unit can generate a visually easy-to-understand G-code. This allows the generation of an optimal G-code according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into an AI, which can estimate the emotion and adjust the length of the G-code.

[0094] When generating G-code, the generation unit can determine the generation priority based on the time of request submission. The generation unit can determine the generation priority based on, for example, the time of request submission. The generation unit can also generate requests that were submitted earlier with priority. Furthermore, the generation unit can quickly generate requests that were submitted more recently. This allows the generation of optimal G-code based on the time of request submission. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on the time of request submission into AI, and the AI ​​can determine the generation priority based on the time of submission.

[0095] When generating G-code, the generation unit can adjust the order of generation based on the relevance of the requests. For example, the generation unit prioritizes the generation of highly relevant requests. The generation unit can also postpone less relevant requests. Furthermore, the generation unit can adjust the order of generation according to the relevance of the requests. This allows the generation of optimal G-code based on the relevance of the requests. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input request relevance data into AI, and the AI ​​can adjust the order of generation based on the relevance.

[0096] When generating G-code, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user's level of expertise is high, the generation unit can generate G-code that uses a lot of technical terminology. Furthermore, if the user's level of expertise is low, the generation unit can also generate G-code in easy-to-understand language. Furthermore, the generation unit can adjust the way the G-code is expressed according to the user's level of expertise. This allows for the generation of optimal G-code according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology in the generation according to the level of expertise.

[0097] The control unit can estimate the user's emotions and adjust the control method of the 3D printer based on the estimated user's emotions. For example, if the user is relaxed, the control unit can control the 3D printer at a normal speed. The control unit can also increase the speed of the 3D printer if the user is in a hurry. Furthermore, if the user is stressed, the control unit can control the 3D printer to minimize errors. This makes it possible to provide an optimal 3D printer control method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the control unit may be performed using AI, or may be performed without AI. For example, the control unit can input the user's emotion data into AI, which can then estimate the emotion and adjust the control method of the 3D printer.

[0098] The control unit can adjust the level of detail of control based on the importance of the request when controlling the 3D printer. For example, the control unit performs detailed control for requests with high importance. The control unit can also perform simple control for requests with low importance. Furthermore, the control unit can adjust the level of detail of control according to the importance of the request. This makes it possible to provide optimal control of the 3D printer according to the importance of the request. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input data on the importance of the request to AI, and the AI ​​can adjust the level of detail of control based on the importance.

[0099] When controlling the 3D printer, the control unit can apply different control algorithms depending on the category of request. For example, the control unit can apply a shape control algorithm to a request regarding shape. The control unit can also apply a size control algorithm to a request regarding size. Furthermore, the control unit can apply a material control algorithm to a request regarding material. This makes it possible to provide optimal control of the 3D printer depending on the category of request. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input request category data into AI, which can then apply different control algorithms depending on the category.

[0100] When controlling a 3D printer, the control unit can improve the accuracy of control by referring to the user's past control results. The control unit can, for example, improve the accuracy of current control based on the user's past control results. The control unit can also optimize the control algorithm by referring to the user's past control history. Furthermore, the control unit can analyze the user's past control results and improve the accuracy of control. This improves the accuracy of control based on the user's past control results. Some or all of the above-described processing in the control unit can be performed using, for example, AI, or can be performed without using AI. For example, the control unit can input the user's past control data into AI, which can then improve the accuracy of control by referring to the past control results.

[0101] The control unit can estimate the user's emotions and adjust the control order of the 3D printer based on the estimated user's emotions. For example, if the user is in a hurry, the control unit can prioritize control of important parts. The control unit can also control in a normal order if the user is relaxed. Furthermore, if the user is stressed, the control unit can adjust the control order to minimize errors. This allows for providing an optimal 3D printer control order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the control unit can be performed using, for example, an AI, or without an AI. For example, the control unit can input the user's emotion data into an AI, which can then estimate the emotion and adjust the control order of the 3D printer.

[0102] When controlling the 3D printer, the control unit can determine the priority of control based on the time of request submission. The control unit determines the priority of control based on, for example, the time of request submission. The control unit can also prioritize control of requests that were submitted earlier. Furthermore, the control unit can quickly control requests that were submitted more recently. This makes it possible to provide optimal control of the 3D printer based on the time of request submission. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input data on the time of request submission into AI, and the AI ​​can determine the priority of control based on the time of submission.

[0103] When controlling a 3D printer, the control unit can adjust the order of control based on the relevance of requests. For example, the control unit prioritizes control of highly relevant requests. The control unit can also postpone less relevant requests. Furthermore, the control unit can adjust the order of control according to the relevance of requests. This makes it possible to provide optimal control of the 3D printer based on the relevance of requests. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input request relevance data into AI, and the AI ​​can adjust the order of control based on the relevance.

[0104] When controlling a 3D printer, the control unit can adjust the use of technical terminology for control according to the user's level of expertise. For example, if the user has a high level of expertise, the control unit can perform control using a lot of technical terminology. Furthermore, if the user has a low level of expertise, the control unit can also perform control using easy-to-understand language. Furthermore, the control unit can adjust the way in which control is expressed according to the user's level of expertise. This allows for optimal control of the 3D printer according to the user's level of expertise. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology for control according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and control 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 and inputs a user's request. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using LLM. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates G-code based on the analyzed request. The control unit is realized, for example, by the control unit 46A of the smart device 14 and controls the 3D printer based on the generated G-code. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and control 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 and inputs a user's request. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using LLM. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates G-code based on the analyzed request. The control unit is realized, for example, by the control unit 46A of the smart glasses 214 and controls the 3D printer based on the generated G-code. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and control 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 and inputs the user's request. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using LLM. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates G-code based on the analyzed request. The control unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and controls the 3D printer based on the generated G-code. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and control 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 and inputs the user's request. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request using LLM. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates G-code based on the analyzed request. The control unit is realized, for example, by the control unit 46A of the robot 414 and controls the 3D printer based on the generated G-code.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] When a user inputs a request, the reception unit can refer to the user's past purchase history and suggest related custom-made items. For example, the reception unit can suggest items with similar designs or materials based on data on items the user has previously purchased. The reception unit can also generate a list of frequently purchased items from the user's past purchase history and present it to the user. Furthermore, the reception unit can analyze the user's past purchase history and suggest custom-made items according to the season or event. This allows for more personalized suggestions based on the user's past purchase history.

[0107] When analyzing a user's request, the analysis unit can adjust the accuracy of the analysis based on the user's current mood and physical condition. For example, if the user is tired, the analysis unit can provide a concise and easy-to-understand analysis result. Alternatively, if the user is concentrating, the analysis unit can provide a detailed analysis result. Furthermore, if the user is feeling stressed, the analysis unit can provide a visually easy-to-understand analysis result. This makes it possible to provide optimal analysis results according to the user's emotions and physical condition.

[0108] When generating G-code, the generator can improve the accuracy of the generation by referring to the user's past feedback. For example, the generator can optimize the generation algorithm based on the user's past feedback. The generator can also adjust the G-code generation method by reflecting the user's past feedback. Furthermore, the generator can improve the accuracy of the generation by referring to the user's feedback history. This allows for the generation of more accurate G-code based on the user's past feedback.

[0109] When controlling the 3D printer, the control unit can select the optimal control method based on the user's geographical location information. For example, if the user is in a hot and humid region, the control unit can strengthen temperature and humidity management of the 3D printer. Also, if the user is in a cold region, the control unit can appropriately adjust the temperature of the 3D printer. Furthermore, if the user is in a high-altitude region, the control unit can optimize the operation of the 3D printer. This makes it possible to provide the optimal 3D printer control method based on the user's geographical location information.

[0110] The reception unit can analyze the user's social media activity and suggest related custom-made products. For example, it can suggest related designs and materials based on images and posts shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest custom-made products based on trends. Furthermore, the reception unit can also suggest related custom-made products based on the activity of the user's friends on social media. This allows for more personalized suggestions based on the user's social media activity.

[0111] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user inputs an urgent request, the analysis of that request is given the highest priority. If the user is relaxed, the analysis can be performed with normal priority. Furthermore, if the user is feeling stressed, the priority of the request can be increased and analyzed quickly. In this way, the analysis priority can be determined according to the user's emotions.

[0112] When generating G-code, the generator can adjust the generation method based on the user's current project and areas of interest. For example, the generator can prioritize generation of requests related to the user's current project. The generator can also apply a relevant generation algorithm based on the user's areas of interest. Furthermore, the generator can suggest a relevant generation method by referring to the user's past project history. This allows the generator to generate optimal G-code based on the user's current project and areas of interest.

[0113] When controlling the 3D printer, the control unit can estimate the user's emotions and adjust the level of control detail based on the estimated user emotions. For example, if the user is relaxed, the control unit can control the 3D printer at a normal level of detail. If the user is in a hurry, the control unit can simplify the level of control detail to respond quickly. Furthermore, if the user is feeling stressed, the control unit can adjust the level of control detail to minimize errors. This makes it possible to provide an optimal 3D printer control method according to the user's emotions.

[0114] The reception unit can customize the desired input interface by reflecting the user's past feedback. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also adjust the input interface by reflecting the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by referring to the user's feedback history. This makes it possible to provide an easier-to-use input interface based on the user's past feedback.

[0115] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is feeling stressed, analysis results that are visually easy to understand can be provided. This makes it possible to provide optimal analysis results according to the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit inputs the user's requirements. The user's requirements include shape, size, material, function, etc. For example, the user can input detailed requirements such as specific shape, size, material, etc. Step 2: The analysis unit uses the LLM to analyze the request entered by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. For example, the analysis unit understands the user's request and generates G-code to control a 3D printer based on it. Step 3: The generator generates G-code based on the requirements analyzed by the analyzer. The G-code is generated based on the algorithm used and the accuracy of the generation. For example, the generator generates G-code corresponding to a specific shape. Step 4: The control unit controls the 3D printer based on the G-code generated by the generation unit. The control is performed based on the control precision and the protocol used. For example, the control unit controls the 3D printer to print a custom-made product based on the generated G-code.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 unit for inputting a user's request; an analysis unit that analyzes the request input by the reception unit; a generation unit that generates a G-code based on the requirements analyzed by the analysis unit; A control unit that controls a 3D printer based on the G-code generated by the generation unit; Equipped with A system characterized by:

2. The reception unit Enter the user's detailed requirements for specific shapes, sizes, and materials.

2. The system of claim 1.

3. The analysis unit Understand the user's requirements and generate G-code to control the 3D printer based on them 2. The system of claim 1.

4. The generation unit Generate G-code for a specific shape 2. The system of claim 1.

5. The control unit The 3D printer prints the custom-made product based on the generated G-code.

2. The system of claim 1.

6. The reception unit The user's emotion is estimated, and a request input interface is adjusted based on the estimated user's emotion.

2. The system of claim 1.

7. The reception unit Analyze the user's past request history and suggest the appropriate input method 2. The system of claim 1.

8. The reception unit As you type your request, filter it based on your current projects and interests 2. The system of claim 1.

9. The reception unit When inputting a request, select the most appropriate input method according to the user's input method.

2. The system of claim 1.

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