system

Abstract

To provide a system for efficiently generating a design that matches a user's sensibility. [Solution] The system includes a sensibility input unit, a design generation unit, and a design provision unit. The sensibility input unit inputs information based on the user's sensibility. The design generation unit generates a design based on the information input by the sensibility input unit. The design provision unit provides the design generated by the design generation unit as a 3D model.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently generate designs that match the user's sensibilities.

[0005] The system according to the embodiment aims to efficiently generate a design that matches the user's sensibilities. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensibility input unit, a design generation unit, and a design provision unit. The sensibility input unit inputs information based on a user's sensibility. The design generation unit generates a design based on the information input by the sensibility input unit. The design provision unit provides the design generated by the design generation unit as a 3D model. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate a design that matches the user's sensibilities. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A room design system according to an embodiment of the present invention provides a room design that reflects a user's sensibilities and preferences. In this room design system, a user inputs their sensibilities and preferences, and AI analyzes the input information to generate a room design that matches the user's sensibilities. The generated design is provided to the user, who can coordinate their room based on the design. For example, when inputting their sensibilities and preferences, a user can specifically input their preferences for color, material, style, etc. For example, they can input information such as "I like rooms with warm colors" or "I prefer designs that use a lot of wood." Next, AI analyzes the input information and generates a room design that matches the user's sensibilities. Based on the input information, the AI ​​generates an optimal design by combining colors, materials, style, etc. For example, a room design that combines warm colors with a lot of wood is generated. The generated design is provided to the user, who can then coordinate their room based on the design. For example, they can select furniture and interior decor based on the generated design and coordinate their room. This system allows users to easily design a room that matches their sensibilities and preferences. Furthermore, because the design is generated by AI, high-quality designs can be easily obtained without having to hire a professional designer. This allows the room design system to provide a room design that matches the user's sensibilities and preferences.

[0029] A room design system according to an embodiment includes a sensibility input unit, a design generation unit, and a design provision unit. The sensibility input unit inputs user preferences, such as color, material, and style. Examples of user-input information include, but are not limited to, warm colors, the use of wood, and a modern style. The sensibility input unit can accept information in the form of text, voice, image, or other formats. For example, if a user inputs, "I like rooms with warm colors," the sensibility input unit accepts the information in text format. Furthermore, if a user inputs, "I like designs that use a lot of wood," the sensibility input unit can convert the information into text using voice recognition technology. Furthermore, if a user uploads an image of a preferred design, the sensibility input unit can analyze the image and extract information about colors and materials. The design generation unit generates a room design that matches the user's sensibility based on the information input by the sensibility input unit. The design generation unit learns past design data using, for example, a machine learning algorithm and generates a design that matches the user's preferences. The design generation unit can also generate an optimal design based on the user's input information using a rule-based generation method. For example, if a user prefers "warm colors" and "use of wood," the design generation unit generates a design that uses warm colors and a lot of wood based on that information. The design providing unit provides the design generated by the design generation unit as a 3D model. The design providing unit can display the 3D model on a web browser, for example. The design providing unit can also display the 3D model using a dedicated application. For example, the design providing unit provides the generated design to the user as a 3D model, and the user can coordinate a room based on the 3D model. In this way, the room design system according to the embodiment can provide a room design that suits the user's sensibilities and preferences.

[0030] The sensibility input unit allows the user to input preferences for color, material, and style in the form of selecting from options. The sensibility input unit allows the user to input preferences for color, material, and style in the form of selecting from options. For example, the sensibility input unit allows the user to select a favorite color from a color palette. The sensibility input unit also allows the user to select a favorite material from a material type. The sensibility input unit also allows the user to select a favorite style from a style category. For example, when the user selects "warm color" from the options, the sensibility input unit causes the design generation unit to generate a design based on that information. When the user selects "wood" from the options, the sensibility input unit causes the design generation unit to generate a design based on that information. This allows the user to easily input specific preferences.

[0031] The design generation unit can learn from past design data and generate a design that matches the user's sensibilities. The design generation unit, for example, learns from past design data and generates a design that matches the user's sensibilities. For example, the design generation unit combines colors and materials that match the user's preferences based on past design data. The design generation unit can also suggest furniture arrangements that match the user's style based on past design data. Furthermore, the design generation unit can generate a lighting design that matches the user's sensibilities based on past design data. For example, if the user prefers "warm colors" and "the use of wood" based on past design data, the design generation unit generates a design that uses warm colors and a lot of wood based on that information. This makes it possible to generate designs with greater accuracy by utilizing past design data.

[0032] The design providing unit can provide the generated design as a 3D model. For example, the design providing unit can display the 3D model on a web browser. The design providing unit can also display the 3D model using a dedicated application. For example, the design providing unit can provide the generated design to a user as a 3D model, allowing the user to coordinate a room based on the 3D model. This allows the user to visually check the design.

[0033] The affective input unit can analyze the user's past input history and suggest the optimal input method. The affective input unit can, for example, analyze the user's past input history and suggest the optimal input method. For example, the affective input unit can automatically display colors and materials selected by the user in the past as candidates. The affective input unit can also preferentially suggest input methods (text, voice, etc.) that the user has used in the past. Furthermore, the affective input unit can predict and suggest design elements that the user will prefer at a specific time period based on the user's past input history. This makes it possible to suggest a more appropriate input method by utilizing the user's past input history.

[0034] The sensibility input unit can customize input items based on the user's living situation and areas of interest when inputting sensibility. For example, the sensibility input unit customizes input items based on the user's living situation and areas of interest when inputting sensibility. For example, if the user has just moved into a new house, the sensibility input unit can provide design options related to moving. Also, if the user is interested in a particular season, the sensibility input unit can suggest design elements that suit that season. Furthermore, if the user has a particular hobby, the sensibility input unit can provide design options related to that hobby. In this way, input items can be provided that correspond to the user's living situation and areas of interest.

[0035] The sensibility input unit can prioritize displaying highly relevant input items based on the user's geographical location information when inputting sensibility. For example, the sensibility input unit prioritizes displaying highly relevant input items based on the user's geographical location information when inputting sensibility. For example, if the user lives in a cold region, the sensibility input unit can prioritize displaying warm colors and materials. Furthermore, if the user lives in an urban area, the sensibility input unit can prioritize displaying modern design elements. Furthermore, if the user lives by the sea, the sensibility input unit can prioritize displaying sea-themed design elements. This makes it possible to provide optimal input items based on the user's geographical location information.

[0036] The sentiment input unit can analyze the user's social media activity at the time of sentiment input and suggest related input items. For example, the sentiment input unit can analyze the user's social media activity at the time of sentiment input and suggest related input items. For example, the sentiment input unit can suggest related design elements based on images and posts shared by the user on social media. The sentiment input unit can also suggest design elements based on the style of influencers the user follows. Furthermore, the sentiment input unit can suggest design elements based on trends in online communities in which the user participates. This makes it possible to provide optimal input items based on the user's social media activity.

[0037] The design generation unit can apply an appropriate design algorithm by referring to past design data when generating a design. For example, the design generation unit can apply an appropriate design algorithm by referring to past design data when generating a design. For example, the design generation unit can combine colors and materials that suit the user's preferences based on past design data. The design generation unit can also suggest furniture arrangements that suit the user's style based on past design data. Furthermore, the design generation unit can generate a lighting design that suits the user's sensibilities based on past design data. This makes it possible to generate designs with higher accuracy by utilizing past design data.

[0038] The design generation unit can customize design elements based on the user's living situation when generating a design. For example, the design generation unit customizes design elements based on the user's living situation when generating a design. For example, if the user has just moved into a new house, the design generation unit can suggest design elements related to moving. Also, if the user is interested in a particular season, the design generation unit can suggest design elements that suit that season. Furthermore, if the user has a particular hobby, the design generation unit can suggest design elements related to that hobby. This makes it possible to provide optimal design elements according to the user's living situation.

[0039] The design generation unit can determine appropriate design priorities based on the user's submission time when generating a design. The design generation unit, for example, determines appropriate design priorities based on the user's submission time when generating a design. For example, if the user is in a hurry, the design generation unit generates a design that prioritizes the most important design elements. Also, if the user has time, the design generation unit can generate a design that includes detailed design elements. Furthermore, if the user has set a specific deadline, the design generation unit can generate a design that meets that deadline. This makes it possible to provide optimal design priorities based on the user's submission time.

[0040] The design generation unit can improve the accuracy of the design by referring to past designs related to the user when generating a design. The design generation unit can improve the accuracy of the design by referring to past designs related to the user when generating a design. For example, the design generation unit adjusts the current design based on designs created by the user in the past. The design generation unit can also generate the current design based on design elements that the user liked in the past. Furthermore, the design generation unit can generate the current design by eliminating design elements that the user avoided in the past. This makes it possible to generate a more accurate design by utilizing the user's past designs.

[0041] The design providing unit can select the optimal display method by referring to the user's past design browsing history when providing a design. For example, the design providing unit can select the optimal display method by referring to the user's past design browsing history when providing a design. For example, the design providing unit can adjust the display method of the current 3D model based on designs the user has previously viewed. The design providing unit can also display the current 3D model based on design elements that the user has previously preferred. Furthermore, the design providing unit can display the current 3D model by excluding design elements that the user has previously avoided. This makes it possible to provide the optimal display method by utilizing the user's past design browsing history.

[0042] The design providing unit can provide an appropriate 3D model display based on the user's current device information when providing a design. For example, the design providing unit can provide an appropriate 3D model display based on the user's current device information when providing a design. For example, if the user is using a smartphone, the design providing unit can display a 3D model that matches the screen size. Also, if the user is using a tablet, the design providing unit can display a 3D model optimized for a large screen. Furthermore, if the user is using a desktop, the design providing unit can display a high-resolution 3D model. This makes it possible to provide an optimal 3D model display based on the user's device information.

[0043] The design providing unit can provide an appropriate 3D model based on the user's geographical location information when providing a design. For example, the design providing unit can provide an appropriate 3D model based on the user's geographical location information when providing a design. For example, if the user lives in a cold region, the design providing unit can provide a 3D model using warm colors and materials. In addition, if the user lives in an urban area, the design providing unit can provide a 3D model with a modern design. Furthermore, if the user lives by the sea, the design providing unit can provide a 3D model with a sea theme. This makes it possible to provide an optimal 3D model based on the user's geographical location information.

[0044] The design providing unit can analyze the user's social media activity and suggest related 3D models when providing a design. For example, the design providing unit can analyze the user's social media activity and suggest related 3D models when providing a design. For example, the design providing unit can suggest related 3D models based on images and posts shared by the user on social media. The design providing unit can also suggest 3D models based on the style of influencers the user follows. Furthermore, the design providing unit can suggest 3D models based on trends in online communities in which the user participates. This makes it possible to provide the optimal 3D model based on the user's social media activity.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The affective input unit can analyze the user's past input history and suggest the optimal input method. For example, the affective input unit can automatically display colors and materials selected by the user in the past as candidates. The affective input unit can also prioritize suggestions for input methods (text, voice, etc.) that the user has used in the past. Furthermore, the affective input unit can predict and suggest design elements that the user will prefer at a specific time period based on the user's past input history. This makes it possible to suggest a more appropriate input method by utilizing the user's past input history.

[0047] The sentiment input unit can customize input items based on the user's lifestyle and areas of interest when inputting sentiment. For example, if the user has just moved into a new house, the sentiment input unit can provide design options related to moving. If the user is interested in a particular season, the sentiment input unit can also suggest design elements that suit that season. Furthermore, if the user has a particular hobby, the sentiment input unit can also provide design options related to that hobby. This makes it possible to provide input items according to the user's lifestyle and areas of interest.

[0048] The sensibility input unit can prioritize displaying highly relevant input items based on the user's geographical location information when inputting sensibility. For example, if the user lives in a cold region, the sensibility input unit can prioritize displaying warm colors and materials. Also, if the user lives in an urban area, the sensibility input unit can prioritize displaying modern design elements. Furthermore, if the user lives by the sea, the sensibility input unit can prioritize displaying sea-themed design elements. This makes it possible to provide optimal input items based on the user's geographical location information.

[0049] The design generation unit can refer to past design data and apply an appropriate design algorithm when generating a design. For example, the design generation unit can combine colors and materials that match the user's preferences based on past design data. The design generation unit can also suggest furniture layouts that match the user's style based on past design data. Furthermore, the design generation unit can generate lighting designs that match the user's sensibilities based on past design data. This makes it possible to generate designs with greater accuracy by utilizing past design data.

[0050] The design generation unit can customize design elements based on the user's living situation when generating a design. For example, if the user has just moved into a new house, the design generation unit can suggest design elements related to moving. Also, if the user is interested in a particular season, the design generation unit can suggest design elements that suit that season. Furthermore, if the user has a particular hobby, the design generation unit can suggest design elements related to that hobby. This makes it possible to provide optimal design elements according to the user's living situation.

[0051] The design generation unit can determine appropriate design priorities based on the user's submission time when generating a design. For example, if the user is in a hurry, the design generation unit can generate designs that prioritize the most important design elements. Alternatively, if the user has time, the design generation unit can generate designs that include detailed design elements. Furthermore, if the user has set a specific deadline, the design generation unit can generate designs that meet that deadline. This makes it possible to provide optimal design priorities based on the user's submission time.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The sentiment input unit receives information about the user's preferences for color, material, style, etc. The user can input information such as warm colors, the use of wood, and a modern style, for example. The sentiment input unit accepts information in the form of text input, voice input, image input, and so on. For example, if the user inputs text such as "I like rooms with warm colors," the information is accepted. In the case of voice input, the voice input is converted into text using voice recognition technology. In the case of image input, the uploaded image is analyzed to extract color and material information. Step 2: The design generation unit generates a room design that matches the user's sensibilities based on the information input by the sensibility input unit. The design generation unit uses a machine learning algorithm to learn from past design data and generate a design that matches the user's preferences. It can also use a rule-based generation method to generate an optimal design based on the user's input information. For example, if the user prefers "warm colors" and "the use of wood," it will generate a design that uses warm colors and a lot of wood based on that information. Step 3: The design provider provides the design generated by the design generator as a 3D model. The design provider can display the 3D model on a web browser or using a dedicated application. For example, the generated design can be provided to the user as a 3D model, allowing the user to coordinate a room based on the 3D model.

[0054] (Example 2) A room design system according to an embodiment of the present invention provides a room design that reflects a user's sensibilities and preferences. In this room design system, a user inputs their sensibilities and preferences, and AI analyzes the input information to generate a room design that matches the user's sensibilities. The generated design is provided to the user, who can coordinate their room based on the design. For example, when inputting their sensibilities and preferences, a user can specifically input their preferences for color, material, style, etc. For example, they can input information such as "I like rooms with warm colors" or "I prefer designs that use a lot of wood." Next, AI analyzes the input information and generates a room design that matches the user's sensibilities. Based on the input information, the AI ​​generates an optimal design by combining colors, materials, style, etc. For example, a room design that combines warm colors with a lot of wood is generated. The generated design is provided to the user, who can then coordinate their room based on the design. For example, they can select furniture and interior decor based on the generated design and coordinate their room. This system allows users to easily design a room that matches their sensibilities and preferences. Furthermore, because the design is generated by AI, high-quality designs can be easily obtained without having to hire a professional designer. This allows the room design system to provide a room design that matches the user's sensibilities and preferences.

[0055] A room design system according to an embodiment includes a sensibility input unit, a design generation unit, and a design provision unit. The sensibility input unit inputs user preferences, such as color, material, and style. Examples of user-input information include, but are not limited to, warm colors, the use of wood, and a modern style. The sensibility input unit can accept information in the form of text, voice, image, or other formats. For example, if a user inputs, "I like rooms with warm colors," the sensibility input unit accepts the information in text format. Furthermore, if a user inputs, "I like designs that use a lot of wood," the sensibility input unit can convert the information into text using voice recognition technology. Furthermore, if a user uploads an image of a preferred design, the sensibility input unit can analyze the image and extract information about colors and materials. The design generation unit generates a room design that matches the user's sensibility based on the information input by the sensibility input unit. The design generation unit learns past design data using, for example, a machine learning algorithm and generates a design that matches the user's preferences. The design generation unit can also generate an optimal design based on the user's input information using a rule-based generation method. For example, if a user prefers "warm colors" and "use of wood," the design generation unit generates a design that uses warm colors and a lot of wood based on that information. The design providing unit provides the design generated by the design generation unit as a 3D model. The design providing unit can display the 3D model on a web browser, for example. The design providing unit can also display the 3D model using a dedicated application. For example, the design providing unit provides the generated design to the user as a 3D model, and the user can coordinate a room based on the 3D model. In this way, the room design system according to the embodiment can provide a room design that suits the user's sensibilities and preferences.

[0056] The sensibility input unit allows the user to input preferences for color, material, and style in the form of selecting from options. The sensibility input unit allows the user to input preferences for color, material, and style in the form of selecting from options. For example, the sensibility input unit allows the user to select a favorite color from a color palette. The sensibility input unit also allows the user to select a favorite material from a material type. The sensibility input unit also allows the user to select a favorite style from a style category. For example, when the user selects "warm color" from the options, the sensibility input unit causes the design generation unit to generate a design based on that information. When the user selects "wood" from the options, the sensibility input unit causes the design generation unit to generate a design based on that information. This allows the user to easily input specific preferences.

[0057] The design generation unit can learn from past design data and generate a design that matches the user's sensibilities. The design generation unit, for example, learns from past design data and generates a design that matches the user's sensibilities. For example, the design generation unit combines colors and materials that match the user's preferences based on past design data. The design generation unit can also suggest furniture arrangements that match the user's style based on past design data. Furthermore, the design generation unit can generate a lighting design that matches the user's sensibilities based on past design data. For example, if the user prefers "warm colors" and "the use of wood" based on past design data, the design generation unit generates a design that uses warm colors and a lot of wood based on that information. This makes it possible to generate designs with greater accuracy by utilizing past design data.

[0058] The design providing unit can provide the generated design as a 3D model. For example, the design providing unit can display the 3D model on a web browser. The design providing unit can also display the 3D model using a dedicated application. For example, the design providing unit can provide the generated design to a user as a 3D model, allowing the user to coordinate a room based on the 3D model. This allows the user to visually check the design.

[0059] The affective input unit can estimate a user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, the affective input unit can estimate a user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, if the user is relaxed, the affective input unit can provide an interface with soft colors and a simple design. If the user is stressed, the affective input unit can provide a visually soothing color scheme and a simple input method. If the user is excited, the affective input unit can provide bright colors and a variety of options. This makes it possible to provide an optimal input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0060] The affective input unit can analyze the user's past input history and suggest the optimal input method. The affective input unit can, for example, analyze the user's past input history and suggest the optimal input method. For example, the affective input unit can automatically display colors and materials selected by the user in the past as candidates. The affective input unit can also preferentially suggest input methods (text, voice, etc.) that the user has used in the past. Furthermore, the affective input unit can predict and suggest design elements that the user will prefer at a specific time period based on the user's past input history. This makes it possible to suggest a more appropriate input method by utilizing the user's past input history.

[0061] The sensibility input unit can customize input items based on the user's living situation and areas of interest when inputting sensibility. For example, the sensibility input unit customizes input items based on the user's living situation and areas of interest when inputting sensibility. For example, if the user has just moved into a new house, the sensibility input unit can provide design options related to moving. Also, if the user is interested in a particular season, the sensibility input unit can suggest design elements that suit that season. Furthermore, if the user has a particular hobby, the sensibility input unit can provide design options related to that hobby. In this way, input items can be provided that correspond to the user's living situation and areas of interest.

[0062] The emotion input unit can estimate the user's emotion and determine the priority of input items based on the estimated user's emotion. The emotion input unit can, for example, estimate the user's emotion and determine the priority of input items based on the estimated user's emotion. For example, when the user is relaxed, the emotion input unit can prioritize displaying detailed input items. Furthermore, when the user is stressed, the emotion input unit can also prioritize displaying basic input items. Furthermore, when the user is excited, the emotion input unit can prioritize displaying a variety of options. This makes it possible to provide an optimal priority of input items according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0063] The sensibility input unit can prioritize displaying highly relevant input items based on the user's geographical location information when inputting sensibility. For example, the sensibility input unit prioritizes displaying highly relevant input items based on the user's geographical location information when inputting sensibility. For example, if the user lives in a cold region, the sensibility input unit can prioritize displaying warm colors and materials. Furthermore, if the user lives in an urban area, the sensibility input unit can prioritize displaying modern design elements. Furthermore, if the user lives by the sea, the sensibility input unit can prioritize displaying sea-themed design elements. This makes it possible to provide optimal input items based on the user's geographical location information.

[0064] The sentiment input unit can analyze the user's social media activity at the time of sentiment input and suggest related input items. For example, the sentiment input unit can analyze the user's social media activity at the time of sentiment input and suggest related input items. For example, the sentiment input unit can suggest related design elements based on images and posts shared by the user on social media. The sentiment input unit can also suggest design elements based on the style of influencers the user follows. Furthermore, the sentiment input unit can suggest design elements based on trends in online communities in which the user participates. This makes it possible to provide optimal input items based on the user's social media activity.

[0065] The design generation unit can estimate the user's emotions and adjust the design expression method based on the estimated user emotions. The design generation unit, for example, estimates the user's emotions and adjusts the design expression method based on the estimated user emotions. For example, if the user is relaxed, the design generation unit can generate soft colors and a simple design. Also, if the user is stressed, the design generation unit can generate visually soothing colors and a concise design. Furthermore, if the user is excited, the design generation unit can generate bright colors and a variety of design elements. This makes it possible to provide an optimal design expression according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The design generation unit can apply an appropriate design algorithm by referring to past design data when generating a design. For example, the design generation unit can apply an appropriate design algorithm by referring to past design data when generating a design. For example, the design generation unit can combine colors and materials that suit the user's preferences based on past design data. The design generation unit can also suggest furniture arrangements that suit the user's style based on past design data. Furthermore, the design generation unit can generate a lighting design that suits the user's sensibilities based on past design data. This makes it possible to generate designs with higher accuracy by utilizing past design data.

[0067] The design generation unit can customize design elements based on the user's living situation when generating a design. For example, the design generation unit customizes design elements based on the user's living situation when generating a design. For example, if the user has just moved into a new house, the design generation unit can suggest design elements related to moving. Also, if the user is interested in a particular season, the design generation unit can suggest design elements that suit that season. Furthermore, if the user has a particular hobby, the design generation unit can suggest design elements related to that hobby. This makes it possible to provide optimal design elements according to the user's living situation.

[0068] The design generation unit can estimate the user's emotions and adjust the level of detail of the design based on the estimated user's emotions. The design generation unit, for example, estimates the user's emotions and adjusts the level of detail of the design based on the estimated user's emotions. For example, the design generation unit generates a design including detailed design elements when the user is relaxed. The design generation unit can also generate a simple, highly visible design when the user is stressed. Furthermore, the design generation unit can also generate a design including a variety of design elements when the user is excited. This makes it possible to provide an optimal level of design detail according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The design generation unit can determine appropriate design priorities based on the user's submission time when generating a design. The design generation unit, for example, determines appropriate design priorities based on the user's submission time when generating a design. For example, if the user is in a hurry, the design generation unit generates a design that prioritizes the most important design elements. Also, if the user has time, the design generation unit can generate a design that includes detailed design elements. Furthermore, if the user has set a specific deadline, the design generation unit can generate a design that meets that deadline. This makes it possible to provide optimal design priorities based on the user's submission time.

[0070] The design generation unit can improve the accuracy of the design by referring to past designs related to the user when generating a design. The design generation unit can improve the accuracy of the design by referring to past designs related to the user when generating a design. For example, the design generation unit adjusts the current design based on designs created by the user in the past. The design generation unit can also generate the current design based on design elements that the user liked in the past. Furthermore, the design generation unit can generate the current design by eliminating design elements that the user avoided in the past. This makes it possible to generate a more accurate design by utilizing the user's past designs.

[0071] The design providing unit can estimate the user's emotions and adjust the display method of the 3D model based on the estimated user emotions. For example, the design providing unit can estimate the user's emotions and adjust the display method of the 3D model based on the estimated user emotions. For example, if the user is relaxed, the design providing unit can display a 3D model with soft colors and a simple design. If the user is stressed, the design providing unit can also display a 3D model with visually soothing colors and a simple design. Furthermore, if the user is excited, the design providing unit can display a 3D model with bright colors and diverse design elements. This makes it possible to provide an optimal display method for the 3D model according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The design providing unit can select the optimal display method by referring to the user's past design browsing history when providing a design. For example, the design providing unit can select the optimal display method by referring to the user's past design browsing history when providing a design. For example, the design providing unit can adjust the display method of the current 3D model based on designs the user has previously viewed. The design providing unit can also display the current 3D model based on design elements that the user has previously preferred. Furthermore, the design providing unit can display the current 3D model by excluding design elements that the user has previously avoided. This makes it possible to provide the optimal display method by utilizing the user's past design browsing history.

[0073] The design providing unit can provide an appropriate 3D model display based on the user's current device information when providing a design. For example, the design providing unit can provide an appropriate 3D model display based on the user's current device information when providing a design. For example, if the user is using a smartphone, the design providing unit can display a 3D model that matches the screen size. Also, if the user is using a tablet, the design providing unit can display a 3D model optimized for a large screen. Furthermore, if the user is using a desktop, the design providing unit can display a high-resolution 3D model. This makes it possible to provide an optimal 3D model display based on the user's device information.

[0074] The design providing unit can estimate the user's emotions and adjust the display order of 3D models based on the estimated user emotions. The design providing unit, for example, estimates the user's emotions and adjusts the display order of 3D models based on the estimated user emotions. For example, when the user is relaxed, the design providing unit can prioritize displaying detailed 3D models. Furthermore, when the user is stressed, the design providing unit can prioritize displaying basic 3D models. Furthermore, when the user is excited, the design providing unit can prioritize displaying diverse 3D models. This makes it possible to provide an optimal display order of 3D models according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The design providing unit can provide an appropriate 3D model based on the user's geographical location information when providing a design. For example, the design providing unit can provide an appropriate 3D model based on the user's geographical location information when providing a design. For example, if the user lives in a cold region, the design providing unit can provide a 3D model using warm colors and materials. In addition, if the user lives in an urban area, the design providing unit can provide a 3D model with a modern design. Furthermore, if the user lives by the sea, the design providing unit can provide a 3D model with a sea theme. This makes it possible to provide an optimal 3D model based on the user's geographical location information.

[0076] The design providing unit can analyze the user's social media activity and suggest related 3D models when providing a design. For example, the design providing unit can analyze the user's social media activity and suggest related 3D models when providing a design. For example, the design providing unit can suggest related 3D models based on images and posts shared by the user on social media. The design providing unit can also suggest 3D models based on the style of influencers the user follows. Furthermore, the design providing unit can suggest 3D models based on trends in online communities in which the user participates. This makes it possible to provide the optimal 3D model based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-described sensibility input unit, design generation unit, and design provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sensibility input unit can input a user's preferences using the reception device 38 of the smart device 14. The design generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information to generate an optimal design. The design provision unit can display the generated design as a 3D model using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described sensibility input unit, design generation unit, and design provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensibility input unit can input the user's preferences by voice using the microphone 238 of the smart glasses 214. The design generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information to generate an optimal design. The design provision unit can provide the generated design by voice using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described sensibility input unit, design generation unit, and design provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the sensibility input unit can input the user's preferences by voice using the microphone 238 of the headset-type terminal 314. The design generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information to generate an optimal design. The design provision unit can display the generated design as a 3D model using, for example, the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-described sensibility input unit, design generation unit, and design provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensibility input unit can input the user's preferences by voice using the microphone 238 of the robot 414. The design generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input information to generate an optimal design. The design provision unit can provide the generated design by voice using the speaker 240 of the robot 414, for example.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The affective input unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, if the user is relaxed, the affective input unit can provide an interface with soft colors and a simple design. If the user is stressed, the affective input unit can also provide a visually soothing color scheme and a simple input method. If the user is excited, the affective input unit can also provide bright colors and a variety of options. This makes it possible to provide an optimal input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The affective input unit can analyze the user's past input history and suggest the optimal input method. For example, the affective input unit can automatically display colors and materials selected by the user in the past as candidates. The affective input unit can also prioritize suggestions for input methods (text, voice, etc.) that the user has used in the past. Furthermore, the affective input unit can predict and suggest design elements that the user will prefer at a specific time period based on the user's past input history. This makes it possible to suggest a more appropriate input method by utilizing the user's past input history.

[0080] The sentiment input unit can customize input items based on the user's lifestyle and areas of interest when inputting sentiment. For example, if the user has just moved into a new house, the sentiment input unit can provide design options related to moving. If the user is interested in a particular season, the sentiment input unit can also suggest design elements that suit that season. Furthermore, if the user has a particular hobby, the sentiment input unit can also provide design options related to that hobby. This makes it possible to provide input items according to the user's lifestyle and areas of interest.

[0081] The emotion input unit can estimate the user's emotion and determine the priority of input items based on the estimated user's emotion. For example, when the user is relaxed, the emotion input unit can prioritize displaying detailed input items. Furthermore, when the user is stressed, the emotion input unit can also prioritize displaying basic input items. Furthermore, when the user is excited, the emotion input unit can also prioritize displaying a variety of options. This makes it possible to provide an optimal priority of input items according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The sensibility input unit can prioritize displaying highly relevant input items based on the user's geographical location information when inputting sensibility. For example, if the user lives in a cold region, the sensibility input unit can prioritize displaying warm colors and materials. Also, if the user lives in an urban area, the sensibility input unit can prioritize displaying modern design elements. Furthermore, if the user lives by the sea, the sensibility input unit can prioritize displaying sea-themed design elements. This makes it possible to provide optimal input items based on the user's geographical location information.

[0083] The design generation unit can estimate the user's emotions and adjust the design expression method based on the estimated user emotions. For example, if the user is relaxed, the design generation unit can generate soft colors and a simple design. Also, if the user is stressed, the design generation unit can generate visually soothing colors and a simple design. Furthermore, if the user is excited, the design generation unit can generate bright colors and a variety of design elements. This makes it possible to provide an optimal design expression according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The design generation unit can refer to past design data and apply an appropriate design algorithm when generating a design. For example, the design generation unit can combine colors and materials that match the user's preferences based on past design data. The design generation unit can also suggest furniture layouts that match the user's style based on past design data. Furthermore, the design generation unit can generate lighting designs that match the user's sensibilities based on past design data. This makes it possible to generate designs with greater accuracy by utilizing past design data.

[0085] The design generation unit can customize design elements based on the user's living situation when generating a design. For example, if the user has just moved into a new house, the design generation unit can suggest design elements related to moving. Also, if the user is interested in a particular season, the design generation unit can suggest design elements that suit that season. Furthermore, if the user has a particular hobby, the design generation unit can suggest design elements related to that hobby. This makes it possible to provide optimal design elements according to the user's living situation.

[0086] The design generation unit can estimate the user's emotions and adjust the level of design detail based on the estimated user's emotions. For example, if the user is relaxed, the design generation unit can generate a design that includes detailed design elements. If the user is stressed, the design generation unit can also generate a simple, highly visible design. Furthermore, if the user is excited, the design generation unit can also generate a design that includes a variety of design elements. This makes it possible to provide an optimal level of design detail according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The design generation unit can determine appropriate design priorities based on the user's submission time when generating a design. For example, if the user is in a hurry, the design generation unit can generate designs that prioritize the most important design elements. Alternatively, if the user has time, the design generation unit can generate designs that include detailed design elements. Furthermore, if the user has set a specific deadline, the design generation unit can generate designs that meet that deadline. This makes it possible to provide optimal design priorities based on the user's submission time.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The sentiment input unit receives information about the user's preferences for color, material, style, etc. The user can input information such as warm colors, the use of wood, and a modern style, for example. The sentiment input unit accepts information in the form of text input, voice input, image input, and so on. For example, if the user inputs text such as "I like rooms with warm colors," the information is accepted. In the case of voice input, the voice input is converted into text using voice recognition technology. In the case of image input, the uploaded image is analyzed to extract color and material information. Step 2: The design generation unit generates a room design that matches the user's sensibilities based on the information input by the sensibility input unit. The design generation unit uses a machine learning algorithm to learn from past design data and generate a design that matches the user's preferences. It can also use a rule-based generation method to generate an optimal design based on the user's input information. For example, if the user prefers "warm colors" and "the use of wood," it will generate a design that uses warm colors and a lot of wood based on that information. Step 3: The design provider provides the design generated by the design generator as a 3D model. The design provider can display the 3D model on a web browser or using a dedicated application. For example, the generated design can be provided to the user as a 3D model, allowing the user to coordinate a room based on the 3D model.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0161] [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sensibility input unit for inputting information based on a user's sensibility; a design generation unit that generates a design based on the information input by the sensitivity input unit; a design providing unit that provides the design generated by the design generating unit as a 3D model; Equipped with A system characterized by:

2. The emotion input unit Users input their color, material, and style preferences in a selection format The system of claim 1 .

3. The design generation unit Learn from past design data and generate designs that match the user's sensibilities The system of claim 1 .

4. The design providing department Providing the generated design as a 3D model The system of claim 1 .

5. The emotion input unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions. The system of claim 1 .

6. The emotion input unit Analyzes the user's past input history and suggests appropriate input methods The system of claim 1 .

7. The emotion input unit When inputting emotions, customize the input items based on the user's living situation and areas of interest. The system of claim 1 .

8. The emotion input unit Estimate the user's emotions and prioritize input items based on the estimated user emotions. The system of claim 1 .

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