system

Abstract

The system according to the embodiment aims to quickly visualize a user's ideas as 3D designs and support their practical application. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a company example unit. The receiving unit receives text data input from a user. The generating unit analyzes the text data received by the receiving unit and generates a 3D design. The company example unit examples companies that can be put into practical use based on the 3D design generated by the generating unit.

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 the problem that there is no adequate process for quickly visualizing users' ideas as 3D designs and supporting their practical application.

[0005] The system according to the embodiment aims to quickly visualize a user's ideas as 3D designs and support their practical application. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a company example unit. The receiving unit receives text data input from a user. The generating unit analyzes the text data received by the receiving unit and generates a 3D design. The company example unit examples companies that can be put into practical use based on the 3D design generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly visualize a user's ideas as 3D designs and support their practical application. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 3D design generation system according to an embodiment of the present invention automatically generates 3D designs based on text data entered by a user and provides examples of companies that can commercialize the design. This system allows users to input their ideas in text, and a generation AI analyzes the text to generate a 3D design. Furthermore, if users wish to commercialize their ideas, the system also provides examples of companies that can create the designs. This function allows users to turn their ideas into concrete products. This service is easy to use for anyone, regardless of age. For example, a user inputs their idea in text. The user then describes the specific idea and design requirements in text. For example, the user might enter, "I'm thinking of a chair design. I want a high back and a wide seat." This information is then input into the generation AI. The generation AI then analyzes the input information and generates a 3D design. The generation AI calculates the optimal 3D design based on the text data and automatically generates the design. For example, a specific 3D model is generated based on the chair design entered by the user. Furthermore, if users wish to commercialize the design, the system also provides examples of companies that can create the design. Based on the generated 3D design, appropriate companies are selected and provided as examples. For example, it provides examples of companies that can realize the user's ideas, such as furniture manufacturers and design studios. This function allows users to turn their ideas into concrete products. This system allows users to easily visualize their ideas and put them into practical use. It is a service that can be easily used by anyone, regardless of age. This allows the 3D design generation system to convert the user's ideas from text data into 3D designs and provide examples of companies that can put them into practical use, thereby supporting the realization and practical application of ideas.

[0029] A 3D design generation system according to an embodiment includes a reception unit, a generation unit, and a company example unit. The reception unit receives text data input from a user. The text data input by the user includes, but is not limited to, sentences, keywords, and bullet points. The reception unit receives specific ideas and design requirements input by the user. For example, a user may input content such as, "I'm thinking about a chair design. I want the backrest to be high and the seat to be wide." The generation unit uses a generation AI to analyze the text data received by the reception unit and generate a 3D design. The generation AI analyzes the text data using, for example, a deep learning model or a generation algorithm and calculates an optimal 3D design. For example, the generation AI generates a specific 3D model based on the chair design input by the user. The generation unit can also automatically generate an optimal 3D design based on the text data using the generation AI. For example, the generation AI calculates an optimal 3D design based on the text data and automatically generates the design. The company example unit displays examples of companies that can be put into practical use based on the 3D design generated by the generation unit. The company example unit selects appropriate companies based on the generated 3D design and provides them to the user. For example, the company example unit provides examples of companies that can realize the user's idea, such as furniture manufacturers and design studios. As a result, the 3D design generation system according to the embodiment converts the user's idea from text data into a 3D design and provides examples of companies that can commercialize the idea, thereby supporting the realization and commercialization of the idea.

[0030] The generation unit can analyze text data using a generation AI and automatically generate a highly accurate 3D design. The generation unit, for example, analyzes text data using a generation AI and automatically generates a highly accurate 3D design. For example, the generation AI analyzes text data using a deep learning model or a generation algorithm and calculates an optimal 3D design. The generation unit can also automatically generate an optimal 3D design based on text data using a generation AI. For example, the generation AI calculates an optimal 3D design based on text data and automatically generates that design. In this way, by using a generation AI, a highly accurate 3D design can be automatically generated from text data.

[0031] The company example unit can select and exemplify companies based on the generated 3D design. For example, the company example unit selects appropriate companies based on the generated 3D design and exemplifies them to the user. For example, the company example unit exemplifies companies that can realize the user's idea, such as furniture manufacturers and design studios. This can support the practical application of the user's idea by exemplifying appropriate companies based on the generated 3D design.

[0032] The reception unit can receive specific ideas or design requirements input by the user. The reception unit receives, for example, specific ideas or design requirements input by the user. For example, the user can input content such as "I'm thinking about designing a chair. I want the backrest to be high and the seat to be wide." This allows the user to input specific ideas and design requirements, thereby improving the accuracy of the generated 3D design.

[0033] The generation unit can calculate and automatically generate a 3D design based on text data. The generation unit, for example, calculates and automatically generates a 3D design based on text data. For example, the generation AI calculates an optimal 3D design based on text data and automatically generates that design. This allows the user's ideas to be quickly visualized by automatically generating an optimal 3D design based on text data.

[0034] The company example section can provide examples of companies that can realize the user's idea, such as furniture manufacturers or design studios.The company example section provides examples of companies that can realize the user's idea, such as furniture manufacturers or design studios.By providing examples of companies that can realize the user's idea, it is possible to support the practical application of the idea.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, ideas and design requirements that the user has frequently input in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest ideas and design requirements to be used in a specific time period from the user's past input history. In this way, by analyzing the user's past input history, it is possible to suggest the optimal input method and improve input efficiency.

[0036] The reception unit can filter the input content based on the user's current project or area of ​​interest when inputting text data. For example, the reception unit filters the input content based on the user's current project or area of ​​interest when inputting text data. For example, keywords related to a project currently being undertaken by the user can be preferentially displayed. Related ideas and design requirements can also be suggested based on the user's area of ​​interest. Furthermore, the input content can be filtered based on topics in which the user has shown interest in the past. In this way, by filtering the input content based on the user's current project or area of ​​interest, it is possible to promote highly relevant input.

[0037] The reception unit can preferentially accept highly relevant input content in consideration of the user's geographical location information when inputting text data. For example, the reception unit preferentially accepts highly relevant input content in consideration of the user's geographical location information when inputting text data. For example, when the user is in a specific area, it can preferentially accept ideas and design requirements related to that area. Also, when the user is traveling, it can preferentially accept ideas and design requirements related to the travel destination. Furthermore, when the user is at home, it can preferentially accept ideas and design requirements related to the home. In this way, it is possible to preferentially accept highly relevant input content by considering the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when inputting text data and receive related input content. For example, the reception unit can analyze the user's social media activity when inputting text data and receive related input content. For example, the reception unit can automatically receive ideas and design requirements shared by the user on social media as input content. The reception unit can also receive related input content based on topics the user follows on social media. Furthermore, the reception unit can receive related input content based on groups and communities the user participates in on social media. In this way, the reception unit can receive related input content by analyzing the user's social media activity.

[0039] The generation unit can adjust the level of detail of the design based on the importance of the idea when generating the 3D design. For example, the generation unit can adjust the level of detail of the design based on the importance of the idea when generating the 3D design. For example, for an idea with high importance, a detailed 3D design can be generated. Also, for an idea with low importance, a simplified 3D design can be generated. Furthermore, the details of the design can be adjusted according to the importance of the idea. In this way, by adjusting the level of detail of the design based on the importance of the idea, a detailed design can be generated for an important idea.

[0040] The generation unit can apply different design algorithms depending on the category of the idea when generating a 3D design. For example, the generation unit applies different design algorithms depending on the category of the idea when generating a 3D design. For example, for furniture design, a specific furniture design algorithm can be applied. For architectural design, an architectural design algorithm can be applied. Furthermore, for industrial design, an industrial design algorithm can be applied. In this way, by applying different design algorithms depending on the category of the idea, a design appropriate for the category can be generated.

[0041] The generation unit can determine the priority of designs based on the time of submission of ideas when generating 3D designs. For example, the generation unit determines the priority of designs based on the time of submission of ideas when generating 3D designs. For example, the generation unit gives priority to recently submitted ideas when generating 3D designs. Also, ideas that were submitted earlier can be postponed. Furthermore, the order in which designs are generated can be adjusted based on the time of submission. In this way, by determining the priority of designs based on the time of submission of ideas, the most recent ideas can be given priority in designing.

[0042] The generation unit can adjust the order of designs based on the relevance of ideas when generating 3D designs. For example, the generation unit adjusts the order of designs based on the relevance of ideas when generating 3D designs. For example, highly relevant ideas are preferentially generated as 3D designs. Less relevant ideas can also be postponed. Furthermore, the order of design generation can be adjusted based on the relevance of ideas. In this way, by adjusting the order of designs based on the relevance of ideas, highly relevant ideas can be given priority in design.

[0043] The company example unit can select the most suitable company based on the characteristics of the generated 3D design when showcasing the companies. For example, the company example unit selects the most suitable company based on the characteristics of the generated 3D design when showcasing the companies. For example, in the case of furniture design, furniture manufacturers can be selected preferentially. In the case of architectural design, architectural companies can be selected preferentially. Furthermore, in the case of industrial design, industrial design studios can be selected preferentially. In this way, by selecting the most suitable company based on the characteristics of the generated 3D design, it is possible to show examples of the most suitable company for realizing the user's idea.

[0044] The company example unit can select an appropriate company by referring to the user's past project history when selecting company examples. For example, the company example unit selects an appropriate company by referring to the user's past project history when selecting company examples. For example, it preferentially selects companies related to projects in which the user has been involved in the past. It can also select the most suitable company from the user's past project history. It can also select companies related to projects in which the user has been successful in the past. In this way, it is possible to select the most suitable company for the user by referring to the user's past project history.

[0045] The company example unit can select the most suitable company by taking into consideration the user's geographical location information when selecting company examples. For example, the company example unit selects the most suitable company by taking into consideration the user's geographical location information when selecting company examples. For example, if the user is in a specific area, it can preferentially select companies related to that area. Also, if the user is traveling, it can preferentially select companies related to the user's travel destination. Furthermore, if the user is at home, it can preferentially select companies related to the user's home. In this way, it is possible to select the most suitable company for the user by taking into consideration the user's geographical location information.

[0046] The company example unit can analyze the user's social media activity and provide examples of related companies when providing company examples. For example, the company example unit can analyze the user's social media activity and provide examples of related companies when providing company examples. For example, the company example unit can provide examples of companies related to ideas and designs shared by the user on social media. It can also provide examples of companies that the user follows on social media with priority. It can also provide examples of companies related to groups and communities that the user participates in on social media. In this way, it is possible to provide examples of related companies by analyzing the user's social media activity.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can automatically search for related past design data based on the text data entered by the user and suggest it to the user. For example, if the user enters "I'm thinking about designing a chair," the reception unit can search for previously created chair design data and suggest similar designs. Also, if the user enters specific requirements such as "I want a high backrest and a wide seat," the reception unit can preferentially suggest past design data that matches those requirements. Furthermore, the reception unit can suggest new related design ideas based on the design data entered by the user in the past. This allows the user to think about new designs while referring to past design data.

[0049] The generator can visualize the 3D design generation process based on text data entered by the user. For example, if a user enters "I'm thinking of a chair design," the generator will display how the design is generated step by step. Also, if a user enters specific requirements, such as "I want a high back and a wide seat," the generator can display in real time how those requirements are reflected in the design. Furthermore, it can provide an interface that allows the user to fine-tune the design at each step of the generation process. This allows users to understand the generation process and give shape to their ideas.

[0050] The company example unit can provide company evaluation information based on the generated 3D design. For example, based on a chair design generated by the user, it can display evaluation information for related furniture manufacturers. When a user selects a design studio, it can also display the studio's past projects and customer evaluations. Furthermore, it can recommend the most suitable company to the user based on the company evaluation information. This allows the user to select the most suitable company while referring to the company evaluation information.

[0051] The reception unit can automatically search for related patent information based on the text data entered by the user and provide it to the user. For example, if a user enters "I'm thinking of designing a chair," it will search for patent information related to that design and display similar patents. Also, if a user enters specific requirements such as "I want a high backrest and a wide seat," it can prioritize and display patent information related to those requirements. Furthermore, it can also suggest related patent information based on design data entered by the user in the past. This allows users to consider new designs while referring to patent information.

[0052] The generation unit can provide material information necessary for generating a 3D design based on text data entered by the user. For example, if a user enters, "I'm thinking of designing a chair," material information suitable for that design will be displayed. Also, if a user enters specific requirements such as, "I want a high backrest and a wide seat," material information that matches those requirements can be displayed preferentially. Furthermore, based on the generated 3D design, the type and amount of materials required can be calculated and provided to the user. This allows the user to proceed with actual product creation while referring to material information suitable for the design.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives input of text data from a user. The text data input by the user may include, but is not limited to, sentences, keywords, and bullet points. For example, the user may input content such as, "I'm thinking about the design of a chair. I want the backrest to be high and the seat to be wide." Step 2: The generation unit uses a generation AI to analyze the text data received by the reception unit and generate a 3D design. The generation AI analyzes the text data using a deep learning model and a generation algorithm to calculate the optimal 3D design. For example, it generates a specific 3D model based on the design of a chair entered by the user. Step 3: The company example unit selects suitable companies based on the 3D design generated by the generation unit and provides them to the user. For example, the company example unit selects suitable companies that can realize the user's idea, such as furniture manufacturers and design studios.

[0055] (Example 2) A 3D design generation system according to an embodiment of the present invention automatically generates 3D designs based on text data entered by a user and provides examples of companies that can commercialize the design. This system allows users to input their ideas in text, and a generation AI analyzes the text to generate a 3D design. Furthermore, if users wish to commercialize their ideas, the system also provides examples of companies that can create the designs. This function allows users to turn their ideas into concrete products. This service is easy to use for anyone, regardless of age. For example, a user inputs their idea in text. The user then describes the specific idea and design requirements in text. For example, the user might enter, "I'm thinking of a chair design. I want a high back and a wide seat." This information is then input into the generation AI. The generation AI then analyzes the input information and generates a 3D design. The generation AI calculates the optimal 3D design based on the text data and automatically generates the design. For example, a specific 3D model is generated based on the chair design entered by the user. Furthermore, if users wish to commercialize the design, the system also provides examples of companies that can create the design. Based on the generated 3D design, appropriate companies are selected and provided as examples. For example, it provides examples of companies that can realize the user's ideas, such as furniture manufacturers and design studios. This function allows users to turn their ideas into concrete products. This system allows users to easily visualize their ideas and put them into practical use. It is a service that can be easily used by anyone, regardless of age. This allows the 3D design generation system to convert the user's ideas from text data into 3D designs and provide examples of companies that can put them into practical use, thereby supporting the realization and practical application of ideas.

[0056] A 3D design generation system according to an embodiment includes a reception unit, a generation unit, and a company example unit. The reception unit receives text data input from a user. The text data input by the user includes, but is not limited to, sentences, keywords, and bullet points. The reception unit receives specific ideas and design requirements input by the user. For example, a user may input content such as, "I'm thinking about a chair design. I want the backrest to be high and the seat to be wide." The generation unit uses a generation AI to analyze the text data received by the reception unit and generate a 3D design. The generation AI analyzes the text data using, for example, a deep learning model or a generation algorithm and calculates an optimal 3D design. For example, the generation AI generates a specific 3D model based on the chair design input by the user. The generation unit can also automatically generate an optimal 3D design based on the text data using the generation AI. For example, the generation AI calculates an optimal 3D design based on the text data and automatically generates the design. The company example unit displays examples of companies that can be put into practical use based on the 3D design generated by the generation unit. The company example unit selects appropriate companies based on the generated 3D design and provides them to the user. For example, the company example unit provides examples of companies that can realize the user's idea, such as furniture manufacturers and design studios. As a result, the 3D design generation system according to the embodiment converts the user's idea from text data into a 3D design and provides examples of companies that can commercialize the idea, thereby supporting the realization and commercialization of the idea.

[0057] The generation unit can analyze text data using a generation AI and automatically generate a highly accurate 3D design. The generation unit, for example, analyzes text data using a generation AI and automatically generates a highly accurate 3D design. For example, the generation AI analyzes text data using a deep learning model or a generation algorithm and calculates an optimal 3D design. The generation unit can also automatically generate an optimal 3D design based on text data using a generation AI. For example, the generation AI calculates an optimal 3D design based on text data and automatically generates that design. In this way, by using a generation AI, a highly accurate 3D design can be automatically generated from text data.

[0058] The company example unit can select and exemplify companies based on the generated 3D design. For example, the company example unit selects appropriate companies based on the generated 3D design and exemplifies them to the user. For example, the company example unit exemplifies companies that can realize the user's idea, such as furniture manufacturers and design studios. This can support the practical application of the user's idea by exemplifying appropriate companies based on the generated 3D design.

[0059] The reception unit can receive specific ideas or design requirements input by the user. The reception unit receives, for example, specific ideas or design requirements input by the user. For example, the user can input content such as "I'm thinking about designing a chair. I want the backrest to be high and the seat to be wide." This allows the user to input specific ideas and design requirements, thereby improving the accuracy of the generated 3D design.

[0060] The generation unit can calculate and automatically generate a 3D design based on text data. The generation unit, for example, calculates and automatically generates a 3D design based on text data. For example, the generation AI calculates an optimal 3D design based on text data and automatically generates that design. This allows the user's ideas to be quickly visualized by automatically generating an optimal 3D design based on text data.

[0061] The company example section can provide examples of companies that can realize the user's idea, such as furniture manufacturers or design studios.The company example section provides examples of companies that can realize the user's idea, such as furniture manufacturers or design studios.By providing examples of companies that can realize the user's idea, it is possible to support the practical application of the idea.

[0062] The reception unit can estimate the user's emotion and adjust the text data input interface based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the text data input interface based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Also, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick text data input. In this way, the user's input experience can be improved by adjusting the input interface according to the user's emotion.

[0063] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, ideas and design requirements that the user has frequently input in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest ideas and design requirements to be used in a specific time period from the user's past input history. In this way, by analyzing the user's past input history, it is possible to suggest the optimal input method and improve input efficiency.

[0064] The reception unit can filter the input content based on the user's current project or area of ​​interest when inputting text data. For example, the reception unit filters the input content based on the user's current project or area of ​​interest when inputting text data. For example, keywords related to a project currently being undertaken by the user can be preferentially displayed. Related ideas and design requirements can also be suggested based on the user's area of ​​interest. Furthermore, the input content can be filtered based on topics in which the user has shown interest in the past. In this way, by filtering the input content based on the user's current project or area of ​​interest, it is possible to promote highly relevant input.

[0065] The reception unit can estimate the user's emotion and determine the priority of input data based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of input data based on the estimated user's emotion. For example, if the user is nervous, the reception unit can cause important data to be input with priority. Also, if the user is relaxed, the reception unit can cause detailed data to be input with priority. Furthermore, if the user is in a hurry, the reception unit can cause the most important data to be input with priority. In this way, by determining the priority of input data according to the user's emotion, important data can be input with priority.

[0066] The reception unit can preferentially accept highly relevant input content in consideration of the user's geographical location information when inputting text data. For example, the reception unit preferentially accepts highly relevant input content in consideration of the user's geographical location information when inputting text data. For example, when the user is in a specific area, it can preferentially accept ideas and design requirements related to that area. Also, when the user is traveling, it can preferentially accept ideas and design requirements related to the travel destination. Furthermore, when the user is at home, it can preferentially accept ideas and design requirements related to the home. In this way, it is possible to preferentially accept highly relevant input content by considering the user's geographical location information.

[0067] The reception unit can analyze the user's social media activity when inputting text data and receive related input content. For example, the reception unit can analyze the user's social media activity when inputting text data and receive related input content. For example, the reception unit can automatically receive ideas and design requirements shared by the user on social media as input content. The reception unit can also receive related input content based on topics the user follows on social media. Furthermore, the reception unit can receive related input content based on groups and communities the user participates in on social media. In this way, the reception unit can receive related input content by analyzing the user's social media activity.

[0068] The generation unit can estimate the user's emotions and adjust the presentation method of the 3D design based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the presentation method of the 3D design based on the estimated user's emotions. For example, if the user is relaxed, a 3D design with soft colors and smooth shapes can be generated. Alternatively, if the user is excited, a 3D design with vivid colors and bold shapes can be generated. Furthermore, if the user is stressed, a simple and calm design can be generated. In this way, by adjusting the presentation method of the 3D design according to the user's emotions, a design that matches the user's intentions can be generated.

[0069] The generation unit can adjust the level of detail of the design based on the importance of the idea when generating the 3D design. For example, the generation unit can adjust the level of detail of the design based on the importance of the idea when generating the 3D design. For example, for an idea with high importance, a detailed 3D design can be generated. Also, for an idea with low importance, a simplified 3D design can be generated. Furthermore, the details of the design can be adjusted according to the importance of the idea. In this way, by adjusting the level of detail of the design based on the importance of the idea, a detailed design can be generated for an important idea.

[0070] The generation unit can apply different design algorithms depending on the category of the idea when generating a 3D design. For example, the generation unit applies different design algorithms depending on the category of the idea when generating a 3D design. For example, for furniture design, a specific furniture design algorithm can be applied. For architectural design, an architectural design algorithm can be applied. Furthermore, for industrial design, an industrial design algorithm can be applied. In this way, by applying different design algorithms depending on the category of the idea, a design appropriate for the category can be generated.

[0071] The generation unit can estimate the user's emotions and adjust the length of the 3D design based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the 3D design based on the estimated user emotions. For example, if the user is in a hurry, a short, to-the-point 3D design can be generated. If the user is relaxed, a longer 3D design including detailed explanations can be generated. Furthermore, if the user is excited, a 3D design with visually stimulating effects can be generated. In this way, by adjusting the length of the 3D design according to the user's emotions, a design that matches the user's intentions can be generated.

[0072] The generation unit can determine the priority of designs based on the time of submission of ideas when generating 3D designs. For example, the generation unit determines the priority of designs based on the time of submission of ideas when generating 3D designs. For example, the generation unit gives priority to recently submitted ideas when generating 3D designs. Also, ideas that were submitted earlier can be postponed. Furthermore, the order in which designs are generated can be adjusted based on the time of submission. In this way, by determining the priority of designs based on the time of submission of ideas, the most recent ideas can be given priority in designing.

[0073] The generation unit can adjust the order of designs based on the relevance of ideas when generating 3D designs. For example, the generation unit adjusts the order of designs based on the relevance of ideas when generating 3D designs. For example, highly relevant ideas are preferentially generated as 3D designs. Less relevant ideas can also be postponed. Furthermore, the order of design generation can be adjusted based on the relevance of ideas. In this way, by adjusting the order of designs based on the relevance of ideas, highly relevant ideas can be given priority in design.

[0074] The company example unit can estimate the user's emotions and adjust the method of company example presentation based on the estimated user's emotions. The company example unit, for example, estimates the user's emotions and adjusts the method of company example presentation based on the estimated user's emotions. For example, if the user is relaxed, detailed company information can be provided. Also, if the user is in a hurry, concise company information can be provided. Furthermore, if the user is excited, visually appealing company information can be provided. In this way, by adjusting the method of company example presentation based on the user's emotions, it is possible to provide the user with the most suitable company information.

[0075] The company example unit can select the most suitable company based on the characteristics of the generated 3D design when showcasing the companies. For example, the company example unit selects the most suitable company based on the characteristics of the generated 3D design when showcasing the companies. For example, in the case of furniture design, furniture manufacturers can be selected preferentially. In the case of architectural design, architectural companies can be selected preferentially. Furthermore, in the case of industrial design, industrial design studios can be selected preferentially. In this way, by selecting the most suitable company based on the characteristics of the generated 3D design, it is possible to show examples of the most suitable company for realizing the user's idea.

[0076] The company example unit can select an appropriate company by referring to the user's past project history when selecting company examples. For example, the company example unit selects an appropriate company by referring to the user's past project history when selecting company examples. For example, it preferentially selects companies related to projects in which the user has been involved in the past. It can also select the most suitable company from the user's past project history. It can also select companies related to projects in which the user has been successful in the past. In this way, it is possible to select the most suitable company for the user by referring to the user's past project history.

[0077] The company example unit can estimate the user's emotions and determine the priority of company examples based on the estimated user's emotions. The company example unit, for example, estimates the user's emotions and determines the priority of company examples based on the estimated user's emotions. For example, if the user is relaxed, detailed company information can be provided preferentially. Also, if the user is in a hurry, concise company information can be provided preferentially. Furthermore, if the user is excited, visually attractive company information can be provided preferentially. In this way, by determining the priority of company examples according to the user's emotions, it is possible to provide the user with the most suitable company information.

[0078] The company example unit can select the most suitable company by taking into consideration the user's geographical location information when selecting company examples. For example, the company example unit selects the most suitable company by taking into consideration the user's geographical location information when selecting company examples. For example, if the user is in a specific area, it can preferentially select companies related to that area. Also, if the user is traveling, it can preferentially select companies related to the user's travel destination. Furthermore, if the user is at home, it can preferentially select companies related to the user's home. In this way, it is possible to select the most suitable company for the user by taking into consideration the user's geographical location information.

[0079] The company example unit can analyze the user's social media activity and provide examples of related companies when providing company examples. For example, the company example unit can analyze the user's social media activity and provide examples of related companies when providing company examples. For example, the company example unit can provide examples of companies related to ideas and designs shared by the user on social media. It can also provide examples of companies that the user follows on social media with priority. It can also provide examples of companies related to groups and communities that the user participates in on social media. In this way, it is possible to provide examples of related companies by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and company example unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text data entered by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text data using a generation AI to generate a 3D design. The company example unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects appropriate companies based on the generated 3D design and presents them as examples to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and company example unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives text data entered by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text data using a generation AI to generate a 3D design. The company example unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects appropriate companies based on the generated 3D design and presents them to the user as examples. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and company example unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives text data input by the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text data using a generation AI to generate a 3D design. The company example unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects appropriate companies based on the generated 3D design and presents them as examples to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and company example unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives text data input by a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text data using a generation AI to generate a 3D design. The company example unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects appropriate companies based on the generated 3D design and presents them as examples to the user.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can automatically search for related past design data based on the text data entered by the user and suggest it to the user. For example, if the user enters "I'm thinking about designing a chair," the reception unit can search for previously created chair design data and suggest similar designs. Also, if the user enters specific requirements such as "I want a high backrest and a wide seat," the reception unit can preferentially suggest past design data that matches those requirements. Furthermore, the reception unit can suggest new related design ideas based on the design data entered by the user in the past. This allows the user to think about new designs while referring to past design data.

[0082] The generator can visualize the 3D design generation process based on text data entered by the user. For example, if a user enters "I'm thinking of a chair design," the generator will display how the design is generated step by step. Also, if a user enters specific requirements, such as "I want a high back and a wide seat," the generator can display in real time how those requirements are reflected in the design. Furthermore, it can provide an interface that allows the user to fine-tune the design at each step of the generation process. This allows users to understand the generation process and give shape to their ideas.

[0083] The company example unit can provide company evaluation information based on the generated 3D design. For example, based on a chair design generated by the user, it can display evaluation information for related furniture manufacturers. When a user selects a design studio, it can also display the studio's past projects and customer evaluations. Furthermore, it can recommend the most suitable company to the user based on the company evaluation information. This allows the user to select the most suitable company while referring to the company evaluation information.

[0084] The reception unit can automatically search for related patent information based on the text data entered by the user and provide it to the user. For example, if a user enters "I'm thinking of designing a chair," it will search for patent information related to that design and display similar patents. Also, if a user enters specific requirements such as "I want a high backrest and a wide seat," it can prioritize and display patent information related to those requirements. Furthermore, it can also suggest related patent information based on design data entered by the user in the past. This allows users to consider new designs while referring to patent information.

[0085] The generation unit can provide material information necessary for generating a 3D design based on text data entered by the user. For example, if a user enters, "I'm thinking of designing a chair," material information suitable for that design will be displayed. Also, if a user enters specific requirements such as, "I want a high backrest and a wide seat," material information that matches those requirements can be displayed preferentially. Furthermore, based on the generated 3D design, the type and amount of materials required can be calculated and provided to the user. This allows the user to proceed with actual product creation while referring to material information suitable for the design.

[0086] The reception unit can estimate the user's emotions and provide design inspiration based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest relaxing design ideas. If the user is excited, it can suggest challenging and innovative design ideas. Furthermore, if the user is relaxed, it can suggest calm and soothing design ideas. In this way, it is possible to bring out the user's creativity by providing design inspiration according to the user's emotions.

[0087] The generation unit can estimate the user's emotion and adjust the color of the design based on the estimated user's emotion. For example, if the user is relaxed, a design using soft colors can be generated. If the user is excited, a design using bright colors can be generated. Furthermore, if the user is stressed, a design using subdued colors can be generated. In this way, the appeal of the design can be enhanced by using colors that correspond to the user's emotion.

[0088] The company example unit can estimate the user's emotions and adjust the way in which companies are introduced based on the estimated user emotions. For example, if the user is relaxed, detailed company information can be provided. If the user is in a hurry, concise company information can be provided. Furthermore, if the user is excited, visually appealing company information can be provided. In this way, by providing a way to introduce companies according to the user's emotions, it is possible to provide the user with the most suitable company information.

[0089] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, detailed input options can be provided. Furthermore, if the user is in a hurry, voice input can be prioritized to allow quick input of text data. In this way, the user's input experience can be improved by providing an input interface that corresponds to the user's emotion.

[0090] The generation unit can estimate the user's emotion and adjust the shape of the design based on the estimated user's emotion. For example, if the user is relaxed, a design using smooth shapes can be generated. If the user is excited, a design using sharp shapes can be generated. Furthermore, if the user is stressed, a design using simple shapes can be generated. In this way, the appeal of the design can be increased by using shapes that correspond to the user's emotion.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit receives input of text data from a user. The text data input by the user may include, but is not limited to, sentences, keywords, and bullet points. For example, the user may input content such as, "I'm thinking about the design of a chair. I want the backrest to be high and the seat to be wide." Step 2: The generation unit uses a generation AI to analyze the text data received by the reception unit and generate a 3D design. The generation AI analyzes the text data using a deep learning model and a generation algorithm to calculate the optimal 3D design. For example, it generates a specific 3D model based on the design of a chair entered by the user. Step 3: The company example unit selects suitable companies based on the 3D design generated by the generation unit and provides them to the user. For example, the company example unit selects suitable companies that can realize the user's idea, such as furniture manufacturers and design studios.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0140] 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.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 reception unit that receives input of text data from a user; a generation unit that analyzes the text data received by the reception unit and generates a 3D design; and a company example unit that examples companies that can put the 3D design generated by the generation unit into practical use. A system characterized by:

2. The generation unit Generative AI analyzes text data and automatically generates highly accurate 3D designs 2. The system of claim 1.

3. The company example department: Select and demonstrate companies based on the generated 3D designs 2. The system of claim 1.

4. The reception unit Accept specific user-entered ideas or design requirements 2. The system of claim 1.

5. The generation unit Calculate and automatically generate 3D designs based on text data 2. The system of claim 1.

6. The company example department: Give examples of companies that can bring your ideas to life, such as furniture manufacturers or design studios.

2. The system of claim 1.

7. The reception unit Estimating user emotions and adjusting a text data input interface based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

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