system

Abstract

The system according to this embodiment aims to achieve efficient and optimal design and functionality by simultaneously performing exterior and interior design. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the user. The generation unit analyzes the information received by the reception unit and simultaneously performs exterior design and interior design. The provision unit provides the design generated by the generation unit to the user.

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, since the exterior design and the internal design are carried out in separate processes, there is a problem that the efficiency is low and it is difficult to achieve both an optimal design and function.

[0005] The system according to the embodiment aims to perform the exterior design and the internal design simultaneously to achieve an efficient and optimal design and function.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the user. The generation unit analyzes the information received by the reception unit and simultaneously performs exterior and interior design. The provision unit provides the design generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can perform exterior and interior design simultaneously, achieving an efficient and optimal design and function. [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, and the like. The communication I / F manages 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] [[ID=⑧]][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 receiving 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 receiving 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 design integration system according to an embodiment of the present invention is a system that utilizes generative AI to simultaneously perform exterior and interior design. This design integration system allows the user to input the required functional overview, parts list, design policy, etc., and the generative AI handles all complex processes, ensuring a perfect result. Specifically, the user first inputs the required functional overview, parts list, design policy, etc. Next, the generative AI analyzes this information and simultaneously performs exterior and interior design. The generative AI generates an optimal design while considering the balance between the exterior and interior. Finally, the generated design is provided to the user, who can make modifications and adjustments as needed. This system integrates and efficiently performs the exterior and interior design processes, improving the functional beauty and usability of the product. Furthermore, by entrusting complex processes to the generative AI, the user's design workload is reduced. For example, the user inputs the required functional overview, parts list, design policy, etc. In this process, the user inputs information using text, images, videos, etc. Next, the generative AI analyzes the input information and simultaneously performs exterior and interior design. The generative AI generates an optimal design while considering the balance between the exterior and interior. For example, by prioritizing exterior design while efficiently arranging the interior layout, wasted space can be eliminated. The generated design is provided to the user, who can then modify or adjust it as needed. The user can review the generated design and instruct on any necessary changes. For example, they can change the placement of parts or modify parts of the design. This system integrates and streamlines the exterior and interior design processes, improving the functional beauty and usability of the product. Furthermore, by entrusting complex processes to the generation AI, the user's design workload is reduced. As a result, the integrated design system enables efficient design by simultaneously handling exterior and interior design.

[0029] The design integration system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the user. User input includes, but is not limited to, text, images, and videos. The reception unit receives, for example, information such as a functional overview, parts list, and design policy required by the user for the product. The generation unit uses a generation AI to analyze the information received by the reception unit and simultaneously performs exterior and interior design. The generation unit generates an optimal design, for example, while considering the balance between the exterior and interior. The generation unit can also use the generation AI to analyze information such as text, images, and videos entered by the user. The generation unit uses the generation AI to simultaneously perform exterior and interior design. For example, the generation unit can eliminate wasted space by prioritizing the exterior design while efficiently arranging the interior layout. The provision unit provides the design generated by the generation unit to the user. The provision unit provides the generated design to the user and allows for modifications and adjustments as needed. The provision unit allows the user to review the generated design and instruct necessary changes. For example, it is possible to change the arrangement of parts or modify parts of the design. As a result, the design integration system according to the embodiment enables efficient design by performing exterior and interior design simultaneously. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user-generated information into the generation AI and have the generation AI perform simultaneous generation of exterior and interior designs. Some or all of the above-described processes in the provision unit may be performed using a generation AI, for example, or without a generation AI. For example, the provision unit can input the generated design into the generation AI and have the generation AI perform the optimal method for providing it to the user.

[0030] The generation unit can generate efficient designs while considering the balance between the exterior and interior. For example, the generation unit can prioritize the exterior design while efficiently arranging the interior layout. For example, the generation unit can optimize the exterior shape while adjusting the placement of internal components. The generation unit can also prioritize the internal function while adjusting the exterior design. For example, the generation unit can optimize the internal functional arrangement while adjusting the exterior design. Furthermore, the generation unit can design to eliminate wasted space while considering the balance between the exterior and interior. For example, the generation unit optimizes the balance between the exterior and interior and designs to minimize wasted space. This eliminates wasted space and enables efficient design by considering the balance between the exterior and interior. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can have a generation AI execute a design that considers the balance between the exterior and interior.

[0031] The generation unit can analyze information from text, images, or videos entered by the user. For example, the generation unit can analyze text information entered by the user. For example, the generation unit can analyze text information using natural language processing technology and reflect it in the design. The generation unit can also analyze image information entered by the user. For example, the generation unit can analyze image information using image recognition technology and reflect it in the design. Furthermore, the generation unit can also analyze video information entered by the user. For example, the generation unit can analyze video information using video analysis technology and reflect it in the design. By analyzing the diverse information entered by the user, it becomes possible to create designs with higher accuracy. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input text, images, or video information entered by the user into a generation AI and have the generation AI perform the analysis.

[0032] The provisioning unit can provide the generated design to the user and allow for modifications and adjustments as needed. For example, the provisioning unit can provide the generated design to the user. For example, the provisioning unit can display the generated design to the user and ask for confirmation. The provisioning unit can also allow the user to review the generated design and make necessary modifications and adjustments. For example, the provisioning unit can enable the user to change the placement of parts or modify parts of the design. Furthermore, the provisioning unit can regenerate the design based on user feedback. For example, the provisioning unit can receive user feedback and instruct the generation unit to regenerate the design. This allows the user to review the generated design and make necessary modifications and adjustments to achieve the optimal design. Some or all of the above processes in the provisioning unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the provisioning unit can input the generated design into a generation AI and have the generation AI execute the optimal method for providing it to the user.

[0033] The reception unit can receive information such as a functional overview, parts list, or design policy required for the product. For example, the reception unit can accept a functional overview from the user. For example, the reception unit can accept a functional overview from the user in text format. The reception unit can also accept a parts list from the user. For example, the reception unit can accept a parts list from the user in image format. Furthermore, the reception unit can also accept a design policy from the user. For example, the reception unit can accept a design policy from the user in video format. This allows the user to input the information required for the product, providing the basic information necessary for the generative AI to perform the optimal design. Some or all of the above processing in the reception unit may be performed using the generative AI, or not. For example, the reception unit can input the information entered by the user into the generative AI and have the generative AI perform the analysis.

[0034] The reception desk can analyze the user's past input history and select an efficient reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can automatically complete relevant input fields based on information the user has previously entered. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze information the user has entered during specific time periods in the past and suggest the most suitable input method for that time period. This allows for efficient input by selecting the optimal reception method through analysis of the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past input history data into a generative AI and have the generative AI select the optimal reception method.

[0035] The reception unit can filter input information based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving information related to the user's current projects. For example, the reception unit can filter and receive information related to the user's current projects. The reception unit can also filter and receive highly relevant information based on the user's areas of interest. For example, the reception unit can filter input information based on areas the user has shown interest in in the past. Furthermore, the reception unit can prioritize receiving highly relevant information based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving information related to the user's current projects. This allows for the efficient reception of highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input information about the user's projects and areas of interest into a generative AI and have the generative AI perform the filtering.

[0036] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input information. For example, if the user is in a specific region, the reception unit can prioritize receiving information related to that region. For example, the reception unit can obtain the user's geographical location information and filter and receive information related to that region. Also, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, the reception unit can filter and receive information related to the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the reception unit can prioritize receiving information around their home. For example, the reception unit can filter and receive information around their home based on the user's geographical location information. In this way, highly relevant information can be efficiently received by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI and have the generative AI perform the filtering of highly relevant information.

[0037] The reception unit can analyze the user's social media activity and receive relevant information when receiving input information. For example, the reception unit can receive relevant input information based on information shared by the user on social media. For example, the reception unit can analyze the user's social media activity and filter and receive relevant information. The reception unit can also receive information related to topics of interest from the user's social media activity. For example, the reception unit can filter and receive relevant information based on the accounts the user follows on social media. Furthermore, the reception unit can prioritize receiving highly relevant information based on the user's social media activity. For example, the reception unit can receive relevant input information based on information shared by the user on social media. This allows for the efficient reception of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI and have the generative AI perform the filtering of relevant information.

[0038] The generation unit can adjust the specific details of the design during generation, taking into account the balance between the exterior and interior. For example, the generation unit can prioritize the exterior design while efficiently arranging the interior layout. For example, the generation unit can optimize the shape of the exterior while adjusting the placement of internal components. The generation unit can also prioritize the interior design while adjusting the exterior design. For example, the generation unit can optimize the arrangement of internal functions while adjusting the exterior design. Furthermore, the generation unit can design to eliminate wasted space by considering the balance between the exterior and interior. For example, the generation unit optimizes the balance between the exterior and interior and designs to minimize wasted space. This eliminates wasted space and enables efficient design by considering the balance between the exterior and interior. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can have a generation AI execute a design that takes into account the balance between the exterior and interior.

[0039] The generation unit can apply different generation algorithms depending on the category of information entered by the user during generation. For example, the generation unit can apply an algorithm to generate a detailed design based on text information. For example, the generation unit can analyze the text information entered by the user and apply an algorithm to generate a detailed design. The generation unit can also apply an algorithm to generate a visually superior design based on image information. For example, the generation unit can analyze the image information entered by the user and apply an algorithm to generate a visually superior design. Furthermore, the generation unit can apply an algorithm to generate a dynamic design based on video information. For example, the generation unit can analyze the video information entered by the user and apply an algorithm to generate a dynamic design. This makes it possible to create more accurate designs by applying the optimal generation algorithm according to the category of information entered by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can cause the generation AI to execute the optimal generation algorithm according to the category of information entered by the user.

[0040] The generation unit can determine the generation priority based on the submission timing of the information entered by the user during the generation process. For example, the generation unit can prioritize the processing of urgent information and generate designs quickly. For example, the generation unit prioritizes the processing of highly urgent information based on the submission timing of the information entered by the user. The generation unit can also prioritize the processing of information with an approaching submission deadline. For example, the generation unit prioritizes the processing of information with an approaching submission deadline based on the submission deadline of the information entered by the user. Furthermore, the generation unit can postpone the processing of information with ample time for submission. For example, the generation unit postpones the processing of information with ample time for submission based on the submission timing of the information entered by the user. This enables efficient design by determining the generation priority based on the submission timing of the information entered by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the submission timing of the information entered by the user into the generation AI and have the generation AI execute the generation priority.

[0041] The generation unit can adjust the generation order based on the relevance of the information entered by the user during generation. For example, the generation unit can prioritize processing highly relevant information and generate a design. For example, the generation unit prioritizes processing highly relevant information based on the relevance of the information entered by the user. The generation unit can also postpone the processing of less relevant information. For example, the generation unit postpones the processing of less relevant information based on the relevance of the information entered by the user. Furthermore, the generation unit can generate an efficient design based on highly relevant information. For example, the generation unit generates an efficient design based on the relevance of the information entered by the user. This makes efficient design possible by adjusting the generation order based on the relevance of the information entered by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the information entered by the user into the generation AI and have the generation AI execute the generation order.

[0042] The service provider can select the optimal service delivery method by referring to the user's past feedback at the time of delivery. For example, the service provider may prioritize providing display methods that the user has previously preferred. For example, the service provider may select the optimal display method based on the user's past feedback. The service provider may also provide improved display methods based on the user's past feedback. For example, the service provider may analyze the user's past feedback and provide a display method that reflects improvements. Furthermore, the service provider may select the optimal display method from the user's past feedback. For example, the service provider may select the optimal display method based on the user's past feedback. This makes it possible to select the optimal service delivery method by referring to the user's past feedback, enabling efficient delivery. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider may input the user's past feedback data into a generative AI and have the generative AI select the optimal service delivery method.

[0043] The service provider can customize the means of delivery based on the user's current project status at the time of delivery. For example, the service provider can prioritize providing information related to the user's ongoing projects. For example, the service provider can prioritize providing relevant information based on the user's current project status. The service provider can also select the optimal means of delivery according to the user's project status. For example, the service provider can select the optimal means of delivery based on the progress of the user's projects. Furthermore, the service provider can provide customized means of delivery based on the progress of the user's projects. For example, the service provider can provide customized means of delivery based on the progress of the user's projects. This makes efficient delivery possible by customizing the means of delivery based on the user's current project status. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's project status data into a generative AI and have the generative AI select the optimal means of delivery.

[0044] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing designs related to that region. For example, the service provider can acquire the user's geographical location information and filter and provide designs related to that region. The service provider can also prioritize providing designs related to the travel destination if the user is traveling. For example, the service provider can filter and provide designs related to the travel destination based on the user's geographical location information. Furthermore, if the service provider is at home, the service provider can prioritize providing designs around the user's home. For example, the service provider can filter and provide designs around the user's home based on the user's geographical location information. In this way, by taking the user's geographical location information into consideration, highly relevant designs can be efficiently provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI perform the filtering of highly relevant designs.

[0045] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can provide relevant designs based on information shared by the user on social media. For example, the service provider can analyze the user's social media activity and filter and provide relevant designs. The service provider can also provide designs related to topics of interest based on the user's social media activity. For example, the service provider can filter and provide relevant designs based on the accounts the user follows on social media. Furthermore, the service provider can prioritize providing highly relevant designs based on the user's social media activity. For example, the service provider can provide relevant designs based on information shared by the user on social media. This allows for the efficient provision of highly relevant designs by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI and have the generative AI perform the filtering of relevant designs.

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

[0047] The reception desk can automatically search for relevant past design data based on user input and provide it as reference information. For example, based on the parts list entered by the user, it can search for past design data that used similar parts and present it to the user. The reception desk can also search for past design data that adopted a similar design policy based on the design policy entered by the user and provide it as reference information. Furthermore, the reception desk can search for past design data of products with similar functions based on the function overview entered by the user and present it to the user. This allows users to proceed with the design more efficiently by referring to past design data.

[0048] The generation unit can evaluate the eco-friendliness of a design based on user input and suggest improvements as needed. For example, it can evaluate the environmental impact of the components used and suggest more environmentally friendly alternatives. It can also evaluate energy efficiency and suggest design changes to reduce energy consumption. Furthermore, it can consider the entire product lifecycle and recommend the use of recyclable materials. This allows users to create environmentally conscious designs.

[0049] The reception desk can visualize the design progress in real time based on user input. For example, it can display the current design progress in graphs and charts based on the parts list entered by the user. The reception desk can also display the design progress in a timeline format based on the design policy entered by the user. Furthermore, the reception desk can display the design progress in a dashboard format based on the function overview entered by the user. This allows users to grasp the design progress in real time and proceed with the design efficiently.

[0050] The reception desk can estimate design costs in real time based on user input. For example, it can sum up the costs of each component based on the parts list entered by the user and display the total cost. The reception desk can also estimate design costs based on the design policy entered by the user. Furthermore, the reception desk can estimate the cost of implementing a function based on the function overview entered by the user. This allows users to understand design costs in real time and proceed with the design efficiently within their budget.

[0051] The reception desk can perform a design risk assessment based on user input and propose risk mitigation measures as needed. For example, it can evaluate the reliability and durability of components based on the parts list entered by the user and identify high-risk components. The reception desk can also evaluate the safety of the design based on the design policy entered by the user and propose risk mitigation measures. Furthermore, the reception desk can evaluate the risks associated with function implementation based on the function overview entered by the user and propose risk mitigation measures. This allows users to understand design risks in advance and take appropriate measures.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The reception desk receives input from the user. User input includes text, images, videos, etc. For example, it receives information such as a functional overview of the product, a parts list, and design guidelines. Step 2: The generation unit uses generation AI to analyze the information received by the reception unit and simultaneously performs exterior and interior design. The generation unit generates the optimal design while considering the balance between the exterior and interior. For example, by prioritizing the exterior design while efficiently arranging the interior layout, it is possible to eliminate wasted space. Step 3: The provider unit provides the user with the design generated by the generator unit. The provider unit provides the generated design to the user and makes modifications and adjustments as needed. The user can review the generated design and instruct on any necessary changes. For example, they can change the placement of parts or modify parts of the design.

[0054] (Example of form 2) The design integration system according to an embodiment of the present invention is a system that utilizes generative AI to simultaneously perform exterior and interior design. This design integration system allows the user to input the required functional overview, parts list, design policy, etc., and the generative AI handles all complex processes, ensuring a perfect result. Specifically, the user first inputs the required functional overview, parts list, design policy, etc. Next, the generative AI analyzes this information and simultaneously performs exterior and interior design. The generative AI generates an optimal design while considering the balance between the exterior and interior. Finally, the generated design is provided to the user, who can make modifications and adjustments as needed. This system integrates and efficiently performs the exterior and interior design processes, improving the functional beauty and usability of the product. Furthermore, by entrusting complex processes to the generative AI, the user's design workload is reduced. For example, the user inputs the required functional overview, parts list, design policy, etc. In this process, the user inputs information using text, images, videos, etc. Next, the generative AI analyzes the input information and simultaneously performs exterior and interior design. The generative AI generates an optimal design while considering the balance between the exterior and interior. For example, by prioritizing exterior design while efficiently arranging the interior layout, wasted space can be eliminated. The generated design is provided to the user, who can then modify or adjust it as needed. The user can review the generated design and instruct on any necessary changes. For example, they can change the placement of parts or modify parts of the design. This system integrates and streamlines the exterior and interior design processes, improving the functional beauty and usability of the product. Furthermore, by entrusting complex processes to the generation AI, the user's design workload is reduced. As a result, the integrated design system enables efficient design by simultaneously handling exterior and interior design.

[0055] The design integration system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the user. User input includes, but is not limited to, text, images, and videos. The reception unit receives, for example, information such as a functional overview, parts list, and design policy required by the user for the product. The generation unit uses a generation AI to analyze the information received by the reception unit and simultaneously performs exterior and interior design. The generation unit generates an optimal design, for example, while considering the balance between the exterior and interior. The generation unit can also use the generation AI to analyze information such as text, images, and videos entered by the user. The generation unit uses the generation AI to simultaneously perform exterior and interior design. For example, the generation unit can eliminate wasted space by prioritizing the exterior design while efficiently arranging the interior layout. The provision unit provides the design generated by the generation unit to the user. The provision unit provides the generated design to the user and allows for modifications and adjustments as needed. The provision unit allows the user to review the generated design and instruct necessary changes. For example, it is possible to change the arrangement of parts or modify parts of the design. As a result, the design integration system according to the embodiment enables efficient design by performing exterior and interior design simultaneously. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user-generated information into the generation AI and have the generation AI perform simultaneous generation of exterior and interior designs. Some or all of the above-described processes in the provision unit may be performed using a generation AI, for example, or without a generation AI. For example, the provision unit can input the generated design into the generation AI and have the generation AI perform the optimal method for providing it to the user.

[0056] The generation unit can generate efficient designs while considering the balance between the exterior and interior. For example, the generation unit can prioritize the exterior design while efficiently arranging the interior layout. For example, the generation unit can optimize the exterior shape while adjusting the placement of internal components. The generation unit can also prioritize the internal function while adjusting the exterior design. For example, the generation unit can optimize the internal functional arrangement while adjusting the exterior design. Furthermore, the generation unit can design to eliminate wasted space while considering the balance between the exterior and interior. For example, the generation unit optimizes the balance between the exterior and interior and designs to minimize wasted space. This eliminates wasted space and enables efficient design by considering the balance between the exterior and interior. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can have a generation AI execute a design that considers the balance between the exterior and interior.

[0057] The generation unit can analyze information from text, images, or videos entered by the user. For example, the generation unit can analyze text information entered by the user. For example, the generation unit can analyze text information using natural language processing technology and reflect it in the design. The generation unit can also analyze image information entered by the user. For example, the generation unit can analyze image information using image recognition technology and reflect it in the design. Furthermore, the generation unit can also analyze video information entered by the user. For example, the generation unit can analyze video information using video analysis technology and reflect it in the design. By analyzing the diverse information entered by the user, it becomes possible to create designs with higher accuracy. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input text, images, or video information entered by the user into a generation AI and have the generation AI perform the analysis.

[0058] The provisioning unit can provide the generated design to the user and allow for modifications and adjustments as needed. For example, the provisioning unit can provide the generated design to the user. For example, the provisioning unit can display the generated design to the user and ask for confirmation. The provisioning unit can also allow the user to review the generated design and make necessary modifications and adjustments. For example, the provisioning unit can enable the user to change the placement of parts or modify parts of the design. Furthermore, the provisioning unit can regenerate the design based on user feedback. For example, the provisioning unit can receive user feedback and instruct the generation unit to regenerate the design. This allows the user to review the generated design and make necessary modifications and adjustments to achieve the optimal design. Some or all of the above processes in the provisioning unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the provisioning unit can input the generated design into a generation AI and have the generation AI execute the optimal method for providing it to the user.

[0059] The reception unit can receive information such as a functional overview, parts list, or design policy required for the product. For example, the reception unit can accept a functional overview from the user. For example, the reception unit can accept a functional overview from the user in text format. The reception unit can also accept a parts list from the user. For example, the reception unit can accept a parts list from the user in image format. Furthermore, the reception unit can also accept a design policy from the user. For example, the reception unit can accept a design policy from the user in video format. This allows the user to input the information required for the product, providing the basic information necessary for the generative AI to perform the optimal design. Some or all of the above processing in the reception unit may be performed using the generative AI, or not. For example, the reception unit can input the information entered by the user into the generative AI and have the generative AI perform the analysis.

[0060] The reception unit can estimate the user's emotions and adjust the timing of input information acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the input timing to provide time for relaxation. For example, the reception unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The reception unit can also accept input immediately if the user is relaxed to facilitate smooth operation. For example, the reception unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the reception unit can accept input quickly to enable efficient operation. For example, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for adjustment of the timing of input information acceptance according to the user's emotions, reducing user stress and enabling efficient input. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above-described processes in the reception area may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception area can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0061] The reception desk can analyze the user's past input history and select an efficient reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can automatically complete relevant input fields based on information the user has previously entered. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze information the user has entered during specific time periods in the past and suggest the most suitable input method for that time period. This allows for efficient input by selecting the optimal reception method through analysis of the user's past input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past input history data into a generative AI and have the generative AI select the optimal reception method.

[0062] The reception unit can filter input information based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving information related to the user's current projects. For example, the reception unit can filter and receive information related to the user's current projects. The reception unit can also filter and receive highly relevant information based on the user's areas of interest. For example, the reception unit can filter input information based on areas the user has shown interest in in the past. Furthermore, the reception unit can prioritize receiving highly relevant information based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving information related to the user's current projects. This allows for the efficient reception of highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input information about the user's projects and areas of interest into a generative AI and have the generative AI perform the filtering.

[0063] The reception desk can estimate the user's emotions and prioritize the information it receives based on those emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. For instance, it might capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Similarly, if the user is relaxed, the reception desk can prioritize receiving detailed information. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the reception desk can prioritize receiving information that requires quick processing. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for efficient reception of important information by prioritizing it according to 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. Some or all of the above-described processes in the reception area may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception area can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0064] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving input information. For example, if the user is in a specific region, the reception unit can prioritize receiving information related to that region. For example, the reception unit can obtain the user's geographical location information and filter and receive information related to that region. Also, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, the reception unit can filter and receive information related to the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the reception unit can prioritize receiving information around their home. For example, the reception unit can filter and receive information around their home based on the user's geographical location information. In this way, highly relevant information can be efficiently received by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI and have the generative AI perform the filtering of highly relevant information.

[0065] The reception unit can analyze the user's social media activity and receive relevant information when receiving input information. For example, the reception unit can receive relevant input information based on information shared by the user on social media. For example, the reception unit can analyze the user's social media activity and filter and receive relevant information. The reception unit can also receive information related to topics of interest from the user's social media activity. For example, the reception unit can filter and receive relevant information based on the accounts the user follows on social media. Furthermore, the reception unit can prioritize receiving highly relevant information based on the user's social media activity. For example, the reception unit can receive relevant input information based on information shared by the user on social media. This allows for the efficient reception of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI and have the generative AI perform the filtering of relevant information.

[0066] The generation unit can estimate the user's emotions and adjust the design's expression based on those emotions. For example, if the user is relaxed, the generation unit can generate a design with soft colors and many curves. For example, the generation unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also generate a simple, linear design if the user is in a hurry. For example, the generation unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate a design with vibrant colors and bold designs. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the design to be adjusted according to the user's emotions, providing the optimal design for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0067] The generation unit can adjust the specific details of the design during generation, taking into account the balance between the exterior and interior. For example, the generation unit can prioritize the exterior design while efficiently arranging the interior layout. For example, the generation unit can optimize the shape of the exterior while adjusting the placement of internal components. The generation unit can also prioritize the interior design while adjusting the exterior design. For example, the generation unit can optimize the arrangement of internal functions while adjusting the exterior design. Furthermore, the generation unit can design to eliminate wasted space by considering the balance between the exterior and interior. For example, the generation unit optimizes the balance between the exterior and interior and designs to minimize wasted space. This eliminates wasted space and enables efficient design by considering the balance between the exterior and interior. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can have a generation AI execute a design that takes into account the balance between the exterior and interior.

[0068] The generation unit can apply different generation algorithms depending on the category of information entered by the user during generation. For example, the generation unit can apply an algorithm to generate a detailed design based on text information. For example, the generation unit can analyze the text information entered by the user and apply an algorithm to generate a detailed design. The generation unit can also apply an algorithm to generate a visually superior design based on image information. For example, the generation unit can analyze the image information entered by the user and apply an algorithm to generate a visually superior design. Furthermore, the generation unit can apply an algorithm to generate a dynamic design based on video information. For example, the generation unit can analyze the video information entered by the user and apply an algorithm to generate a dynamic design. This makes it possible to create more accurate designs by applying the optimal generation algorithm according to the category of information entered by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can cause the generation AI to execute the optimal generation algorithm according to the category of information entered by the user.

[0069] The generation unit can estimate the user's emotions and adjust the length of the generated design based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise design. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also generate a longer design with detailed explanations if the user is relaxed. For example, the generation unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate a design with visually stimulating effects. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the design to be adjusted according to the user's emotions, providing the optimal design for the user. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0070] The generation unit can determine the generation priority based on the submission timing of the information entered by the user during the generation process. For example, the generation unit can prioritize the processing of urgent information and generate designs quickly. For example, the generation unit prioritizes the processing of highly urgent information based on the submission timing of the information entered by the user. The generation unit can also prioritize the processing of information with an approaching submission deadline. For example, the generation unit prioritizes the processing of information with an approaching submission deadline based on the submission deadline of the information entered by the user. Furthermore, the generation unit can postpone the processing of information with ample time for submission. For example, the generation unit postpones the processing of information with ample time for submission based on the submission timing of the information entered by the user. This enables efficient design by determining the generation priority based on the submission timing of the information entered by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the submission timing of the information entered by the user into the generation AI and have the generation AI execute the generation priority.

[0071] The generation unit can adjust the generation order based on the relevance of the information entered by the user during generation. For example, the generation unit can prioritize processing highly relevant information and generate a design. For example, the generation unit prioritizes processing highly relevant information based on the relevance of the information entered by the user. The generation unit can also postpone the processing of less relevant information. For example, the generation unit postpones the processing of less relevant information based on the relevance of the information entered by the user. Furthermore, the generation unit can generate an efficient design based on highly relevant information. For example, the generation unit generates an efficient design based on the relevance of the information entered by the user. This makes efficient design possible by adjusting the generation order based on the relevance of the information entered by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the information entered by the user into the generation AI and have the generation AI execute the generation order.

[0072] The service provider can estimate the user's emotions and adjust the display method of the design based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. For example, the service provider can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The service provider can also provide a display method that includes detailed information if the user is relaxed. For example, the service provider can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows the service provider to provide the optimal display for the user by adjusting the display method of the design 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provision unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The service provider can select the optimal service delivery method by referring to the user's past feedback at the time of delivery. For example, the service provider may prioritize providing display methods that the user has previously preferred. For example, the service provider may select the optimal display method based on the user's past feedback. The service provider may also provide improved display methods based on the user's past feedback. For example, the service provider may analyze the user's past feedback and provide a display method that reflects improvements. Furthermore, the service provider may select the optimal display method from the user's past feedback. For example, the service provider may select the optimal display method based on the user's past feedback. This makes it possible to select the optimal service delivery method by referring to the user's past feedback, enabling efficient delivery. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider may input the user's past feedback data into a generative AI and have the generative AI select the optimal service delivery method.

[0074] The service provider can customize the means of delivery based on the user's current project status at the time of delivery. For example, the service provider can prioritize providing information related to the user's ongoing projects. For example, the service provider can prioritize providing relevant information based on the user's current project status. The service provider can also select the optimal means of delivery according to the user's project status. For example, the service provider can select the optimal means of delivery based on the progress of the user's projects. Furthermore, the service provider can provide customized means of delivery based on the progress of the user's projects. For example, the service provider can provide customized means of delivery based on the progress of the user's projects. This makes efficient delivery possible by customizing the means of delivery based on the user's current project status. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's project status data into a generative AI and have the generative AI select the optimal means of delivery.

[0075] The service provider can estimate the user's emotions and determine the priority of the designs to be provided based on those estimated emotions. For example, if the user is stressed, the service provider will prioritize providing important designs. For instance, the service provider might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Similarly, if the user is relaxed, the service provider can prioritize providing detailed designs. For example, the service provider might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the service provider can prioritize providing designs that require quick processing. For example, the service provider might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the efficient provision of important designs by determining the priority of the designs to be provided according to 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. Some or all of the processing described above in the service provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provision unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing designs related to that region. For example, the service provider can acquire the user's geographical location information and filter and provide designs related to that region. The service provider can also prioritize providing designs related to the travel destination if the user is traveling. For example, the service provider can filter and provide designs related to the travel destination based on the user's geographical location information. Furthermore, if the service provider is at home, the service provider can prioritize providing designs around the user's home. For example, the service provider can filter and provide designs around the user's home based on the user's geographical location information. In this way, by taking the user's geographical location information into consideration, highly relevant designs can be efficiently provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI perform the filtering of highly relevant designs.

[0077] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can provide relevant designs based on information shared by the user on social media. For example, the service provider can analyze the user's social media activity and filter and provide relevant designs. The service provider can also provide designs related to topics of interest based on the user's social media activity. For example, the service provider can filter and provide relevant designs based on the accounts the user follows on social media. Furthermore, the service provider can prioritize providing highly relevant designs based on the user's social media activity. For example, the service provider can provide relevant designs based on information shared by the user on social media. This allows for the efficient provision of highly relevant designs by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI and have the generative AI perform the filtering of relevant designs. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input such as text, images, and videos from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simultaneously performs exterior and internal design using generation AI. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated design to the user and makes modifications and adjustments as needed. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simultaneously performs exterior and internal design using generation AI. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated design to the user, allowing for modifications and adjustments as needed. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simultaneously performs exterior and internal design using generation AI. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the generated design to the user, allowing for modifications and adjustments as needed. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simultaneously performs exterior and internal design using generation AI. The provision unit is implemented by the speaker 240 of the robot 414 and provides the generated design to the user, allowing for modifications and adjustments as needed.

[0078] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0079] The reception desk can automatically search for relevant past design data based on user input and provide it as reference information. For example, based on the parts list entered by the user, it can search for past design data that used similar parts and present it to the user. The reception desk can also search for past design data that adopted a similar design policy based on the design policy entered by the user and provide it as reference information. Furthermore, the reception desk can search for past design data of products with similar functions based on the function overview entered by the user and present it to the user. This allows users to proceed with the design more efficiently by referring to past design data.

[0080] The generation unit can estimate the user's emotions and adjust the complexity of the design based on those emotions. For example, if the user is stressed, it can generate a simple and intuitive design. For instance, the generation unit might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also generate detailed and complex designs if the user is relaxed. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate visually stimulating designs. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the design complexity to be adjusted according to the user's emotions, providing the optimal design for the user.

[0081] The generation unit can evaluate the eco-friendliness of a design based on user input and suggest improvements as needed. For example, it can evaluate the environmental impact of the components used and suggest more environmentally friendly alternatives. It can also evaluate energy efficiency and suggest design changes to reduce energy consumption. Furthermore, it can consider the entire product lifecycle and recommend the use of recyclable materials. This allows users to create environmentally conscious designs.

[0082] The system can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is stressed, it can provide concise and positive feedback. For instance, the system might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The system can also provide detailed feedback if the user is relaxed. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, it can provide visually engaging feedback. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to adjust the feedback method according to the user's emotions, providing the most optimal feedback for the user.

[0083] The reception desk can visualize the design progress in real time based on user input. For example, it can display the current design progress in graphs and charts based on the parts list entered by the user. The reception desk can also display the design progress in a timeline format based on the design policy entered by the user. Furthermore, the reception desk can display the design progress in a dashboard format based on the function overview entered by the user. This allows users to grasp the design progress in real time and proceed with the design efficiently.

[0084] The design generator can estimate the user's emotions and adjust the design's colors based on those emotions. For example, if the user is relaxed, it can generate a design using soft colors. For instance, the generator can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is stressed, the generator can generate a design using calming colors. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is excited, the generator can generate a design using vibrant colors. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the design to be adjusted according to the user's emotions, providing the user with the optimal design.

[0085] The reception desk can estimate design costs in real time based on user input. For example, it can sum up the costs of each component based on the parts list entered by the user and display the total cost. The reception desk can also estimate design costs based on the design policy entered by the user. Furthermore, the reception desk can estimate the cost of implementing a function based on the function overview entered by the user. This allows users to understand design costs in real time and proceed with the design efficiently within their budget.

[0086] The service provider can estimate the user's emotions and adjust the interactivity of the design based on those emotions. For example, if the user is relaxed, it can provide a design with many interactive elements. For instance, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is stressed, the service provider can provide a simple and intuitive interface. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Additionally, if the user is excited, the service provider can provide a visually stimulating interface. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the service provider to adjust interactivity according to the user's emotions, providing the optimal interface for the user.

[0087] The reception desk can perform a design risk assessment based on user input and propose risk mitigation measures as needed. For example, it can evaluate the reliability and durability of components based on the parts list entered by the user and identify high-risk components. The reception desk can also evaluate the safety of the design based on the design policy entered by the user and propose risk mitigation measures. Furthermore, the reception desk can evaluate the risks associated with function implementation based on the function overview entered by the user and propose risk mitigation measures. This allows users to understand design risks in advance and take appropriate measures.

[0088] The system can estimate the user's emotions and adjust the navigation design based on those emotions. For example, if the user is relaxed, it can provide detailed navigation. For instance, the system might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The system can also provide concise and intuitive navigation if the user is stressed. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is excited, it can provide visually appealing navigation. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to adjust the navigation method according to the user's emotions, providing optimal navigation for the user.

[0089] The following briefly describes the processing flow for example form 2.

[0090] Step 1: The reception desk receives input from the user. User input includes text, images, videos, etc. For example, it receives information such as a functional overview of the product, a parts list, and design guidelines. Step 2: The generation unit uses generation AI to analyze the information received by the reception unit and simultaneously performs exterior and interior design. The generation unit generates the optimal design while considering the balance between the exterior and interior. For example, by prioritizing the exterior design while efficiently arranging the interior layout, it is possible to eliminate wasted space. Step 3: The provider unit provides the user with the design generated by the generator unit. The provider unit provides the generated design to the user and makes modifications and adjustments as needed. The user can review the generated design and instruct on any necessary changes. For example, they can change the placement of parts or modify parts of the design.

[0091] 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.

[0092] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0093] 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.

[0094] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0095] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.).

[0107] 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.

[0108] 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. 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.

[0109] 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.

[0110] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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. 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.

[0125] 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.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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. 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.

[0142] 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.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of Symbols]

[0163] 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. A reception area that receives input from users, The generation unit analyzes the information received by the reception unit and simultaneously performs exterior design and interior design, The system includes a providing unit that provides the design generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is Creating an efficient design while considering the balance between the exterior and interior. The system according to feature 1.

3. The generating unit is Analyzes information from text, images, or videos entered by the user. The system according to feature 1.

4. The aforementioned supply unit is, The generated design is provided to the user, and modifications and adjustments are made as needed. The system according to feature 1.

5. The aforementioned reception unit is We accept information regarding the product's required functional overview, parts list, or design guidelines. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input information acceptance based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history to select the most efficient method of processing their input. The system according to feature 1.

8. The aforementioned reception unit is When receiving input information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

Images