System

Abstract

An object of a system according to an embodiment is to provide a step-by-step guide by designing a craft work and selecting a material in accordance with a user's taste.SOLUTION: A system according to an embodiment includes a design proposal unit, a material selection unit, and a guide provision unit. A design proposal part analyzes the taste of a user and past work data and proposes the design of craft work. The material selection unit selects a material optimal for the design of the craft work proposed by the design proposal unit. The guide providing unit provides a step-by-step guide for creating a craft work using the material selected by the material selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately accommodate the design and material selection of craft items that suit the user's preferences, and there is room for improvement.

[0005] The system of the embodiment aims to design and select materials for craft projects that suit the user's tastes and provide step-by-step guidance. [Means for solving the problem]

[0006] The system according to the embodiment includes a design proposal unit, a material selection unit, and a guide provision unit. The design proposal unit analyzes a user's preferences and past craft work data to propose a craft work design. The material selection unit selects materials that are optimal for the craft work design proposed by the design proposal unit. The guide provision unit provides a step-by-step guide for creating a craft work using the materials selected by the material selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can design and select materials for craft projects that suit the user's tastes and provide step-by-step guidance. [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) The craft suggestion system according to an embodiment of the present invention uses AI to suggest creative handmade ideas and projects to craft enthusiasts and hobbyists. This allows the craft suggestion system to support users' creativity, accommodate a variety of craft genres, and provide ideas tailored to individual tastes.

[0029] A craft suggestion system according to an embodiment includes a design suggestion unit, a material selection unit, and a guide provision unit. The design suggestion unit analyzes a user's preferences and past craft data to propose a craft design. For example, the design suggestion unit generates a new design using a generation AI based on the user's past works and preferred style. The design suggestion unit generates a design using a prompt containing information about the user's preferences. The material selection unit selects optimal materials for the craft design proposed by the design suggestion unit. For example, the material selection unit suggests appropriate fabrics, threads, and decorations for a specific design using the generation AI. The material selection unit selects materials using a prompt containing information about the design. The guide provision unit provides a step-by-step guide for creating a craft using the materials selected by the material selection unit. For example, the guide provision unit provides detailed explanations of the creation steps by the generation AI and lists the tools and materials required for each step. The guide provision unit generates a guide using a prompt containing information about the creation steps by the generation AI. This enables the craft suggestion system to support the user's creativity. For example, even beginners can create their own creations by following step-by-step guides, bringing the joy of crafting to more people. Furthermore, suggestions tailored to individual tastes can maximize users' creativity.

[0030] The design suggestion unit can analyze the user's past failures, identify the causes of the failures, and propose designs that avoid them. For example, the generation AI analyzes the user's past failures, and if the failure was caused by using too much glue, the design suggestion unit generates a design that suggests an appropriate amount of glue. The design suggestion unit also generates a design that suggests an appropriate type of fabric to avoid mistakes in fabric selection based on the user's past failures. For example, the generation AI analyzes the characteristics of the fabric and selects an appropriate fabric. The design suggestion unit also generates a design that suggests detailed work procedures to avoid mistakes in work procedures based on the user's past failures. For example, the generation AI analyzes work procedures and proposes steps to avoid mistakes. This can improve the success rate by proposing designs that avoid the user's past failures.

[0031] The design suggestion unit can analyze the user's living environment and suggest designs that are optimal for that environment. For example, the generation AI in the design suggestion unit analyzes the interior of the user's room and suggests craft pieces with simple and sophisticated designs for modern interiors. The design suggestion unit also suggests craft pieces with designs using natural materials for natural interiors based on the user's living environment. For example, it generates designs using wood and linen. The design suggestion unit also suggests craft pieces with vibrant designs for colorful interiors based on the user's living environment. For example, it generates designs using colorful fabrics and decorations. This allows for improving practicality by suggesting designs that are optimal for the user's living environment.

[0032] The material selection unit can analyze the user's allergy information and suggest materials that do not cause allergies. For example, the generation AI analyzes the user's allergy information and suggests latex-free materials to a user with a latex allergy. The material selection unit also suggests wool-free materials to a user with a wool allergy based on the user's allergy information. For example, the generation AI selects an alternative material to wool. Furthermore, the material selection unit also suggests chemical-free materials to a user with a chemical allergy based on the user's allergy information. For example, the generation AI selects natural materials. This allows for improved safety by suggesting materials that take the user's allergies into consideration.

[0033] The material selection unit can consider the user's budget and suggest cost-effective materials. For example, the generation AI analyzes the user's budget and suggests inexpensive, high-quality materials to users with a low budget. The material selection unit also suggests cost-effective materials to users with a medium budget based on the user's budget. For example, the generation AI analyzes the price-to-quality ratio and selects the optimal material. Furthermore, the material selection unit also suggests high-quality, durable materials to users with a high budget based on the user's budget. For example, the generation AI selects materials taking long-term cost effectiveness into consideration. This allows for improved economic efficiency by suggesting materials that take the user's budget into consideration.

[0034] The material selection unit can analyze the climatic conditions of the user's region and suggest materials suitable for that climate. For example, the generation AI analyzes the climatic conditions of the user's region and suggests moisture-proof materials for humid regions. The material selection unit also suggests materials with high heat retention for cold regions based on the climatic conditions of the user's region. For example, the generation AI selects insulating materials. The material selection unit also suggests breathable materials for hot regions based on the climatic conditions of the user's region. For example, the generation AI selects breathable fabrics. This allows for improved practicality by suggesting materials suitable for the climatic conditions of the user's region.

[0035] The guide providing unit can analyze the user's skill level and provide a customized guide ranging from beginner to advanced. In the guide providing unit, for example, the generation AI analyzes the user's skill level and provides a guide that explains basic techniques to beginners. In addition, the guide providing unit provides a guide that explains applied techniques to intermediate users based on the user's skill level. For example, the generation AI suggests projects for intermediate users. In addition, the guide providing unit provides a guide that explains advanced techniques to advanced users based on the user's skill level. For example, the generation AI suggests complex projects. In this way, learning effectiveness can be improved by providing a guide customized according to the user's skill level.

[0036] The guide providing unit can analyze the user's work pace and provide a guide that matches the user's pace. For example, the generation AI of the guide providing unit analyzes the user's work pace and provides a guide that includes detailed explanations to users who work slowly. Furthermore, the guide providing unit provides a guide that includes concise explanations to users who work quickly, based on the user's work pace. For example, the generation AI analyzes the progress speed of the work and generates an optimal guide. Furthermore, the guide providing unit provides a guide that takes into account the frequency of breaks based on the user's work pace. For example, the generation AI analyzes the rhythm of the work and suggests appropriate break times. This allows for the provision of a guide that matches the user's work pace, thereby improving work efficiency.

[0037] The guide providing unit can analyze the user's work environment and provide a guide that is optimal for that environment. For example, the generation AI in the guide providing unit analyzes the user's work environment and provides a space-saving guide to a user working in a small space. The guide providing unit also provides a guide with detailed explanations to a user working in bright lighting conditions based on the user's work environment. For example, the generation AI analyzes lighting conditions and generates an optimal guide. Furthermore, the guide providing unit also provides a guide to increase concentration to a user working in an environment with a high noise level based on the user's work environment. For example, the generation AI analyzes noise levels and suggests an appropriate work method. This allows the user to improve work comfort by providing a guide that is optimal for the user's work environment.

[0038] The guide providing unit can combine guides from different craft genres to allow the user to learn new techniques. For example, the generation AI analyzes guides from different craft genres and provides a guide that combines knitting and embroidery. The guide providing unit also provides a guide that combines woodworking and paper crafts based on guides from different craft genres. For example, the generation AI analyzes techniques from different genres and suggests the optimal combination. Furthermore, the guide providing unit provides a guide that combines pottery and painting based on guides from different craft genres. For example, the generation AI analyzes the range of application of a technique and generates a guide to help the user learn a new technique. In this way, by combining guides from different craft genres, the user can learn a new technique.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The craft suggestion system can also analyze the user's health condition and make craft suggestions that take health into consideration. For example, the generation AI analyzes the user's health data and suggests craft projects that can be completed in a short amount of time for users who have difficulty working for long periods of time. The generation AI also uses the user's health data to suggest craft projects that can be completed with simple tasks for users who have difficulty with tasks that require manual dexterity. Furthermore, the generation AI uses the user's health data to suggest craft projects that use large parts for users with poor eyesight. In this way, craft suggestions that take the user's health condition into consideration allow more users to enjoy crafting.

[0041] The craft suggestion system can also analyze the user's past successful creations, identify the factors that contributed to their success, and propose designs that replicate similar success. For example, if the generative AI analyzes the user's past successful creations and determines that specific color usage or patterns were factors in their success, it will propose designs that include similar color usage or patterns. Furthermore, if the generative AI determines that specific materials or techniques were factors in their success based on the user's past successful creations, it will propose designs that use similar materials or techniques. Furthermore, if the generative AI determines that specific work procedures were factors in their success based on the user's past successful creations, it will propose designs that include similar work procedures. This allows the system to propose designs that replicate the user's past successes, thereby improving the success rate.

[0042] The craft suggestion system can further analyze the user's cultural background and suggest designs that match that culture. For example, the generation AI analyzes the user's cultural background and suggests Japanese-style craft pieces to a user who prefers Japanese-style designs. The generation AI also suggests Western-style craft pieces to a user who prefers Western-style designs, based on the user's cultural background. The generation AI also suggests ethnic craft pieces to a user who prefers ethnic designs, based on the user's cultural background. This makes it possible to improve user satisfaction by suggesting designs that match the user's cultural background.

[0043] The craft suggestion system can also analyze a user's hobbies and interests and make craft suggestions based on them. For example, the generation AI analyzes a user's hobbies and suggests gardening-related craft projects to a user who likes gardening. Similarly, based on the user's interests, the generation AI suggests kitchen craft projects to a user who likes cooking. Furthermore, based on the user's hobbies and interests, the generation AI suggests pet-related craft projects to a user who likes pets. This makes it possible to improve user satisfaction by making craft suggestions that match the user's hobbies and interests.

[0044] The craft suggestion system can also analyze the user's workspace and suggest crafts that are optimal for that space. For example, the generation AI analyzes the user's workspace and suggests space-saving crafts for users working in small spaces. The generation AI also suggests large crafts for users working in large spaces based on the user's workspace. Furthermore, the generation AI suggests crafts that can be enjoyed outdoors for users working outdoors based on the user's workspace. This makes work more comfortable by suggesting crafts that are tailored to the user's workspace.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The design suggestion unit analyzes the user's preferences and past work data to suggest designs for craft projects. For example, the design suggestion unit uses a generation AI to generate new designs based on the user's past work and preferred style. The design suggestion unit also uses a generation AI to generate designs based on prompts containing information about the user's preferences. Step 2: The material selection unit selects the best materials for the craft design proposed by the design proposal unit. For example, the material selection unit suggests appropriate fabrics, threads, and decorations for a specific design. The material selection unit also selects materials based on prompts provided by the generation AI that contain information about the design. Step 3: The guide providing unit provides a step-by-step guide for creating a craft item using the materials selected by the material selecting unit. For example, the guide providing unit may provide a detailed explanation of the steps for creating the item by the generating AI and list the tools and materials required for each step. The guide providing unit may also generate a guide based on prompts provided by the generating AI that include information about the steps.

[0047] (Example 2) The craft suggestion system according to an embodiment of the present invention uses AI to suggest creative handmade ideas and projects to craft enthusiasts and hobbyists. This allows the craft suggestion system to support users' creativity, accommodate a variety of craft genres, and provide ideas tailored to individual tastes.

[0048] A craft suggestion system according to an embodiment includes a design suggestion unit, a material selection unit, and a guide provision unit. The design suggestion unit analyzes a user's preferences and past craft data to propose a craft design. For example, the design suggestion unit generates a new design using a generation AI based on the user's past works and preferred style. The design suggestion unit generates a design using a prompt containing information about the user's preferences. The material selection unit selects optimal materials for the craft design proposed by the design suggestion unit. For example, the material selection unit suggests appropriate fabrics, threads, and decorations for a specific design using the generation AI. The material selection unit selects materials using a prompt containing information about the design. The guide provision unit provides a step-by-step guide for creating a craft using the materials selected by the material selection unit. For example, the guide provision unit provides detailed explanations of the creation steps by the generation AI and lists the tools and materials required for each step. The guide provision unit generates a guide using a prompt containing information about the creation steps by the generation AI. This enables the craft suggestion system to support the user's creativity. For example, even beginners can create their own creations by following step-by-step guides, bringing the joy of crafting to more people. Furthermore, suggestions tailored to individual tastes can maximize users' creativity.

[0049] The design suggestion unit can analyze the user's emotional data and suggest designs that match the user's mood. For example, the generation AI in the design suggestion unit analyzes the user's emotional data and suggests craft pieces with soft colors and simple designs to a user who feels like relaxing. For example, it generates a design based on blue and green. The design suggestion unit also uses the user's emotional data to suggest craft pieces with bright colors and complex designs to a user who is feeling energetic. For example, it generates a design based on red and orange. This makes it possible to improve user satisfaction by suggesting designs that match the user's mood.

[0050] The design suggestion unit can analyze the user's past failures, identify the causes of the failures, and propose designs that avoid them. For example, the generation AI analyzes the user's past failures, and if the failure was caused by using too much glue, the design suggestion unit generates a design that suggests an appropriate amount of glue. The design suggestion unit also generates a design that suggests an appropriate type of fabric to avoid mistakes in fabric selection based on the user's past failures. For example, the generation AI analyzes the characteristics of the fabric and selects an appropriate fabric. The design suggestion unit also generates a design that suggests detailed work procedures to avoid mistakes in work procedures based on the user's past failures. For example, the generation AI analyzes work procedures and proposes steps to avoid mistakes. This can improve the success rate by proposing designs that avoid the user's past failures.

[0051] The design suggestion unit can analyze the user's living environment and suggest designs that are optimal for that environment. For example, the generation AI in the design suggestion unit analyzes the interior of the user's room and suggests craft pieces with simple and sophisticated designs for modern interiors. The design suggestion unit also suggests craft pieces with designs using natural materials for natural interiors based on the user's living environment. For example, it generates designs using wood and linen. The design suggestion unit also suggests craft pieces with vibrant designs for colorful interiors based on the user's living environment. For example, it generates designs using colorful fabrics and decorations. This allows for improving practicality by suggesting designs that are optimal for the user's living environment.

[0052] The material selection unit can analyze the user's allergy information and suggest materials that do not cause allergies. For example, the generation AI analyzes the user's allergy information and suggests latex-free materials to a user with a latex allergy. The material selection unit also suggests wool-free materials to a user with a wool allergy based on the user's allergy information. For example, the generation AI selects an alternative material to wool. Furthermore, the material selection unit also suggests chemical-free materials to a user with a chemical allergy based on the user's allergy information. For example, the generation AI selects natural materials. This allows for improved safety by suggesting materials that take the user's allergies into consideration.

[0053] The material selection unit can consider the user's budget and suggest cost-effective materials. For example, the generation AI analyzes the user's budget and suggests inexpensive, high-quality materials to users with a low budget. The material selection unit also suggests cost-effective materials to users with a medium budget based on the user's budget. For example, the generation AI analyzes the price-to-quality ratio and selects the optimal material. Furthermore, the material selection unit also suggests high-quality, durable materials to users with a high budget based on the user's budget. For example, the generation AI selects materials taking long-term cost effectiveness into consideration. This allows for improved economic efficiency by suggesting materials that take the user's budget into consideration.

[0054] The material selection unit can analyze the climatic conditions of the user's region and suggest materials suitable for that climate. For example, the generation AI analyzes the climatic conditions of the user's region and suggests moisture-proof materials for humid regions. The material selection unit also suggests materials with high heat retention for cold regions based on the climatic conditions of the user's region. For example, the generation AI selects insulating materials. The material selection unit also suggests breathable materials for hot regions based on the climatic conditions of the user's region. For example, the generation AI selects breathable fabrics. This allows for improved practicality by suggesting materials suitable for the climatic conditions of the user's region.

[0055] The guide providing unit can analyze the user's skill level and provide a customized guide ranging from beginner to advanced. In the guide providing unit, for example, the generation AI analyzes the user's skill level and provides a guide that explains basic techniques to beginners. In addition, the guide providing unit provides a guide that explains applied techniques to intermediate users based on the user's skill level. For example, the generation AI suggests projects for intermediate users. In addition, the guide providing unit provides a guide that explains advanced techniques to advanced users based on the user's skill level. For example, the generation AI suggests complex projects. In this way, learning effectiveness can be improved by providing a guide customized according to the user's skill level.

[0056] The guide providing unit can analyze the user's work pace and provide a guide that matches the user's pace. For example, the generation AI of the guide providing unit analyzes the user's work pace and provides a guide that includes detailed explanations to users who work slowly. Furthermore, the guide providing unit provides a guide that includes concise explanations to users who work quickly, based on the user's work pace. For example, the generation AI analyzes the progress speed of the work and generates an optimal guide. Furthermore, the guide providing unit provides a guide that takes into account the frequency of breaks based on the user's work pace. For example, the generation AI analyzes the rhythm of the work and suggests appropriate break times. This allows for the provision of a guide that matches the user's work pace, thereby improving work efficiency.

[0057] The guide providing unit can analyze the user's work environment and provide a guide that is optimal for that environment. For example, the generation AI in the guide providing unit analyzes the user's work environment and provides a space-saving guide to a user working in a small space. The guide providing unit also provides a guide with detailed explanations to a user working in bright lighting conditions based on the user's work environment. For example, the generation AI analyzes lighting conditions and generates an optimal guide. Furthermore, the guide providing unit also provides a guide to increase concentration to a user working in an environment with a high noise level based on the user's work environment. For example, the generation AI analyzes noise levels and suggests an appropriate work method. This allows the user to improve work comfort by providing a guide that is optimal for the user's work environment.

[0058] The guide providing unit can combine guides from different craft genres to allow the user to learn new techniques. For example, the generation AI analyzes guides from different craft genres and provides a guide that combines knitting and embroidery. The guide providing unit also provides a guide that combines woodworking and paper crafts based on guides from different craft genres. For example, the generation AI analyzes techniques from different genres and suggests the optimal combination. Furthermore, the guide providing unit provides a guide that combines pottery and painting based on guides from different craft genres. For example, the generation AI analyzes the range of application of a technique and generates a guide to help the user learn a new technique. In this way, by combining guides from different craft genres, the user can learn a new technique.

[0059] The guide providing unit can use the emotion estimation function to analyze the emotional response of the user when following the guide and provide a guide that elicits the most positive response. For example, the guide providing unit can use the emotion estimation function to analyze the facial expression of the user when following the guide and provide a guide that elicits the most smiles. The guide providing unit can also use the emotion estimation function to analyze the voice of the user when following the guide and provide a guide that elicits the most positive response. For example, the generation AI can use voice analysis technology to analyze the tone and speed of the user's voice. Furthermore, the guide providing unit can use the emotion estimation function to analyze biometric data when the user follows the guide and provide a guide that elicits the most positive response. For example, the generation AI can analyze heart rate and electrodermal activity to evaluate the user's emotional response. This allows the system to analyze the user's emotional response and provide a guide that elicits the most positive response, thereby improving user satisfaction.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The craft suggestion system can also analyze the user's health condition and make craft suggestions that take health into consideration. For example, the generation AI analyzes the user's health data and suggests craft projects that can be completed in a short amount of time for users who have difficulty working for long periods of time. The generation AI also uses the user's health data to suggest craft projects that can be completed with simple tasks for users who have difficulty with tasks that require manual dexterity. Furthermore, the generation AI uses the user's health data to suggest craft projects that use large parts for users with poor eyesight. In this way, craft suggestions that take the user's health condition into consideration allow more users to enjoy crafting.

[0062] The craft suggestion system can also estimate the user's emotions and adjust the difficulty of the craft based on the estimated emotions. For example, the generation AI analyzes the user's emotional data and suggests simple craft projects that are relaxing for a user who is feeling stressed. The generation AI also uses the user's emotional data to suggest challenging craft projects for a user who is feeling enjoyment. The generation AI also uses the user's emotional data to suggest craft projects that require detailed work for a user who is experiencing heightened concentration. This makes it possible to improve user satisfaction by suggesting craft projects that match the user's emotions.

[0063] The craft suggestion system can also analyze the user's past successful creations, identify the factors that contributed to their success, and propose designs that replicate similar success. For example, if the generative AI analyzes the user's past successful creations and determines that specific color usage or patterns were factors in their success, it will propose designs that include similar color usage or patterns. Furthermore, if the generative AI determines that specific materials or techniques were factors in their success based on the user's past successful creations, it will propose designs that use similar materials or techniques. Furthermore, if the generative AI determines that specific work procedures were factors in their success based on the user's past successful creations, it will propose designs that include similar work procedures. This allows the system to propose designs that replicate the user's past successes, thereby improving the success rate.

[0064] The craft suggestion system can further estimate the user's emotions and suggest craft themes based on the estimated emotions. For example, the generation AI analyzes the user's emotional data and suggests craft pieces with a bright and fun theme to a user who is feeling happy. The generation AI can also use the user's emotional data to suggest craft pieces with a healing or encouraging theme to a user who is feeling depressed. The generation AI can also use the user's emotional data to suggest craft pieces with an energetic theme to an excited user. This can improve user satisfaction by suggesting craft pieces with themes that match the user's emotions.

[0065] The craft suggestion system can further analyze the user's cultural background and suggest designs that match that culture. For example, the generation AI analyzes the user's cultural background and suggests Japanese-style craft pieces to a user who prefers Japanese-style designs. The generation AI also suggests Western-style craft pieces to a user who prefers Western-style designs, based on the user's cultural background. The generation AI also suggests ethnic craft pieces to a user who prefers ethnic designs, based on the user's cultural background. This makes it possible to improve user satisfaction by suggesting designs that match the user's cultural background.

[0066] The craft suggestion system can further estimate the user's emotions and suggest craft colors based on the estimated emotions. For example, the generation AI analyzes the user's emotional data and suggests craft pieces with blue and green colors to a user who wants to relax. Similarly, the generation AI, based on the user's emotional data, suggests craft pieces with red and orange colors to a user who is feeling energetic. Furthermore, the generation AI, based on the user's emotional data, suggests craft pieces with pastel colors to a user who wants to calm down. This makes it possible to improve user satisfaction by suggesting craft pieces with colors that match the user's emotions.

[0067] The craft suggestion system can also analyze a user's hobbies and interests and make craft suggestions based on them. For example, the generation AI analyzes a user's hobbies and suggests gardening-related craft projects to a user who likes gardening. Similarly, based on the user's interests, the generation AI suggests kitchen craft projects to a user who likes cooking. Furthermore, based on the user's hobbies and interests, the generation AI suggests pet-related craft projects to a user who likes pets. This makes it possible to improve user satisfaction by making craft suggestions that match the user's hobbies and interests.

[0068] The craft suggestion system can also estimate the user's emotions and adjust the crafting time based on the estimated emotions. For example, the generation AI analyzes the user's emotional data and suggests crafts that can be completed in a short time for a tired user. The generation AI also uses the user's emotional data to suggest crafts that take a long time to complete for a user who is highly focused. Furthermore, the generation AI uses the user's emotional data to suggest crafts with an appropriate work time for a user who wants to relax. In this way, user satisfaction can be improved by suggesting crafting times that match the user's emotions.

[0069] The craft suggestion system can also analyze the user's workspace and suggest crafts that are optimal for that space. For example, the generation AI analyzes the user's workspace and suggests space-saving crafts for users working in small spaces. The generation AI also suggests large crafts for users working in large spaces based on the user's workspace. Furthermore, the generation AI suggests crafts that can be enjoyed outdoors for users working outdoors based on the user's workspace. This makes work more comfortable by suggesting crafts that are tailored to the user's workspace.

[0070] The craft suggestion system can further estimate the user's emotions and suggest a craft theme song based on the estimated emotions. For example, the generation AI analyzes the user's emotional data and suggests relaxing music to a user who feels like relaxing. The generation AI also suggests up-tempo music to a user who feels energetic based on the user's emotional data. The generation AI also suggests music that improves concentration to a user who feels like concentrating based on the user's emotional data. This makes it possible to improve user satisfaction by suggesting theme songs for craft that match the user's emotions.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The design suggestion unit analyzes the user's preferences and past work data to suggest designs for craft projects. For example, the design suggestion unit uses a generation AI to generate new designs based on the user's past work and preferred style. The design suggestion unit also uses a generation AI to generate designs based on prompts containing information about the user's preferences. Step 2: The material selection unit selects the best materials for the craft design proposed by the design proposal unit. For example, the material selection unit suggests appropriate fabrics, threads, and decorations for a specific design. The material selection unit also selects materials based on prompts provided by the generation AI that contain information about the design. Step 3: The guide providing unit provides a step-by-step guide for creating a craft item using the materials selected by the material selecting unit. For example, the guide providing unit may provide a detailed explanation of the steps for creating the item by the generating AI and list the tools and materials required for each step. The guide providing unit may also generate a guide based on prompts provided by the generating AI that include information about the steps.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. The device includes a design suggestion unit that analyzes a user's preferences and past work data and proposes a design for a craft work, a material selection unit that selects the most suitable material for the design of the craft work proposed by the design suggestion unit, and a guide provision unit that provides a step-by-step guide for creating a craft work using the material selected by the material selection unit. A system characterized by:

2. The design proposal section Analyzing the user's emotional data and proposing a design that matches the user's mood 2. The system of claim 1.

3. The material selection unit Analyze the user's allergy information and suggest ingredients that do not cause allergies 2. The system of claim 1.

4. The material selection unit Analyze the climatic conditions of the user's region and suggest materials suitable for that climate 2. The system of claim 1.

5. The guide providing unit Analyze the user's skill level and provide customized guides ranging from beginner to advanced.

2. The system of claim 1.

6. The guide providing unit Analyzing the user's work pace and providing a guide that matches the user's pace 2. The system of claim 1.

7. The guide providing unit Analyze the user's work environment and provide the optimal guide for that environment 2. The system of claim 1.

8. The guide providing unit Analyzing the emotional response of the user when the user executes the guide and providing the guide that elicits the most positive response 2. The system of claim 1.

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