system

Abstract

The system according to this embodiment aims to provide a customized doll based on the user's wishes, allowing them to relieve stress in a fun and safe way. [Solution] The system according to the embodiment comprises a design unit, a generation unit, a reaction unit, and an optimization unit. The design unit understands the user's wishes and designs the appearance. The generation unit creates a new appearance based on the appearance designed by the design unit and integrates an AI agent into it. The reaction unit provides the user with multiple reaction options for the doll generated by the generation unit. The optimization unit learns from user feedback based on the reactions provided by the reaction unit and optimizes the reactions.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the options for stress divergence methods are limited and it is difficult to express one's own feelings comfortably.

[0005] The system according to the embodiment provides a customized humanoid based on the user's wishes and aims to diverge stress in a fun and safe manner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a design unit, a generation unit, a reaction unit, and an optimization unit. The design unit understands the user's wishes and designs the appearance. The generation unit creates a new appearance based on the appearance designed by the design unit and integrates an AI agent into it. The reaction unit provides the user with multiple reaction options for the doll generated by the generation unit. The optimization unit learns from user feedback based on the reactions provided by the reaction unit and optimizes the reactions. [Effects of the Invention]

[0007] The system according to this embodiment provides a doll customized based on the user's wishes, allowing them to relieve stress in a fun and safe way. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The stress relief support system according to an embodiment of the present invention is a system that supports stress relief by generating a customized doll according to the user's wishes using a generative AI. This system understands the user's wishes and designs the appearance. Next, a new appearance is created using a 3D printer and an AI agent is integrated into it. The AI ​​agent provides multiple response options that the user can choose from. For example, there are modes such as weak objection, apologizing, and simply brushing it off. The generative AI continuously learns from the user's feedback and optimizes its responses. This provides a fun and safe space to release stress and realizes a unique and personalized emotional release. It also provides a new method and option in mental healthcare. For example, the system understands the user's wishes in detail through dialogue and designs an appearance according to individual needs. Next, a new appearance is created using a 3D printer and an AI agent is integrated into it. The AI ​​agent provides multiple response options that the user can choose from. For example, there are modes such as weak objection, apologizing, and simply brushing it off. This allows the user to choose a response that suits their emotions and release stress. The generative AI continuously learns from the user's feedback and optimizes its responses. This allows for the provision of optimal responses tailored to user needs. For example, if a user prefers a particular response, the system will be optimized to prioritize that response. This improves user satisfaction and enhances the effectiveness of stress relief. This provides a fun and safe space for stress relief. Users can confidently express their emotions and achieve a unique and personalized emotional release. It also offers new methods and options in mental healthcare. This allows the stress relief support system to generate customized dolls according to the user's wishes and support stress relief.

[0029] The stress relief support system according to this embodiment comprises a design unit, a generation unit, a reaction unit, and an optimization unit. The design unit understands the user's wishes and designs the appearance. The design unit, for example, understands the wishes in detail through dialogue with the user and designs an appearance that meets individual needs. The design unit can understand the user's wishes and design the appearance using generation AI. The generation unit creates a new appearance based on the appearance designed by the design unit and integrates an AI agent into it. The generation unit, for example, creates a new appearance using a 3D printer and integrates an AI agent into it. The generation unit can create a new appearance and integrate an AI agent into it using generation AI. The reaction unit provides multiple reaction options that the user can choose from in the doll generated by the generation unit. The reaction unit provides multiple reaction options such as a weak objection, an apologizing mode, and a mode that simply brushes things off. The reaction unit can provide multiple reaction options that the user can choose from using AI. The optimization unit learns feedback from the user based on the reactions provided by the reaction unit and optimizes the reactions. The optimization unit, for example, continuously learns from user feedback using a generating AI and optimizes its response. The optimization unit can learn from user feedback using the generating AI and optimize its response. As a result, the stress relief support system according to the embodiment can generate a customized doll according to the user's wishes and support stress relief.

[0030] The design department understands user preferences and designs the appearance. For example, the design department understands preferences in detail through dialogue with users and designs an appearance that meets individual needs. Specifically, the design department collects detailed preferences such as color, shape, size, and material through dialogue with users. They conduct detailed interviews to understand what kind of design users prefer and what functions they are looking for, and then design based on that. The design department can understand user preferences and design the appearance using generative AI. Generative AI analyzes user preferences using natural language processing technology and accurately understands the user's intentions. For example, if a user wants a "relaxing design," the generative AI analyzes that intention and suggests colors and shapes that enhance the relaxing effect. The generative AI also refers to past design data and trend information to generate the optimal design for the user's preferences. This allows the design department to quickly and accurately design a customized appearance that meets user preferences. Furthermore, the design department can revise and adjust the design through dialogue with users. For example, if a user wants to change part of the design, the design department revise the design according to that request and finalize the design. This allows the design department to create an appearance that perfectly matches the user's wishes.

[0031] The generation unit creates a new appearance based on the appearance designed by the design unit and integrates an AI agent within it. For example, the generation unit can create a new appearance using a 3D printer and integrate an AI agent within it. Specifically, the generation unit physically generates the appearance using a 3D printer based on the design data provided by the design unit. The 3D printer uses high-precision printing technology to create an appearance faithful to the design data. The generation unit can use generation AI to create a new appearance and integrate an AI agent within it. The generation AI analyzes the design data and sets optimal printing parameters. For example, it optimizes material selection, printing speed, and layer thickness to produce a high-quality appearance. The generation AI also manages the AI ​​agent integration process. Since the AI ​​agent is responsible for user interaction and responses, it needs to be appropriately positioned within the appearance. The generation AI optimizes the placement and connection of the AI ​​agent, integrating the appearance and the AI ​​agent so that they become one. This allows the generation unit to generate the appearance designed by the design unit with high precision and integrate the AI ​​agent. Furthermore, the production unit performs quality control of the production process, ensuring that the generated appearance is faithful to the design data. For example, it inspects the dimensions and shape of the generated appearance and makes corrections as needed. This allows the production unit to produce high-quality appearances and provide customized dolls that meet the user's needs.

[0032] The response unit provides multiple response options for the user to choose from in the doll generated by the generation unit. For example, the response unit offers several response options such as a weak objection mode, an apologetic mode, and a mode that simply brushes off the conversation. Specifically, the response unit selects and provides an appropriate response based on the user's input. The response unit can use AI to provide multiple response options for the user to choose from. The AI ​​analyzes the user's input and selects the optimal response. For example, if the user is feeling stressed, the AI ​​analyzes the situation and provides an appropriate response option. In weak objection mode, the AI ​​gently objectes to the user's opinion to encourage discussion. In apologetic mode, the AI ​​apologizes to the user to ease their feelings. In brush-off mode, the AI ​​brushes off what the user is saying, allowing the user to focus on speaking. This allows the response unit to provide appropriate responses tailored to the user's situation and feelings, supporting stress relief. Furthermore, the response unit can collect user responses and feedback to continuously improve the accuracy and effectiveness of the response options. For example, it can evaluate whether the user is satisfied with a particular response option and adjust the response options based on the results. This allows the reaction unit to provide the optimal response according to the user's needs, effectively supporting stress relief.

[0033] The optimization unit learns from user feedback based on the responses provided by the response unit and optimizes the responses. For example, the optimization unit continuously learns from user feedback using a generating AI and optimizes the responses. Specifically, the optimization unit collects user responses and feedback and improves response options based on them. The generating AI analyzes user feedback and evaluates the effectiveness of the response options. For example, it evaluates whether the user is satisfied with a particular response option and adjusts the response option based on the result. The generating AI continuously learns from user feedback and improves the accuracy and effectiveness of the response options. This allows the optimization unit to provide the optimal response that meets the user's needs. Furthermore, the optimization unit can also develop new response options based on user feedback. For example, if a user requests a new response to a particular situation, the optimization unit develops and provides a new response option that meets that need. This allows the optimization unit to respond flexibly to user needs and maximize the effectiveness of the stress relief support system. In addition, the optimization unit identifies and implements improvements to enhance the overall system performance based on user feedback. This allows the optimization unit to improve the overall reliability and effectiveness of the system, and effectively support user stress relief.

[0034] The response unit can offer multiple response options, such as a weak counter-argument, an apologizing mode, or a mode that simply brushes off the comment. For example, the response unit can offer a weak counter-argument. For example, the response unit can reduce user stress by offering a mild counter-argument to what the user has said. For example, the response unit can offer an apologizing mode. For example, the response unit can reduce user stress by apologizing for what the user has said. For example, the response unit can offer a mode that simply brushes off the comment. For example, the response unit can reduce user stress by simply brushing off what the user has said. In this way, the response unit can support stress relief by offering the user multiple response options to choose from.

[0035] The optimization unit allows the generating AI to continuously learn from user feedback and optimize its responses. For example, the optimization unit collects user feedback and the generating AI learns from that feedback. For example, if the user prefers a particular response, the optimization unit optimizes to prioritize providing that response. For example, if the user dislikes a particular response, the optimization unit optimizes to avoid providing that response. The optimization unit can also add new response options based on user feedback. In this way, the optimization unit can improve user satisfaction by learning from user feedback and optimizing its responses.

[0036] The design department can understand user preferences in detail through dialogue and design an appearance that meets individual needs. For example, the design department can understand user preferences in detail through dialogue. For example, the design department can understand the colors and shapes desired by users and design the appearance based on them. For example, the design department can understand the materials and design styles desired by users and design the appearance based on them. For example, the design department can understand the functions and features desired by users and design the appearance based on them. In this way, the design department can understand user preferences in detail and design an appearance that meets individual needs, thereby improving user satisfaction.

[0037] The generation unit can create new appearances using a 3D printer and integrate an AI agent into them. For example, the generation unit can create a new appearance using a 3D printer. For example, the generation unit can create an appearance using a 3D printer based on the colors and shapes desired by the user. For example, the generation unit can create an appearance using a 3D printer based on the materials and design styles desired by the user. For example, the generation unit can create an appearance using a 3D printer based on the functions and features desired by the user. For example, the generation unit can integrate an AI agent into the created appearance. For example, the generation unit can place the AI ​​agent inside the appearance to enable interaction with the user. For example, the generation unit can place the AI ​​agent inside the appearance and perform actions in response to the user's reactions. In this way, the generation unit can quickly create appearances that meet the user's wishes by using a 3D printer.

[0038] The design department can analyze a user's past design history and propose the optimal design pattern. For example, the design department can use its generative AI to propose a new design based on the colors and shapes the user has preferred in the past. For example, the design department can analyze the user's past design trends and have its generative AI propose a design based on that. For example, the design department can consider design elements the user has avoided in the past and have its generative AI propose a design that excludes them. In this way, the design department can propose the optimal design pattern by analyzing the user's past design history.

[0039] The design department can select design themes based on the user's current lifestyle and areas of interest during the design process. For example, if a user is currently traveling, the design department's generative AI will suggest design themes related to travel. If a user has recently started a new hobby, the design department's generative AI will suggest design themes related to that hobby. If a user has plans to attend a specific event, the design department's generative AI will suggest design themes related to that event. In this way, the design department can improve user satisfaction by providing design themes that are tailored to the user's lifestyle and areas of interest.

[0040] The design department can prioritize and propose highly relevant designs by considering the user's geographical location during the design process. For example, if the user is at the beach, the design department's generative AI will propose designs related to the sea. If the user is in an urban area, the design department's generative AI will propose urban designs. If the user is in a mountainous area, the design department's generative AI will propose designs related to nature. In this way, the design department can improve user satisfaction by providing designs based on the user's geographical location.

[0041] The design department can analyze users' social media activity during the design process and propose relevant designs. For example, the design department can use generative AI to suggest relevant designs based on images and posts that users have recently shared on social media. For example, the design department can analyze the trends of accounts that users follow and use generative AI to suggest designs based on that. For example, the design department can use generative AI to suggest relevant designs based on content that users have shown interest in on social media. In this way, the design department can improve user satisfaction by providing designs based on users' social media activity.

[0042] The generation unit can analyze the user's past generation history to select the optimal generation method during the generation process. For example, the generation unit's generation AI can generate a new doll based on materials and shapes the user has preferred in the past. For example, the generation unit can analyze the trends of generation methods the user has chosen in the past, and the generation AI can select a generation method based on that. For example, the generation unit can consider generation elements the user has avoided in the past, and the generation AI can select a generation method that excludes them. In this way, the generation unit can select the optimal generation method by analyzing the user's past generation history.

[0043] The generation unit can customize the functions of the dolls it generates based on the user's current lifestyle. For example, if the user is currently traveling, the generation AI will generate a doll with functions related to travel. If the user has recently started a new hobby, the generation AI will generate a doll with functions related to that hobby. If the user has plans to attend a specific event, the generation AI will generate a doll with functions related to that event. In this way, the generation unit can improve user satisfaction by providing dolls with functions that match the user's lifestyle.

[0044] The generation unit can select the optimal generation method by considering the user's geographical location information during the generation process. For example, if the user is at the beach, the generation AI will select materials and designs related to the sea. If the user is in an urban area, the generation AI will select urban materials and designs. If the user is in a mountainous area, the generation AI will select materials and designs related to nature. In this way, the generation unit can improve user satisfaction by providing a generation method based on the user's geographical location information.

[0045] The generation unit can analyze the user's social media activity during generation and propose functions for the generated doll. For example, the generation unit can propose a doll with relevant functions based on images and posts recently shared by the user on social media. For example, the generation unit can analyze the trends of accounts the user follows and propose a doll with functions based on that analysis. For example, the generation unit can propose a doll with relevant functions based on content the user has shown interest in on social media. In this way, the generation unit can improve user satisfaction by providing dolls with functions based on the user's social media activity.

[0046] The response unit can analyze the user's past response history to select the optimal response pattern during a response. For example, the AI ​​can select a new response based on the response patterns the user has preferred in the past. For example, the AI ​​can analyze the trends of response patterns the user has chosen in the past and select a response based on that. For example, the AI ​​can consider response patterns the user has avoided in the past and select a response that excludes them. In this way, the response unit can provide the optimal response pattern by analyzing the user's past response history.

[0047] The response unit can customize the content of its responses based on the user's current life circumstances. For example, if the user is currently traveling, the AI ​​will provide responses related to travel. If the user has recently started a new hobby, the AI ​​will provide responses related to that hobby. If the user has plans to attend a specific event, the AI ​​will provide responses related to that event. In this way, the response unit can improve user satisfaction by providing responses that are tailored to the user's life circumstances.

[0048] The response unit can select the optimal response by considering the user's geographical location information when responding. For example, if the user is at the beach, the AI ​​will show a response related to the sea. For example, if the user is in an urban area, the AI ​​will show an urban response. For example, if the user is in a mountainous area, the AI ​​will show a response related to nature. In this way, the response unit can improve user satisfaction by providing responses based on the user's geographical location information.

[0049] The response unit can analyze the user's social media activity and suggest appropriate responses when responding. For example, the AI ​​can suggest relevant responses based on images and posts the user has recently shared on social media. For example, the AI ​​can analyze the trends of accounts the user follows and suggest responses based on that. For example, the AI ​​can suggest relevant responses based on content the user has shown interest in on social media. In this way, the response unit can improve user satisfaction by providing responses based on the user's social media activity.

[0050] The optimization unit can analyze the user's past feedback history to select the optimal optimization method during the optimization process. For example, the optimization unit's generating AI selects a new optimization method based on the user's preferred response patterns in the past. For example, the optimization unit analyzes the trends of optimization methods previously chosen by the user, and its generating AI selects an optimization method based on that. For example, the optimization unit considers optimization elements that the user has previously avoided, and its generating AI selects an optimization method that excludes them. In this way, the optimization unit can provide the optimal optimization method by analyzing the user's past feedback history.

[0051] The optimization unit can customize the optimization content based on the user's current lifestyle during the optimization process. For example, if the user is currently traveling, the generating AI will provide optimization content related to travel. For example, if the user has recently started a new hobby, the generating AI will provide optimization content related to that hobby. For example, if the user has plans to attend a specific event, the generating AI will provide optimization content related to that event. In this way, the optimization unit can improve user satisfaction by providing optimization content tailored to the user's lifestyle.

[0052] The optimization unit can select the optimal optimization method by considering the user's geographical location information during optimization. For example, if the user is at the beach, the generating AI will provide optimization content related to the sea. For example, if the user is in an urban area, the generating AI will provide urban optimization content. For example, if the user is in a mountainous area, the generating AI will provide optimization content related to nature. In this way, the optimization unit can improve user satisfaction by providing an optimization method based on the user's geographical location information.

[0053] The optimization unit can analyze the user's social media activity during the optimization process and propose optimization content. For example, the optimization unit's generating AI can provide relevant optimization content based on images and posts recently shared by the user on social media. For example, the optimization unit can analyze the trends of accounts the user follows and its generating AI can provide optimization content based on that analysis. For example, the optimization unit's generating AI can provide relevant optimization content based on content the user has shown interest in on social media. In this way, the optimization unit can improve user satisfaction by providing optimization content based on the user's social media activity.

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

[0055] The design department can analyze a user's past design history and propose the most suitable design pattern. For example, the generation AI can suggest a new design based on the colors and shapes the user has preferred in the past. The generation AI can analyze the user's past design trends and propose designs based on that. The generation AI can consider design elements the user has avoided in the past and propose designs that exclude them. In this way, the design department can propose the most suitable design pattern by analyzing the user's past design history.

[0056] The generation unit can analyze the user's past generation history to select the optimal generation method during the generation process. For example, the generation AI can generate a new doll based on the materials and shapes the user has preferred in the past. The generation AI can analyze the trends of the generation methods the user has chosen in the past and select a generation method based on that. The generation AI can consider the generation elements the user has avoided in the past and select a generation method that excludes them. In this way, the generation unit can select the optimal generation method by analyzing the user's past generation history.

[0057] The reaction unit can analyze the user's past reaction history to select the optimal reaction pattern during a reaction. For example, the AI ​​can select a new reaction based on the reaction patterns the user has preferred in the past. The AI ​​can analyze the trends of reaction patterns the user has previously chosen and select a reaction based on that. The AI ​​can consider reaction patterns the user has avoided in the past and select a reaction that excludes them. In this way, the reaction unit can provide the optimal reaction pattern by analyzing the user's past reaction history.

[0058] The optimization unit can analyze the user's past feedback history to select the optimal optimization method during the optimization process. For example, the generating AI can select a new optimization method based on the user's preferred response patterns in the past. The generating AI can analyze the trends of optimization methods previously chosen by the user and select an optimization method based on that. The generating AI can consider optimization elements that the user has previously avoided and select an optimization method that excludes them. In this way, the optimization unit can provide the optimal optimization method by analyzing the user's past feedback history.

[0059] The optimization unit can customize the optimization content based on the user's current lifestyle during the optimization process. For example, if the user is currently traveling, the generating AI can provide optimization content related to travel. If the user has recently started a new hobby, the generating AI can provide optimization content related to that hobby. If the user has plans to attend a specific event, the generating AI can provide optimization content related to that event. In this way, the optimization unit can improve user satisfaction by providing optimization content tailored to the user's lifestyle.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The design department understands the user's wishes and designs the appearance. For example, the design department understands the user's wishes in detail through dialogue and designs an appearance that meets individual needs. The design department can use generative AI to understand the user's wishes and design the appearance. Step 2: The generation unit creates a new appearance based on the appearance designed by the design unit and integrates an AI agent into it. The generation unit can, for example, use a 3D printer to create a new appearance and integrate an AI agent into it. The generation unit can use generation AI to create a new appearance and integrate an AI agent into it. Step 3: The reaction unit provides the user with multiple reaction options for the doll generated by the generation unit. The reaction unit provides multiple reaction options such as a weak counter-argument, an apologizing mode, and a mode that simply brushes things off. The reaction unit can use AI to provide multiple reaction options for the user. Step 4: The optimization unit learns user feedback based on the response provided by the response unit and optimizes the response. For example, the optimization unit can optimize the response by having the generating AI continuously learn user feedback. The optimization unit can use the generating AI to learn user feedback and optimize the response.

[0062] (Example of form 2) The stress relief support system according to an embodiment of the present invention is a system that supports stress relief by generating a customized doll according to the user's wishes using a generative AI. This system understands the user's wishes and designs the appearance. Next, a new appearance is created using a 3D printer and an AI agent is integrated into it. The AI ​​agent provides multiple response options that the user can choose from. For example, there are modes such as weak objection, apologizing, and simply brushing it off. The generative AI continuously learns from the user's feedback and optimizes its responses. This provides a fun and safe space to release stress and realizes a unique and personalized emotional release. It also provides a new method and option in mental healthcare. For example, the system understands the user's wishes in detail through dialogue and designs an appearance according to individual needs. Next, a new appearance is created using a 3D printer and an AI agent is integrated into it. The AI ​​agent provides multiple response options that the user can choose from. For example, there are modes such as weak objection, apologizing, and simply brushing it off. This allows the user to choose a response that suits their emotions and release stress. The generative AI continuously learns from the user's feedback and optimizes its responses. This allows for the provision of optimal responses tailored to user needs. For example, if a user prefers a particular response, the system will be optimized to prioritize that response. This improves user satisfaction and enhances the effectiveness of stress relief. This provides a fun and safe space for stress relief. Users can confidently express their emotions and achieve a unique and personalized emotional release. It also offers new methods and options in mental healthcare. This allows the stress relief support system to generate customized dolls according to the user's wishes and support stress relief.

[0063] The stress relief support system according to this embodiment comprises a design unit, a generation unit, a reaction unit, and an optimization unit. The design unit understands the user's wishes and designs the appearance. The design unit, for example, understands the wishes in detail through dialogue with the user and designs an appearance that meets individual needs. The design unit can understand the user's wishes and design the appearance using generation AI. The generation unit creates a new appearance based on the appearance designed by the design unit and integrates an AI agent into it. The generation unit, for example, creates a new appearance using a 3D printer and integrates an AI agent into it. The generation unit can create a new appearance and integrate an AI agent into it using generation AI. The reaction unit provides multiple reaction options that the user can choose from in the doll generated by the generation unit. The reaction unit provides multiple reaction options such as a weak objection, an apologizing mode, and a mode that simply brushes things off. The reaction unit can provide multiple reaction options that the user can choose from using AI. The optimization unit learns feedback from the user based on the reactions provided by the reaction unit and optimizes the reactions. The optimization unit, for example, continuously learns from user feedback using a generating AI and optimizes its response. The optimization unit can learn from user feedback using the generating AI and optimize its response. As a result, the stress relief support system according to the embodiment can generate a customized doll according to the user's wishes and support stress relief.

[0064] The design department understands user preferences and designs the appearance. For example, the design department understands preferences in detail through dialogue with users and designs an appearance that meets individual needs. Specifically, the design department collects detailed preferences such as color, shape, size, and material through dialogue with users. They conduct detailed interviews to understand what kind of design users prefer and what functions they are looking for, and then design based on that. The design department can understand user preferences and design the appearance using generative AI. Generative AI analyzes user preferences using natural language processing technology and accurately understands the user's intentions. For example, if a user wants a "relaxing design," the generative AI analyzes that intention and suggests colors and shapes that enhance the relaxing effect. The generative AI also refers to past design data and trend information to generate the optimal design for the user's preferences. This allows the design department to quickly and accurately design a customized appearance that meets user preferences. Furthermore, the design department can revise and adjust the design through dialogue with users. For example, if a user wants to change part of the design, the design department revise the design according to that request and finalize the design. This allows the design department to create an appearance that perfectly matches the user's wishes.

[0065] The generation unit creates a new appearance based on the appearance designed by the design unit and integrates an AI agent within it. For example, the generation unit can create a new appearance using a 3D printer and integrate an AI agent within it. Specifically, the generation unit physically generates the appearance using a 3D printer based on the design data provided by the design unit. The 3D printer uses high-precision printing technology to create an appearance faithful to the design data. The generation unit can use generation AI to create a new appearance and integrate an AI agent within it. The generation AI analyzes the design data and sets optimal printing parameters. For example, it optimizes material selection, printing speed, and layer thickness to produce a high-quality appearance. The generation AI also manages the AI ​​agent integration process. Since the AI ​​agent is responsible for user interaction and responses, it needs to be appropriately positioned within the appearance. The generation AI optimizes the placement and connection of the AI ​​agent, integrating the appearance and the AI ​​agent so that they become one. This allows the generation unit to generate the appearance designed by the design unit with high precision and integrate the AI ​​agent. Furthermore, the production unit performs quality control of the production process, ensuring that the generated appearance is faithful to the design data. For example, it inspects the dimensions and shape of the generated appearance and makes corrections as needed. This allows the production unit to produce high-quality appearances and provide customized dolls that meet the user's needs.

[0066] The response unit provides multiple response options for the user to choose from in the doll generated by the generation unit. For example, the response unit offers several response options such as a weak objection mode, an apologetic mode, and a mode that simply brushes off the conversation. Specifically, the response unit selects and provides an appropriate response based on the user's input. The response unit can use AI to provide multiple response options for the user to choose from. The AI ​​analyzes the user's input and selects the optimal response. For example, if the user is feeling stressed, the AI ​​analyzes the situation and provides an appropriate response option. In weak objection mode, the AI ​​gently objectes to the user's opinion to encourage discussion. In apologetic mode, the AI ​​apologizes to the user to ease their feelings. In brush-off mode, the AI ​​brushes off what the user is saying, allowing the user to focus on speaking. This allows the response unit to provide appropriate responses tailored to the user's situation and feelings, supporting stress relief. Furthermore, the response unit can collect user responses and feedback to continuously improve the accuracy and effectiveness of the response options. For example, it can evaluate whether the user is satisfied with a particular response option and adjust the response options based on the results. This allows the reaction unit to provide the optimal response according to the user's needs, effectively supporting stress relief.

[0067] The optimization unit learns from user feedback based on the responses provided by the response unit and optimizes the responses. For example, the optimization unit continuously learns from user feedback using a generating AI and optimizes the responses. Specifically, the optimization unit collects user responses and feedback and improves response options based on them. The generating AI analyzes user feedback and evaluates the effectiveness of the response options. For example, it evaluates whether the user is satisfied with a particular response option and adjusts the response option based on the result. The generating AI continuously learns from user feedback and improves the accuracy and effectiveness of the response options. This allows the optimization unit to provide the optimal response that meets the user's needs. Furthermore, the optimization unit can also develop new response options based on user feedback. For example, if a user requests a new response to a particular situation, the optimization unit develops and provides a new response option that meets that need. This allows the optimization unit to respond flexibly to user needs and maximize the effectiveness of the stress relief support system. In addition, the optimization unit identifies and implements improvements to enhance the overall system performance based on user feedback. This allows the optimization unit to improve the overall reliability and effectiveness of the system, and effectively support user stress relief.

[0068] The response unit can offer multiple response options, such as a weak counter-argument, an apologizing mode, or a mode that simply brushes off the comment. For example, the response unit can offer a weak counter-argument. For example, the response unit can reduce user stress by offering a mild counter-argument to what the user has said. For example, the response unit can offer an apologizing mode. For example, the response unit can reduce user stress by apologizing for what the user has said. For example, the response unit can offer a mode that simply brushes off the comment. For example, the response unit can reduce user stress by simply brushing off what the user has said. In this way, the response unit can support stress relief by offering the user multiple response options to choose from.

[0069] The optimization unit allows the generating AI to continuously learn from user feedback and optimize its responses. For example, the optimization unit collects user feedback and the generating AI learns from that feedback. For example, if the user prefers a particular response, the optimization unit optimizes to prioritize providing that response. For example, if the user dislikes a particular response, the optimization unit optimizes to avoid providing that response. The optimization unit can also add new response options based on user feedback. In this way, the optimization unit can improve user satisfaction by learning from user feedback and optimizing its responses.

[0070] The design department can understand user preferences in detail through dialogue and design an appearance that meets individual needs. For example, the design department can understand user preferences in detail through dialogue. For example, the design department can understand the colors and shapes desired by users and design the appearance based on them. For example, the design department can understand the materials and design styles desired by users and design the appearance based on them. For example, the design department can understand the functions and features desired by users and design the appearance based on them. In this way, the design department can understand user preferences in detail and design an appearance that meets individual needs, thereby improving user satisfaction.

[0071] The generation unit can create new appearances using a 3D printer and integrate an AI agent into them. For example, the generation unit can create a new appearance using a 3D printer. For example, the generation unit can create an appearance using a 3D printer based on the colors and shapes desired by the user. For example, the generation unit can create an appearance using a 3D printer based on the materials and design styles desired by the user. For example, the generation unit can create an appearance using a 3D printer based on the functions and features desired by the user. For example, the generation unit can integrate an AI agent into the created appearance. For example, the generation unit can place the AI ​​agent inside the appearance to enable interaction with the user. For example, the generation unit can place the AI ​​agent inside the appearance and perform actions in response to the user's reactions. In this way, the generation unit can quickly create appearances that meet the user's wishes by using a 3D printer.

[0072] The design department can estimate the user's emotions and adjust the design's colors and shapes based on those emotions. For example, if the user is relaxed, the design department's generative AI will design soft colors and curved shapes. If the user is stressed, the design department's generative AI will design calm colors and simple shapes. If the user is excited, the design department's generative AI will design vibrant colors and dynamic shapes. This allows the design department to improve user satisfaction by providing designs that match the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The design department can analyze a user's past design history and propose the optimal design pattern. For example, the design department can use its generative AI to propose a new design based on the colors and shapes the user has preferred in the past. For example, the design department can analyze the user's past design trends and have its generative AI propose a design based on that. For example, the design department can consider design elements the user has avoided in the past and have its generative AI propose a design that excludes them. In this way, the design department can propose the optimal design pattern by analyzing the user's past design history.

[0074] The design department can select design themes based on the user's current lifestyle and areas of interest during the design process. For example, if a user is currently traveling, the design department's generative AI will suggest design themes related to travel. If a user has recently started a new hobby, the design department's generative AI will suggest design themes related to that hobby. If a user has plans to attend a specific event, the design department's generative AI will suggest design themes related to that event. In this way, the design department can improve user satisfaction by providing design themes that are tailored to the user's lifestyle and areas of interest.

[0075] The design department can estimate the user's emotions and prioritize designs based on those emotions. For example, if the user is stressed, the design department's generative AI will prioritize suggesting relaxing designs. If the user is excited, the design department's generative AI will prioritize suggesting stimulating designs. If the user is tired, the design department's generative AI will prioritize suggesting simple and calming designs. In this way, the design department can improve user satisfaction by prioritizing designs according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The design department can prioritize and propose highly relevant designs by considering the user's geographical location during the design process. For example, if the user is at the beach, the design department's generative AI will propose designs related to the sea. If the user is in an urban area, the design department's generative AI will propose urban designs. If the user is in a mountainous area, the design department's generative AI will propose designs related to nature. In this way, the design department can improve user satisfaction by providing designs based on the user's geographical location.

[0077] The design department can analyze users' social media activity during the design process and propose relevant designs. For example, the design department can use generative AI to suggest relevant designs based on images and posts that users have recently shared on social media. For example, the design department can analyze the trends of accounts that users follow and use generative AI to suggest designs based on that. For example, the design department can use generative AI to suggest relevant designs based on content that users have shown interest in on social media. In this way, the design department can improve user satisfaction by providing designs based on users' social media activity.

[0078] The generation unit can estimate the user's emotions and select the material of the doll to be generated based on the estimated emotions. For example, if the user is relaxed, the generation AI will select a soft material. If the user is stressed, the generation AI will select a durable material. If the user is excited, the generation AI will select a visually stimulating material. In this way, the generation unit can improve user satisfaction by selecting a material that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The generation unit can analyze the user's past generation history to select the optimal generation method during the generation process. For example, the generation unit's generation AI can generate a new doll based on materials and shapes the user has preferred in the past. For example, the generation unit can analyze the trends of generation methods the user has chosen in the past, and the generation AI can select a generation method based on that. For example, the generation unit can consider generation elements the user has avoided in the past, and the generation AI can select a generation method that excludes them. In this way, the generation unit can select the optimal generation method by analyzing the user's past generation history.

[0080] The generation unit can customize the functions of the dolls it generates based on the user's current lifestyle. For example, if the user is currently traveling, the generation AI will generate a doll with functions related to travel. If the user has recently started a new hobby, the generation AI will generate a doll with functions related to that hobby. If the user has plans to attend a specific event, the generation AI will generate a doll with functions related to that event. In this way, the generation unit can improve user satisfaction by providing dolls with functions that match the user's lifestyle.

[0081] The generation unit can estimate the user's emotions and determine the priority of the dolls to generate based on the estimated emotions. For example, if the user is stressed, the generation AI will prioritize generating dolls with a relaxing effect. For example, if the user is excited, the generation AI will prioritize generating stimulating dolls. For example, if the user is tired, the generation AI will prioritize generating simple and calming dolls. In this way, the generation unit can improve user satisfaction by determining the priority of dolls according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The generation unit can select the optimal generation method by considering the user's geographical location information during the generation process. For example, if the user is at the beach, the generation AI will select materials and designs related to the sea. If the user is in an urban area, the generation AI will select urban materials and designs. If the user is in a mountainous area, the generation AI will select materials and designs related to nature. In this way, the generation unit can improve user satisfaction by providing a generation method based on the user's geographical location information.

[0083] The generation unit can analyze the user's social media activity during generation and propose functions for the generated doll. For example, the generation unit can propose a doll with relevant functions based on images and posts recently shared by the user on social media. For example, the generation unit can analyze the trends of accounts the user follows and propose a doll with functions based on that analysis. For example, the generation unit can propose a doll with relevant functions based on content the user has shown interest in on social media. In this way, the generation unit can improve user satisfaction by providing dolls with functions based on the user's social media activity.

[0084] The response unit can estimate the user's emotions and adjust the intensity of its response based on the estimated emotions. For example, if the user is relaxed, the AI ​​will give a gentle response. If the user is stressed, the AI ​​will give a stronger response. If the user is excited, the AI ​​will give a more active response. In this way, the response unit can improve user satisfaction by providing a response intensity that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The response unit can analyze the user's past response history to select the optimal response pattern during a response. For example, the AI ​​can select a new response based on the response patterns the user has preferred in the past. For example, the AI ​​can analyze the trends of response patterns the user has chosen in the past and select a response based on that. For example, the AI ​​can consider response patterns the user has avoided in the past and select a response that excludes them. In this way, the response unit can provide the optimal response pattern by analyzing the user's past response history.

[0086] The response unit can customize the content of its responses based on the user's current life circumstances. For example, if the user is currently traveling, the AI ​​will provide responses related to travel. If the user has recently started a new hobby, the AI ​​will provide responses related to that hobby. If the user has plans to attend a specific event, the AI ​​will provide responses related to that event. In this way, the response unit can improve user satisfaction by providing responses that are tailored to the user's life circumstances.

[0087] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user is stressed, the AI ​​will prioritize responses that have a relaxing effect. For example, if the user is excited, the AI ​​will prioritize responses that are stimulating. For example, if the user is tired, the AI ​​will prioritize responses that are simple and calming. In this way, the response unit can improve user satisfaction by determining the priority of responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The response unit can select the optimal response by considering the user's geographical location information when responding. For example, if the user is at the beach, the AI ​​will show a response related to the sea. For example, if the user is in an urban area, the AI ​​will show an urban response. For example, if the user is in a mountainous area, the AI ​​will show a response related to nature. In this way, the response unit can improve user satisfaction by providing responses based on the user's geographical location information.

[0089] The response unit can analyze the user's social media activity and suggest appropriate responses when responding. For example, the AI ​​can suggest relevant responses based on images and posts the user has recently shared on social media. For example, the AI ​​can analyze the trends of accounts the user follows and suggest responses based on that. For example, the AI ​​can suggest relevant responses based on content the user has shown interest in on social media. In this way, the response unit can improve user satisfaction by providing responses based on the user's social media activity.

[0090] The optimization unit can estimate the user's emotions and adjust the optimization algorithm based on the estimated emotions. For example, if the user is relaxed, the optimization unit adjusts the algorithm so that the generating AI prioritizes calm responses. For example, if the user is stressed, the optimization unit adjusts the algorithm so that the generating AI prioritizes responses that have a stress-reducing effect. For example, if the user is excited, the optimization unit adjusts the algorithm so that the generating AI prioritizes stimulating responses. In this way, the optimization unit can improve user satisfaction by providing an optimization algorithm that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The optimization unit can analyze the user's past feedback history to select the optimal optimization method during the optimization process. For example, the optimization unit's generating AI selects a new optimization method based on the user's preferred response patterns in the past. For example, the optimization unit analyzes the trends of optimization methods previously chosen by the user, and its generating AI selects an optimization method based on that. For example, the optimization unit considers optimization elements that the user has previously avoided, and its generating AI selects an optimization method that excludes them. In this way, the optimization unit can provide the optimal optimization method by analyzing the user's past feedback history.

[0092] The optimization unit can customize the optimization content based on the user's current lifestyle during the optimization process. For example, if the user is currently traveling, the generating AI will provide optimization content related to travel. For example, if the user has recently started a new hobby, the generating AI will provide optimization content related to that hobby. For example, if the user has plans to attend a specific event, the generating AI will provide optimization content related to that event. In this way, the optimization unit can improve user satisfaction by providing optimization content tailored to the user's lifestyle.

[0093] The optimization unit can estimate the user's emotions and determine optimization priorities based on those emotions. For example, if the user is stressed, the optimization unit's generating AI will prioritize optimizations that reduce stress. If the user is excited, the optimization unit's generating AI will prioritize stimulating optimizations. If the user is tired, the optimization unit's generating AI will prioritize optimizations that promote relaxation. In this way, the optimization unit can improve user satisfaction by providing optimization priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The optimization unit can select the optimal optimization method by considering the user's geographical location information during optimization. For example, if the user is at the beach, the generating AI will provide optimization content related to the sea. For example, if the user is in an urban area, the generating AI will provide urban optimization content. For example, if the user is in a mountainous area, the generating AI will provide optimization content related to nature. In this way, the optimization unit can improve user satisfaction by providing an optimization method based on the user's geographical location information.

[0095] The optimization unit can analyze the user's social media activity during the optimization process and propose optimization content. For example, the optimization unit's generating AI can provide relevant optimization content based on images and posts recently shared by the user on social media. For example, the optimization unit can analyze the trends of accounts the user follows and its generating AI can provide optimization content based on that analysis. For example, the optimization unit's generating AI can provide relevant optimization content based on content the user has shown interest in on social media. In this way, the optimization unit can improve user satisfaction by providing optimization content based on the user's social media activity.

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

[0097] The design department can estimate the user's emotions and adjust the design's colors and shapes based on those emotions. For example, if the user is relaxed, the generating AI can design soft colors and curved shapes. If the user is stressed, the generating AI can design calm colors and simple shapes. If the user is excited, the generating AI can design vibrant colors and dynamic shapes. This allows the design department to improve user satisfaction by providing designs that match the user's emotions.

[0098] The generation unit can estimate the user's emotions and select the material for the doll to be generated based on those emotions. For example, if the user is relaxed, the generation AI can select a soft material. If the user is stressed, the generation AI can select a durable material. If the user is excited, the generation AI can select a visually stimulating material. In this way, the generation unit can improve user satisfaction by selecting materials that match the user's emotions.

[0099] The response unit can estimate the user's emotions and adjust the intensity of its response based on those emotions. For example, if the user is relaxed, the AI ​​can give a gentle response. If the user is stressed, the AI ​​can give a stronger response. If the user is excited, the AI ​​can give a more active response. In this way, the response unit can improve user satisfaction by providing a response intensity that matches the user's emotions.

[0100] The optimization unit can estimate the user's emotions and adjust the optimization algorithm based on those emotions. For example, if the user is relaxed, the generating AI can adjust the algorithm to prioritize calm responses. If the user is stressed, the generating AI can adjust the algorithm to prioritize responses that reduce stress. If the user is excited, the generating AI can adjust the algorithm to prioritize stimulating responses. In this way, the optimization unit can improve user satisfaction by providing an optimization algorithm that responds to the user's emotions.

[0101] The optimization unit can estimate the user's emotions and determine optimization priorities based on those emotions. For example, if the user is stressed, the generating AI can prioritize optimizations that reduce stress. If the user is excited, the generating AI can prioritize stimulating optimizations. If the user is tired, the generating AI can prioritize optimizations that promote relaxation. In this way, the optimization unit can improve user satisfaction by providing optimization priorities that correspond to the user's emotions.

[0102] The design department can analyze a user's past design history and propose the most suitable design pattern. For example, the generation AI can suggest a new design based on the colors and shapes the user has preferred in the past. The generation AI can analyze the user's past design trends and propose designs based on that. The generation AI can consider design elements the user has avoided in the past and propose designs that exclude them. In this way, the design department can propose the most suitable design pattern by analyzing the user's past design history.

[0103] The generation unit can analyze the user's past generation history to select the optimal generation method during the generation process. For example, the generation AI can generate a new doll based on the materials and shapes the user has preferred in the past. The generation AI can analyze the trends of the generation methods the user has chosen in the past and select a generation method based on that. The generation AI can consider the generation elements the user has avoided in the past and select a generation method that excludes them. In this way, the generation unit can select the optimal generation method by analyzing the user's past generation history.

[0104] The reaction unit can analyze the user's past reaction history to select the optimal reaction pattern during a reaction. For example, the AI ​​can select a new reaction based on the reaction patterns the user has preferred in the past. The AI ​​can analyze the trends of reaction patterns the user has previously chosen and select a reaction based on that. The AI ​​can consider reaction patterns the user has avoided in the past and select a reaction that excludes them. In this way, the reaction unit can provide the optimal reaction pattern by analyzing the user's past reaction history.

[0105] The optimization unit can analyze the user's past feedback history to select the optimal optimization method during the optimization process. For example, the generating AI can select a new optimization method based on the user's preferred response patterns in the past. The generating AI can analyze the trends of optimization methods previously chosen by the user and select an optimization method based on that. The generating AI can consider optimization elements that the user has previously avoided and select an optimization method that excludes them. In this way, the optimization unit can provide the optimal optimization method by analyzing the user's past feedback history.

[0106] The optimization unit can customize the optimization content based on the user's current lifestyle during the optimization process. For example, if the user is currently traveling, the generating AI can provide optimization content related to travel. If the user has recently started a new hobby, the generating AI can provide optimization content related to that hobby. If the user has plans to attend a specific event, the generating AI can provide optimization content related to that event. In this way, the optimization unit can improve user satisfaction by providing optimization content tailored to the user's lifestyle.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The design department understands the user's wishes and designs the appearance. For example, the design department understands the user's wishes in detail through dialogue and designs an appearance that meets individual needs. The design department can use generative AI to understand the user's wishes and design the appearance. Step 2: The generation unit creates a new appearance based on the appearance designed by the design unit and integrates an AI agent into it. The generation unit can, for example, use a 3D printer to create a new appearance and integrate an AI agent into it. The generation unit can use generation AI to create a new appearance and integrate an AI agent into it. Step 3: The reaction unit provides the user with multiple reaction options for the doll generated by the generation unit. The reaction unit provides multiple reaction options such as a weak counter-argument, an apologizing mode, and a mode that simply brushes things off. The reaction unit can use AI to provide multiple reaction options for the user. Step 4: The optimization unit learns user feedback based on the response provided by the response unit and optimizes the response. For example, the optimization unit can optimize the response by having the generating AI continuously learn user feedback. The optimization unit can use the generating AI to learn user feedback and optimize the response.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the design unit, generation unit, reaction unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the design unit is implemented by the control unit 46A of the smart device 14, which understands the user's wishes in detail through interaction and designs an appearance that meets individual needs. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates a new appearance using a 3D printer and integrates an AI agent into it. The reaction unit is implemented by the control unit 46A of the smart device 14, which provides the user with multiple reaction options to choose from. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns user feedback and optimizes the reaction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the design unit, generation unit, reaction unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the design unit is implemented by the control unit 46A of the smart glasses 214, which understands the user's wishes in detail through interaction and designs an appearance that meets individual needs. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates a new appearance using a 3D printer and integrates an AI agent into it. The reaction unit is implemented by the control unit 46A of the smart glasses 214, which provides the user with multiple reaction options to choose from. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns user feedback and optimizes the reaction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

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

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the design unit, generation unit, reaction unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the design unit is implemented by the control unit 46A of the headset terminal 314, which understands the user's wishes in detail through interaction and designs an appearance that meets individual needs. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates a new appearance using a 3D printer and integrates an AI agent into it. The reaction unit is implemented by the control unit 46A of the headset terminal 314, which provides the user with multiple reaction options to choose from. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns user feedback and optimizes the reaction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the design unit, generation unit, reaction unit, and optimization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the design unit is implemented by the control unit 46A of the robot 414, which understands the user's wishes in detail through interaction and designs an appearance that meets individual needs. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates a new appearance using a 3D printer and integrates an AI agent into it. The reaction unit is implemented by the control unit 46A of the robot 414, which provides the user with multiple reaction options to choose from. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which learns user feedback and optimizes the reaction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The design department understands the user's wishes and designs the exterior, A generation unit creates a new appearance based on the appearance designed by the aforementioned design unit and integrates an AI agent into it. The doll produced by the generation unit includes a reaction unit that provides multiple reaction options selectable by the user, The system includes an optimization unit that learns user feedback based on the reaction provided by the reaction unit and optimizes the reaction. A system characterized by the following features. (Note 2) The reaction section is It offers multiple response options, such as a weak counter-argument, an apologetic mode, and a mode of simply brushing it off. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, The AI ​​continuously learns from user feedback and optimizes its response. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned design department, We gain a detailed understanding of user preferences through dialogue and design an appearance that meets individual needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Create a new appearance using a 3D printer and integrate an AI agent into it. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned design department, It estimates the user's emotions and adjusts the design's colors and shapes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned design department, We analyze the user's past design history and suggest the optimal design pattern. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned design department, During the design process, the design theme is selected based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned design department, We estimate user emotions and determine design priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned design department, During the design process, we prioritize proposing highly relevant designs by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned design department, During the design process, we analyze users' social media activity and propose relevant designs. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the user's emotions and selects the materials for the doll to be generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the system analyzes the user's past generation history to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During creation, the functions of the generated doll are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and determines the priority of the dolls to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the optimal generation method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is We propose a feature that generates avatars by analyzing the user's social media activity during the creation process. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reaction section is It estimates the user's emotions and adjusts the intensity of the response based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The reaction section is During the response process, the system analyzes the user's past response history to select the optimal response pattern. The system described in Appendix 1, characterized by the features described herein. (Note 20) The reaction section is When responding, the response content is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The reaction section is It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The reaction section is When responding, the system selects the optimal response by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The reaction section is When responding, the system analyzes the user's social media activity and suggests appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, It estimates the user's emotions and adjusts the optimization algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, During optimization, the system analyzes the user's past feedback history to select the most suitable optimization method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, During optimization, the optimization process is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, During optimization, the optimal optimization method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, During optimization, we analyze users' social media activity and propose optimization strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The design department understands the user's wishes and designs the exterior, A generation unit creates a new appearance based on the appearance designed by the aforementioned design unit and integrates an AI agent into it. The doll produced by the generation unit includes a reaction unit that provides multiple reaction options selectable by the user, The system includes an optimization unit that learns user feedback based on the reaction provided by the reaction unit and optimizes the reaction. A system characterized by the following features.

2. The reaction section is It offers multiple response options, such as a weak counter-argument, an apologetic mode, and a mode of simply brushing it off. The system according to feature 1.

3. The optimization unit, The AI ​​continuously learns from user feedback and optimizes its response. The system according to feature 1.

4. The aforementioned design department, We gain a detailed understanding of user preferences through dialogue and design an appearance that meets individual needs. The system according to feature 1.

5. The generating unit is Create a new appearance using a 3D printer and integrate an AI agent into it. The system according to feature 1.

6. The aforementioned design department, It estimates the user's emotions and adjusts the design's colors and shapes based on those estimated emotions. The system according to feature 1.

7. The aforementioned design department, We analyze the user's past design history and suggest the optimal design pattern. The system according to feature 1.

8. The aforementioned design department, During the design process, the design theme is selected based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned design department, We estimate user emotions and determine design priorities based on those estimated emotions. The system according to feature 1.

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