System

Abstract

An object of the system according to the embodiment is to enable even a person who does not have a 3D printer to easily make his or her own products.SOLUTION: A system according to an embodiment includes an image generation AI, a matching system, and a notifying system. The image generation AI generates a 3D model based on the user's request. The matching system matches the owner of the 3D printer with a person who wants to produce a commodity based on the 3D model generated by the image generation AI. The notifying system notifies the 3D printer holder matched by the matching system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when people who do not own a 3D printer wanted to create their own products, it was difficult for individuals to align on the specifications.

[0005] The system according to the embodiment aims to enable people who do not own a 3D printer to easily create their own unique products. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation AI, a matching system, and a notification system. The image generation AI generates a 3D model based on a user's request. The matching system matches 3D printer owners with people who want to create products based on the 3D model generated by the image generation AI. The notification system notifies 3D printer owners who have been matched by the matching system. [Effects of the Invention]

[0007] The system according to the embodiment can enable even people who do not have a 3D printer to easily create their own unique products. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A matching system according to an embodiment of the present invention matches people who own a home 3D printer with people who want to create their own unique products. This system uses image generation AI to facilitate the recognition and alignment of detailed specifications, aiming to create products that satisfy both parties. This allows the matching system to efficiently match people who own a home 3D printer with people who want to create their own unique products, enabling the creation of products that satisfy both parties.

[0029] A matching system according to an embodiment includes an image generation AI, a matching system, and a notification system. The image generation AI generates a 3D model based on a user's request. For example, if a user requests a "cat-shaped pen holder," the image generation AI generates a 3D model of a cat-shaped pen holder based on the request. The generation AI receives a prompt containing instructions from the user about what the user wants the generation AI to do, and the generation AI generates the 3D model based on the prompt. The matching system matches 3D printer owners with people looking to create products based on the 3D model generated by the image generation AI. For example, 3D printer owners can register their printer's specifications, materials they are good at, and sizes they can create, allowing people looking to create products to find printer owners who meet their needs. The notification system notifies 3D printer owners who have been matched by the matching system. For example, after a match is established, data on the generated 3D model is sent to the 3D printer owner. Thus, the matching system according to an embodiment generates a 3D model based on the user's request, matches them with appropriate 3D printer owners, and notifies them, enabling efficient product creation.

[0030] Image generation AI can learn from a user's past design history and automatically generate proposals tailored to the user's preferences. For example, image generation AI can analyze a user's past design history and automatically generate design proposals based on the user's preferences. For example, it can learn the patterns and color usage of designs created in the past and generate a 3D model that reflects the user's preferred style. For past design history, it is necessary to clarify how the past design history is collected and stored. For example, the database format, storage period, etc. For suggestions tailored to preferences, it is necessary to clarify the specific criteria and methods for making suggestions tailored to preferences. For example, the user's past preference patterns, suggestion algorithm, etc. In this way, the system can learn from a user's past design history and automatically generate proposals tailored to their preferences, thereby improving user satisfaction.

[0031] Image generation AI can analyze a user's voice input and generate a 3D model based on the voice instruction. For example, if a user verbally instructs, "I want to make a round vase," the generation AI generates a 3D model based on that instruction. For voice input, the specific format of the voice input and the analysis method must be clarified. For example, the voice recognition technology and type of voice command must be clarified. For voice instructions, the specific content and format of the voice instruction must be clarified. For example, a list of voice commands and how the instructions are interpreted must be clarified. This improves user convenience by generating a 3D model based on the user's voice instruction.

[0032] The matching system can learn a user's past evaluation history and recommend the most suitable 3D printer owner. For example, the matching system can learn a user's past evaluation history and recommend the most suitable 3D printer owner. For example, it can prioritize the recommendation of 3D printer owners that have received high ratings in the past. For evaluation history, the specific collection and storage methods of the evaluation history must be made clear. For example, the evaluation criteria and storage period must be made clear. For recommendations, the specific criteria and methods for recommendations must be made clear. For example, an algorithm based on the evaluation history and a method for displaying recommendations must be made clear. This allows the system to learn a user's past evaluation history and recommend the most suitable 3D printer owner, improving the accuracy of matching.

[0033] The matching system can monitor the operating status of 3D printer owners in real time and recommend the owner who can respond most quickly. For example, the matching system can monitor the operating status of 3D printer owners in real time and recommend the owner who can respond most quickly. For example, it can give priority to recommending owners with printers that are currently in operation. For operating status, specific monitoring methods and evaluation criteria must be clarified. For example, operating rate, operating hours, and methods for obtaining real-time data. This improves the efficiency of matching by monitoring the operating status of 3D printer owners in real time and recommending owners who can respond quickly.

[0034] The matching system can incorporate experts from different industries and recommend 3D printer owners with specialized knowledge. For example, the matching system could incorporate experts from different industries and recommend 3D printer owners with specialized knowledge. For example, it could recommend someone with expertise in the medical field. For experts from different industries, the specific definition and selection criteria for each expert must be clarified. For example, the type of industry and the content of the expertise. For specialized knowledge, the specific content and evaluation criteria must be clarified. For example, technical knowledge, industry-specific knowledge, etc. This makes it possible to incorporate experts from different industries and recommend 3D printer owners with specialized knowledge, enabling the creation of more specialized products.

[0035] The matching system can recommend the nearest 3D printer owner based on the user's geographic location information. For example, the matching system can recommend the nearest 3D printer owner based on the user's geographic location information. For example, the matching system can recommend the nearest owner based on the user's address. Regarding geographic location information, the specific collection and usage methods of the geographic location information must be clarified. For example, GPS data, location information accuracy, etc. For closest, the specific criteria and evaluation methods for closest must be clarified. For example, the method of measuring distance, consideration of transportation means, etc. This can reduce delivery costs and time by recommending the nearest 3D printer owner based on the user's geographic location information.

[0036] Image generation AI can automatically generate multiple revision suggestions based on user feedback and provide the user with options. For example, if a user provides feedback such as "Make it a little lower," the generation AI will generate multiple height revision suggestions. The specific content of the revision suggestions and the generation method must be made clear, such as the type of revision and the generation algorithm. The specific content of the options and the display method must be made clear, such as the number of options and the display format. This allows multiple revision suggestions to be automatically generated based on user feedback and provided as options, improving user satisfaction.

[0037] Image generation AI can receive feedback from different devices (smartphones, tablets, etc.) and modify the 3D model. Image generation AI can receive feedback from different devices, such as smartphones and tablets, and modify the 3D model. For example, if a user provides feedback from their smartphone saying, "Make it a little lower," the generation AI modifies the 3D model based on that instruction. For different devices, it is necessary to clarify the specific types of devices and how to support them. For example, smartphones, tablets, PCs, etc. This allows for feedback from different devices to be received and the 3D model to be modified, improving user convenience.

[0038] Image generation AI can provide a visual simulation of proposed revisions based on user feedback, allowing the user to visually confirm the results. For example, if a user provides feedback such as "Make it a little lower," the generation AI will display a visual simulation of that proposed revision. For visual simulations, the specific content and implementation method of the visual simulation must be made clear. For example, the type of simulation, display format, etc. This allows the user to visually confirm a visual simulation of proposed revisions based on user feedback, improving user satisfaction.

[0039] The notification system can analyze the schedule of the 3D printer owner and send notifications at the optimal time. For example, the notification system can analyze the schedule of the 3D printer owner and send notifications at the optimal time. For example, notifications can be sent during times when the owner is free. For schedules, the specific collection and analysis methods for the schedule must be clarified. For example, calendar information, task management data, etc. For optimal timing, the specific criteria and evaluation methods for the optimal timing must be clarified. For example, task priority, required time, etc. This allows the system to analyze the schedule of the 3D printer owner and send notifications at the optimal time, thereby achieving efficient work.

[0040] The notification system can learn the past creation history of 3D printer owners and provide advice on creation. For example, the notification system can learn the past creation history of 3D printer owners and provide advice on creation. For example, advice can be given based on creation methods that have been successful in the past. The specific collection and storage methods for creation history must be made clear. For example, the creation date and time, the creation content, etc. The specific content of advice and how it is provided must be made clear. For example, technical advice, suggestions for work procedures, etc. In this way, the notification system can learn the past creation history of 3D printer owners and provide advice on creation, improving the accuracy and efficiency of creation.

[0041] The notification system can reliably send notifications using different communication methods. For example, the notification system can reliably send notifications using different communication methods (email, SMS, app notification, etc.). For example, if the owner does not check their email, a notification can be sent via SMS. For different communication methods, the specific type and usage method of each method must be made clear. For example, email, SMS, app notification, etc. This ensures that notifications are sent using different communication methods, preventing 3D printer owners from missing notifications.

[0042] The notification system can suggest the optimal delivery method based on the geographic location information of the 3D printer owner. For example, the notification system can suggest the optimal delivery method based on the geographic location information of the 3D printer owner. For example, it can suggest the fastest and most economical delivery method based on the owner's address. For geographic location information, the specific methods for collecting and using the geographic location information must be clarified. For example, GPS data, accuracy of location information, etc. For delivery methods, the specific type of delivery method and selection criteria must be clarified. For example, courier service, mail, drone delivery, etc. This can reduce delivery costs and time by suggesting the optimal delivery method based on the geographic location information of the 3D printer owner.

[0043] The feedback system can learn from the user's past rating history and make suggestions to improve the quality of the feedback. For example, the feedback system can learn from the user's past rating history and make suggestions to improve the quality of the feedback. For example, it can learn the patterns of feedback that have received high ratings in the past and make suggestions to the user. For rating history, it is necessary to clarify the specific methods for collecting and storing the rating history. For example, the rating criteria and storage period. For feedback quality, it is necessary to clarify the specific evaluation criteria and methods for improving the quality of the feedback. For example, the accuracy and detail of the feedback. In this way, the system can learn from the user's past rating history and make suggestions to improve the quality of the feedback, thereby improving the accuracy and usefulness of the feedback.

[0044] A feedback system can incorporate different evaluation criteria and conduct a comprehensive evaluation. For example, a feedback system can incorporate different evaluation criteria (technical accuracy, aesthetics of design, etc.) and conduct a comprehensive evaluation. For example, a comprehensive evaluation of technical accuracy and aesthetics of design can be conducted. The specific content and application method of the evaluation criteria must be made clear. For example, technical accuracy, aesthetics of design, etc. Comprehensive evaluation requires that the specific criteria and methods for comprehensive evaluation be made clear. For example, weighting of multiple evaluation criteria, methods for integrating evaluations, etc. In this way, by incorporating different evaluation criteria and conducting a comprehensive evaluation, the accuracy and reliability of the evaluation can be improved.

[0045] The feedback system can analyze regional evaluation trends based on the user's geographical location information. For example, the feedback system may analyze regional evaluation trends based on the user's geographical location information. For example, it may analyze designs that have received high ratings in a specific region. For geographical location information, it is necessary to clarify the specific collection and usage methods of the geographical location information. For example, GPS data, location information accuracy, etc. For regional evaluation trends, it is necessary to clarify the specific analysis methods and standards for regional evaluation trends. For example, the collection method of regional evaluation data, analysis algorithms, etc. This makes it possible to respond to regional needs by analyzing regional evaluation trends based on the user's geographical location information.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The matching system can also recommend 3D printer owners who share the same hobbies as the user based on their hobbies and interests. For example, if a user is interested in making figurines, it will recommend 3D printer owners who are also good at making figurines. Hobbies and interests are collected from the user's profile information and past activity history. This makes it possible to match users with 3D printer owners who share the same hobbies, which is expected to facilitate smooth communication. Matching based on hobbies and interests also has the effect of improving user satisfaction.

[0048] Image generation AI can also take into account a user's cultural background and propose designs that are culturally appropriate. For example, a user who likes traditional Japanese designs can be proposed a Japanese-style design. Cultural background is collected from the user's profile information and past design history. This makes it possible to propose designs that are tailored to the user's cultural background, thereby improving user satisfaction. Furthermore, culturally appropriate design proposals lead to users feeling that their culture is respected.

[0049] Image generation AI can also take into account the user's health condition and make health-conscious design suggestions. For example, it can suggest designs that are easy to use for long periods of time and designs that are visually relaxing. Health condition is collected from the user's profile information and past feedback. This makes it possible to suggest designs that take the user's health condition into consideration, thereby improving user satisfaction. Health-conscious design suggestions also lead to users feeling that their health is being valued.

[0050] The matching system can also learn the user's past evaluation history and recommend the most suitable 3D printer owner. For example, it will prioritize recommendations for 3D printer owners who have received high ratings in the past. The evaluation history is collected from the user's past matching results and feedback. This enables matching based on the user's past evaluation history, improving matching accuracy. Furthermore, recommendations based on past evaluation history can lead to users being matched with owners they can trust.

[0051] The matching system can monitor the operating status of 3D printer owners in real time and recommend the owner who can respond most quickly. For example, it can give priority to recommending owners whose printers are currently in operation. Operating status is collected from the operating data of the owner's printer. This makes it possible to match based on real-time operating status, improving matching efficiency. Furthermore, recommending owners who can respond quickly means that users can receive their products more quickly.

[0052] The matching system can also incorporate experts from different industries to recommend 3D printer owners with specialized knowledge. For example, it can recommend 3D printer owners with expertise in the medical field. Experts from different industries are collected from the owner's profile information and past creation history. This allows for matching based on expertise in different industries, enabling more specialized product creation. Recommending 3D printer owners with specialized knowledge also leads to users receiving high-quality products.

[0053] The matching system can also recommend the nearest 3D printer owner based on the user's geographic location information. For example, it can recommend the nearest owner based on the user's address. Geographic location information is collected from the user's GPS data and address information. This enables matching based on geographic location information, reducing delivery costs and time. Recommending the nearest owner also allows the user to receive their product more quickly.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The image generation AI generates a 3D model based on the user's request. For example, if the user requests, "I want a pen holder shaped like a cat," the image generation AI generates a 3D model of a cat-shaped pen holder based on that request. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a 3D model based on that prompt. Step 2: The matching system matches 3D printer owners with people who want to make products based on the 3D models generated by image generation AI. For example, people who own 3D printers can register their printer's specifications, materials it is good at, and sizes it can make, allowing people who want to make products to find people who own printers that meet their needs. Step 3: The notification system notifies the 3D printer owner who has been matched by the matching system. For example, after the match is established, the generated 3D model data is sent to the 3D printer owner.

[0056] (Example 2) A matching system according to an embodiment of the present invention matches people who own a home 3D printer with people who want to create their own unique products. This system uses image generation AI to facilitate the recognition and alignment of detailed specifications, aiming to create products that satisfy both parties. This allows the matching system to efficiently match people who own a home 3D printer with people who want to create their own unique products, enabling the creation of products that satisfy both parties.

[0057] A matching system according to an embodiment includes an image generation AI, a matching system, and a notification system. The image generation AI generates a 3D model based on a user's request. For example, if a user requests a "cat-shaped pen holder," the image generation AI generates a 3D model of a cat-shaped pen holder based on the request. The generation AI receives a prompt containing instructions from the user about what the user wants the generation AI to do, and the generation AI generates the 3D model based on the prompt. The matching system matches 3D printer owners with people looking to create products based on the 3D model generated by the image generation AI. For example, 3D printer owners can register their printer's specifications, materials they are good at, and sizes they can create, allowing people looking to create products to find printer owners who meet their needs. The notification system notifies 3D printer owners who have been matched by the matching system. For example, after a match is established, data on the generated 3D model is sent to the 3D printer owner. Thus, the matching system according to an embodiment generates a 3D model based on the user's request, matches them with appropriate 3D printer owners, and notifies them, enabling efficient product creation.

[0058] Image generation AI can learn from a user's past design history and automatically generate proposals tailored to the user's preferences. For example, image generation AI can analyze a user's past design history and automatically generate design proposals based on the user's preferences. For example, it can learn the patterns and color usage of designs created in the past and generate a 3D model that reflects the user's preferred style. For past design history, it is necessary to clarify how the past design history is collected and stored. For example, the database format, storage period, etc. For suggestions tailored to preferences, it is necessary to clarify the specific criteria and methods for making suggestions tailored to preferences. For example, the user's past preference patterns, suggestion algorithm, etc. In this way, the system can learn from a user's past design history and automatically generate proposals tailored to their preferences, thereby improving user satisfaction.

[0059] Image generation AI can analyze a user's voice input and generate a 3D model based on the voice instruction. For example, if a user verbally instructs, "I want to make a round vase," the generation AI generates a 3D model based on that instruction. For voice input, the specific format of the voice input and the analysis method must be clarified. For example, the voice recognition technology and type of voice command must be clarified. For voice instructions, the specific content and format of the voice instruction must be clarified. For example, a list of voice commands and how the instructions are interpreted must be clarified. This improves user convenience by generating a 3D model based on the user's voice instruction.

[0060] Image generation AI can use emotion estimation to analyze a user's emotional state and make design suggestions that elicit positive emotions. For example, image generation AI can use emotion estimation to analyze a user's emotional state and make design suggestions that elicit positive emotions. For example, if a user feels like relaxing, the generation AI can suggest designs that have a relaxing effect. The emotion estimation function requires clarification of the specific technology and method used, such as facial expression recognition, voice analysis, or text analysis. The emotional state requires clarification of the specific classification and evaluation criteria for the emotional state, such as positive, negative, or neutral. The design proposal requires clarification of the specific content and format of the design proposal, such as the type of proposal and how it is displayed. This allows the system to analyze a user's emotional state and make design suggestions that elicit positive emotions, thereby improving user satisfaction.

[0061] The matching system can learn a user's past evaluation history and recommend the most suitable 3D printer owner. For example, the matching system can learn a user's past evaluation history and recommend the most suitable 3D printer owner. For example, it can prioritize the recommendation of 3D printer owners that have received high ratings in the past. For evaluation history, the specific collection and storage methods of the evaluation history must be made clear. For example, the evaluation criteria and storage period must be made clear. For recommendations, the specific criteria and methods for recommendations must be made clear. For example, an algorithm based on the evaluation history and a method for displaying recommendations must be made clear. This allows the system to learn a user's past evaluation history and recommend the most suitable 3D printer owner, improving the accuracy of matching.

[0062] The matching system can monitor the operating status of 3D printer owners in real time and recommend the owner who can respond most quickly. For example, the matching system can monitor the operating status of 3D printer owners in real time and recommend the owner who can respond most quickly. For example, it can give priority to recommending owners with printers that are currently in operation. For operating status, specific monitoring methods and evaluation criteria must be clarified. For example, operating rate, operating hours, and methods for obtaining real-time data. This improves the efficiency of matching by monitoring the operating status of 3D printer owners in real time and recommending owners who can respond quickly.

[0063] The matching system can use an emotion estimation function to analyze the user's emotional state and recommend 3D printer owners who are emotionally compatible. For example, the matching system can use the emotion estimation function to analyze the user's emotional state and recommend 3D printer owners who are emotionally compatible. For example, if the user is relaxed, it can recommend owners who are similarly relaxed. The emotion estimation function requires clarification of the specific technology and method used, such as facial expression recognition, voice analysis, or text analysis. The emotional state requires clarification of specific classifications and evaluation criteria, such as positive, negative, or neutral. This allows the system to analyze the user's emotional state and recommend 3D printer owners who are emotionally compatible, thereby improving matching satisfaction.

[0064] The matching system can incorporate experts from different industries and recommend 3D printer owners with specialized knowledge. For example, the matching system could incorporate experts from different industries and recommend 3D printer owners with specialized knowledge. For example, it could recommend someone with expertise in the medical field. For experts from different industries, the specific definition and selection criteria for each expert must be clarified. For example, the type of industry and the content of the expertise. For specialized knowledge, the specific content and evaluation criteria must be clarified. For example, technical knowledge, industry-specific knowledge, etc. This makes it possible to incorporate experts from different industries and recommend 3D printer owners with specialized knowledge, enabling the creation of more specialized products.

[0065] The matching system can recommend the nearest 3D printer owner based on the user's geographic location information. For example, the matching system can recommend the nearest 3D printer owner based on the user's geographic location information. For example, the matching system can recommend the nearest owner based on the user's address. Regarding geographic location information, the specific collection and usage methods of the geographic location information must be clarified. For example, GPS data, location information accuracy, etc. For closest, the specific criteria and evaluation methods for closest must be clarified. For example, the method of measuring distance, consideration of transportation means, etc. This can reduce delivery costs and time by recommending the nearest 3D printer owner based on the user's geographic location information.

[0066] The matching system can use the emotion estimation function to analyze the emotions of users when using the matching system and make matching suggestions that elicit positive emotions. For example, the matching system can use the emotion estimation function to analyze the emotions of users when using the matching system and make matching suggestions that elicit positive emotions. For example, if the user is feeling anxious, the system can recommend users who will give the user a sense of security. The emotion estimation function requires clarification of the specific technology and method of the emotion estimation function, such as facial expression recognition, voice analysis, and text analysis. For emotions, the specific classification and evaluation criteria of emotions must be clarified, such as positive, negative, and neutral. This allows the system to analyze the user's emotions and make matching suggestions that elicit positive emotions, thereby improving user satisfaction.

[0067] Image generation AI can automatically generate multiple revision suggestions based on user feedback and provide the user with options. For example, if a user provides feedback such as "Make it a little lower," the generation AI will generate multiple height revision suggestions. The specific content of the revision suggestions and the generation method must be made clear, such as the type of revision and the generation algorithm. The specific content of the options and the display method must be made clear, such as the number of options and the display format. This allows multiple revision suggestions to be automatically generated based on user feedback and provided as options, improving user satisfaction.

[0068] Image generation AI can use emotion estimation to analyze the user's emotions when providing feedback and propose revisions that are emotionally satisfying. For example, image generation AI can use emotion estimation to analyze the user's emotions when providing feedback and propose revisions that are emotionally satisfying. For example, if the user is feeling dissatisfied, it can propose revisions that resolve that dissatisfaction. The emotion estimation function requires clarification of the specific technology and method used. For example, facial expression recognition, voice analysis, text analysis, etc. For emotions, it is necessary to clarify the specific classification and evaluation criteria for emotions. For example, positive, negative, neutral, etc. This allows the AI ​​to analyze the user's emotions when providing feedback and propose revisions that are emotionally satisfying, thereby improving user satisfaction.

[0069] Image generation AI can receive feedback from different devices (smartphones, tablets, etc.) and modify the 3D model. Image generation AI can receive feedback from different devices, such as smartphones and tablets, and modify the 3D model. For example, if a user provides feedback from their smartphone saying, "Make it a little lower," the generation AI modifies the 3D model based on that instruction. For different devices, it is necessary to clarify the specific types of devices and how to support them. For example, smartphones, tablets, PCs, etc. This allows for feedback from different devices to be received and the 3D model to be modified, improving user convenience.

[0070] Image generation AI can provide a visual simulation of proposed revisions based on user feedback, allowing the user to visually confirm the results. For example, if a user provides feedback such as "Make it a little lower," the generation AI will display a visual simulation of that proposed revision. For visual simulations, the specific content and implementation method of the visual simulation must be made clear. For example, the type of simulation, display format, etc. This allows the user to visually confirm a visual simulation of proposed revisions based on user feedback, improving user satisfaction.

[0071] Image generation AI can use its emotion estimation function to analyze the emotions of users when they provide feedback and make revision suggestions that elicit positive emotions. For example, image generation AI can use its emotion estimation function to analyze the emotions of users when they provide feedback and make revision suggestions that elicit positive emotions. For example, if a user is feeling dissatisfied, it can make revision suggestions that resolve that dissatisfaction. The emotion estimation function requires clarification of the specific technology and method used. For example, facial expression recognition, voice analysis, text analysis, etc. For emotions, it is necessary to clarify the specific classification and evaluation criteria for emotions. For example, positive, negative, neutral, etc. This allows the AI ​​to analyze the emotions of users when they provide feedback and make revision suggestions that elicit positive emotions, thereby improving user satisfaction.

[0072] The notification system can analyze the schedule of the 3D printer owner and send notifications at the optimal time. For example, the notification system can analyze the schedule of the 3D printer owner and send notifications at the optimal time. For example, notifications can be sent during times when the owner is free. For schedules, the specific collection and analysis methods for the schedule must be clarified. For example, calendar information, task management data, etc. For optimal timing, the specific criteria and evaluation methods for the optimal timing must be clarified. For example, task priority, required time, etc. This allows the system to analyze the schedule of the 3D printer owner and send notifications at the optimal time, thereby achieving efficient work.

[0073] The notification system can learn the past creation history of 3D printer owners and provide advice on creation. For example, the notification system can learn the past creation history of 3D printer owners and provide advice on creation. For example, advice can be given based on creation methods that have been successful in the past. The specific collection and storage methods for creation history must be made clear. For example, the creation date and time, the creation content, etc. The specific content of advice and how it is provided must be made clear. For example, technical advice, suggestions for work procedures, etc. In this way, the notification system can learn the past creation history of 3D printer owners and provide advice on creation, improving the accuracy and efficiency of creation.

[0074] The notification system can use an emotion estimation function to analyze the emotional state of the 3D printer owner and send notifications that elicit positive emotions. For example, the notification system can use the emotion estimation function to analyze the emotional state of the 3D printer owner and send notifications that elicit positive emotions. For example, a notification can be sent if the owner is relaxed. The emotion estimation function requires clarification of the specific technology and method used, such as facial expression recognition, voice analysis, or text analysis. The emotional state requires clarification of specific classifications and evaluation criteria, such as positive, negative, or neutral. This allows the system to analyze the emotional state of the 3D printer owner and send notifications that elicit positive emotions, thereby improving the owner's work efficiency and satisfaction.

[0075] The notification system can reliably send notifications using different communication methods. For example, the notification system can reliably send notifications using different communication methods (email, SMS, app notification, etc.). For example, if the owner does not check their email, a notification can be sent via SMS. For different communication methods, the specific type and usage method of each method must be made clear. For example, email, SMS, app notification, etc. This ensures that notifications are sent using different communication methods, preventing 3D printer owners from missing notifications.

[0076] The notification system can suggest the optimal delivery method based on the geographic location information of the 3D printer owner. For example, the notification system can suggest the optimal delivery method based on the geographic location information of the 3D printer owner. For example, it can suggest the fastest and most economical delivery method based on the owner's address. For geographic location information, the specific methods for collecting and using the geographic location information must be clarified. For example, GPS data, accuracy of location information, etc. For delivery methods, the specific type of delivery method and selection criteria must be clarified. For example, courier service, mail, drone delivery, etc. This can reduce delivery costs and time by suggesting the optimal delivery method based on the geographic location information of the 3D printer owner.

[0077] The notification system can use an emotion estimation function to analyze the emotions of 3D printer owners when they receive notifications and send notifications that elicit positive emotions. For example, the notification system can use the emotion estimation function to analyze the emotions of 3D printer owners when they receive notifications and send notifications that elicit positive emotions. For example, a notification could be sent if the owner is relaxed. The emotion estimation function requires clarification of the specific technology and method used. For example, facial expression recognition, voice analysis, text analysis, etc. For emotions, the specific classification and evaluation criteria for emotions must be clarified. For example, positive, negative, neutral, etc. This allows the system to analyze the emotions of 3D printer owners when they receive notifications and send notifications that elicit positive emotions, thereby improving the owner's work efficiency and satisfaction.

[0078] The feedback system can learn from the user's past rating history and make suggestions to improve the quality of the feedback. For example, the feedback system can learn from the user's past rating history and make suggestions to improve the quality of the feedback. For example, it can learn the patterns of feedback that have received high ratings in the past and make suggestions to the user. For rating history, it is necessary to clarify the specific methods for collecting and storing the rating history. For example, the rating criteria and storage period. For feedback quality, it is necessary to clarify the specific evaluation criteria and methods for improving the quality of the feedback. For example, the accuracy and detail of the feedback. In this way, the system can learn from the user's past rating history and make suggestions to improve the quality of the feedback, thereby improving the accuracy and usefulness of the feedback.

[0079] The feedback system can use an emotion estimation function to analyze the user's emotions when providing feedback and make feedback suggestions that elicit positive emotions. For example, the feedback system can use the emotion estimation function to analyze the user's emotions when providing feedback and make feedback suggestions that elicit positive emotions. For example, if the user is feeling dissatisfied, the system can make feedback suggestions that resolve that dissatisfaction. The emotion estimation function requires clarification of the specific technology and method of the emotion estimation function, such as facial expression recognition, voice analysis, and text analysis. For emotions, the specific classification and evaluation criteria for emotions must be clarified, such as positive, negative, and neutral. This allows the system to analyze the user's emotions when providing feedback and make feedback suggestions that elicit positive emotions, thereby improving the quality of feedback.

[0080] A feedback system can incorporate different evaluation criteria and conduct a comprehensive evaluation. For example, a feedback system can incorporate different evaluation criteria (technical accuracy, aesthetics of design, etc.) and conduct a comprehensive evaluation. For example, a comprehensive evaluation of technical accuracy and aesthetics of design can be conducted. The specific content and application method of the evaluation criteria must be made clear. For example, technical accuracy, aesthetics of design, etc. Comprehensive evaluation requires that the specific criteria and methods for comprehensive evaluation be made clear. For example, weighting of multiple evaluation criteria, methods for integrating evaluations, etc. In this way, by incorporating different evaluation criteria and conducting a comprehensive evaluation, the accuracy and reliability of the evaluation can be improved.

[0081] The feedback system can analyze regional evaluation trends based on the user's geographical location information. For example, the feedback system may analyze regional evaluation trends based on the user's geographical location information. For example, it may analyze designs that have received high ratings in a specific region. For geographical location information, it is necessary to clarify the specific collection and usage methods of the geographical location information. For example, GPS data, location information accuracy, etc. For regional evaluation trends, it is necessary to clarify the specific analysis methods and standards for regional evaluation trends. For example, the collection method of regional evaluation data, analysis algorithms, etc. This makes it possible to respond to regional needs by analyzing regional evaluation trends based on the user's geographical location information.

[0082] The feedback system can use an emotion estimation function to analyze the emotions of a user when providing feedback and make feedback suggestions that elicit positive emotions. For example, the feedback system can use the emotion estimation function to analyze the emotions of a user when providing feedback and make feedback suggestions that elicit positive emotions. For example, if a user is feeling dissatisfied, the system can make feedback suggestions that resolve that dissatisfaction. The emotion estimation function requires clarification of specific technologies and methods for the emotion estimation function, such as facial expression recognition, voice analysis, and text analysis. For emotions, specific classifications and evaluation criteria for emotions must be clarified, such as positive, negative, and neutral. This allows the system to analyze the emotions of a user when providing feedback and make feedback suggestions that elicit positive emotions, thereby improving the quality of feedback.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The matching system can also recommend 3D printer owners who share the same hobbies as the user based on their hobbies and interests. For example, if a user is interested in making figurines, it will recommend 3D printer owners who are also good at making figurines. Hobbies and interests are collected from the user's profile information and past activity history. This makes it possible to match users with 3D printer owners who share the same hobbies, which is expected to facilitate smooth communication. Matching based on hobbies and interests also has the effect of improving user satisfaction.

[0085] Image generation AI can also take into account a user's cultural background and propose designs that are culturally appropriate. For example, a user who likes traditional Japanese designs can be proposed a Japanese-style design. Cultural background is collected from the user's profile information and past design history. This makes it possible to propose designs that are tailored to the user's cultural background, thereby improving user satisfaction. Furthermore, culturally appropriate design proposals lead to users feeling that their culture is respected.

[0086] Image generation AI can also take into account the user's health condition and make health-conscious design suggestions. For example, it can suggest designs that are easy to use for long periods of time and designs that are visually relaxing. Health condition is collected from the user's profile information and past feedback. This makes it possible to suggest designs that take the user's health condition into consideration, thereby improving user satisfaction. Health-conscious design suggestions also lead to users feeling that their health is being valued.

[0087] Image generation AI can also analyze a user's emotional state and suggest designs that elicit positive emotions. For example, if a user is feeling stressed, it can suggest designs that have a relaxing effect. Emotional states are analyzed from the user's facial expressions and voice. This makes it possible to suggest designs that match the user's emotional state, improving user satisfaction. Furthermore, design suggestions that elicit positive emotions lead to users feeling that their emotions are understood.

[0088] The matching system can also learn the user's past evaluation history and recommend the most suitable 3D printer owner. For example, it will prioritize recommendations for 3D printer owners who have received high ratings in the past. The evaluation history is collected from the user's past matching results and feedback. This enables matching based on the user's past evaluation history, improving matching accuracy. Furthermore, recommendations based on past evaluation history can lead to users being matched with owners they can trust.

[0089] The matching system can monitor the operating status of 3D printer owners in real time and recommend the owner who can respond most quickly. For example, it can give priority to recommending owners whose printers are currently in operation. Operating status is collected from the operating data of the owner's printer. This makes it possible to match based on real-time operating status, improving matching efficiency. Furthermore, recommending owners who can respond quickly means that users can receive their products more quickly.

[0090] The matching system can also use its emotion estimation function to analyze the user's emotional state and recommend 3D printer owners with emotional compatibility. For example, if the user is relaxed, it will recommend owners who are similarly relaxed. The emotional state is analyzed from the user's facial expressions and voice. This allows for matching that is tailored to the user's emotional state, improving matching satisfaction. Furthermore, recommending owners with emotional compatibility allows users to create products with peace of mind.

[0091] The matching system can also incorporate experts from different industries to recommend 3D printer owners with specialized knowledge. For example, it can recommend 3D printer owners with expertise in the medical field. Experts from different industries are collected from the owner's profile information and past creation history. This allows for matching based on expertise in different industries, enabling more specialized product creation. Recommending 3D printer owners with specialized knowledge also leads to users receiving high-quality products.

[0092] The matching system can also recommend the nearest 3D printer owner based on the user's geographic location information. For example, it can recommend the nearest owner based on the user's address. Geographic location information is collected from the user's GPS data and address information. This enables matching based on geographic location information, reducing delivery costs and time. Recommending the nearest owner also allows the user to receive their product more quickly.

[0093] The matching system can use its emotion estimation function to analyze the emotions of users when using the matching system and make matching suggestions that elicit positive emotions. For example, if a user is feeling anxious, it can recommend people who give them a sense of security. The user's emotional state is analyzed from their facial expressions and voice. This makes it possible to make matching suggestions that are tailored to the user's emotional state, thereby improving user satisfaction. Furthermore, matching suggestions that elicit positive emotions can help users create products with peace of mind.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The image generation AI generates a 3D model based on the user's request. For example, if the user requests, "I want a pen holder shaped like a cat," the image generation AI generates a 3D model of a cat-shaped pen holder based on that request. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates a 3D model based on that prompt. Step 2: The matching system matches 3D printer owners with people who want to make products based on the 3D models generated by image generation AI. For example, people who own 3D printers can register their printer's specifications, materials it is good at, and sizes it can make, allowing people who want to make products to find people who own printers that meet their needs. Step 3: The notification system notifies the 3D printer owner who has been matched by the matching system. For example, after the match is established, the generated 3D model data is sent to the 3D printer owner.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] 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).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 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. Image generation AI that generates 3D models based on user requests; A matching system that matches 3D printer owners with people who want to make products based on the 3D model generated by the image generation AI; A notification system that notifies the 3D printer owner who has been matched by the matching system. A system characterized by:

2. The image generation AI is Analyzing the user's voice input and generating the 3D model based on the voice instructions.

2. The system of claim 1.

3. The matching system includes: Monitor the operating status of the 3D printer owners in real time and recommend the 3D printer owners who can respond most quickly.

2. The system of claim 1.

4. The image generation AI is Analyze the user's feedback in real time and instantly modify the 3D model 2. The system of claim 1.

5. The notification system comprises: Analyze the schedule of the 3D printer owner and send the notification at the optimal time 2. The system of claim 1.

6. The image generation AI is Analyze the user's emotional state and propose designs that elicit positive emotions 2. The system of claim 1.

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