system

Abstract

The system according to this embodiment aims to efficiently generate an image of an ideal partner based on user input information. [Solution] The system according to the embodiment comprises a data collection unit, a slot filling unit, a conversion unit, a prompt generation unit, and an image generation unit. The data collection unit collects user input information. The slot filling unit slots the information collected by the data collection unit. The conversion unit converts the information slot-filled by the slot filling unit into English. The prompt generation unit converts the information converted by the conversion unit into a prompt. The image generation unit generates an image based on the prompt generated by the prompt generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of generating an image of an ideal partner based on the input information of the user is complicated and difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently generate an image of an ideal partner based on the input information of the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a slot filling unit, a conversion unit, a prompt generation unit, and an image generation unit. The data collection unit collects user input information. The slot filling unit slots the information collected by the data collection unit. The conversion unit converts the information slot-filled by the slot filling unit into English. The prompt generation unit converts the information converted by the conversion unit into a prompt. The image generation unit generates an image based on the prompt generated by the prompt generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate an image of an ideal partner based on user input information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The partner image generation system according to an embodiment of the present invention is a system that makes it easier for users to concretely visualize the qualities they seek in a marriage partner. This partner image generation system takes the user's desired qualities in a marriage partner as input in the form of questions and uses a generation AI (e.g., Langchain) to perform slot-filling. Next, once the slot-filled information is complete, the generation AI converts it into English and then into prompts to be input into Stable Diffusion. For example, information such as "a 30-year-old engineer, whose hobby is hiking, and who has a gentle personality" is converted into prompts. Finally, Stable Diffusion generates a person image based on the prompts. This image is intended to make it easier for the user to visualize their ideal marriage partner. This system is expected to enable users to concretely visualize their ideal marriage partner and increase their awareness of marriage. Furthermore, user privacy protection and feedback functions for the generated image are also considered. For example, a detailed input section can be provided to improve the specificity and detail of the information input by the user. Additionally, an improvement section can be provided to ensure that the generated image meets the user's expectations. As a result, the partner image generation system can make it easier for users to concretely visualize the qualities they seek in a marriage partner and increase their awareness of marriage. This allows the partner image generation system to help users visualize the specific qualities they seek in a marriage partner, thereby increasing their awareness of marriage.

[0029] The partner image generation system according to the embodiment comprises a collection unit, a slot filling unit, a conversion unit, a prompt generation unit, and an image generation unit. The collection unit collects user input information. The collection unit provides, for example, an interface for the user to input the conditions they seek in a marriage partner in the form of questions. For example, the collection unit provides a form for inputting items such as age, occupation, hobbies, and personality. The collection unit can also store the information entered by the user in a database. The slot filling unit slot-fills the information collected by the collection unit. The slot filling unit uses, for example, a generation AI (e.g., Langchain) to fill the information entered by the user into slots. For example, the slot filling unit fills the information entered by the user, such as age, occupation, hobbies, and personality, into slots. The conversion unit converts the information slot-filled by the slot filling unit into English. The conversion unit uses, for example, a generation AI to convert the slot-filled information into English. For example, the conversion unit converts information such as "a 30-year-old engineer, whose hobby is hiking, and who has a gentle personality" into English. The prompt generation unit converts the information converted by the conversion unit into a prompt. The prompt generation unit uses, for example, a generation AI to convert the converted information into a prompt to be input to Stable Diffusion. For example, the prompt generation unit converts information such as "a 30-year-old engineer, whose hobby is hiking, and who has a gentle personality" into a prompt. The image generation unit generates an image based on the prompt generated by the prompt generation unit. The image generation unit uses, for example, Stable Diffusion to generate a person image based on the prompt. For example, the image generation unit generates a person image based on a prompt such as "a 30-year-old engineer, whose hobby is hiking, and who has a gentle personality". As a result, the partner image generation system according to the embodiment can efficiently collect, slot-fill, convert, generate prompts, and generate images from user input information.

[0030] Furthermore, the partner image generation system includes a security unit with functions to protect user privacy. The security unit has functions to protect user privacy. For example, it encrypts data. For instance, it encrypts information entered by the user to prevent third parties from accessing it. The security unit can also perform access control. For example, it restricts who has the authority to access user information. In this way, the security unit can protect user privacy.

[0031] Furthermore, the partner image generation system includes a feedback collection unit that collects user feedback on the generated images. The feedback collection unit collects user feedback on the generated images. For example, the feedback collection unit provides an interface for users to evaluate the generated images. For example, the feedback collection unit provides a form for users to evaluate the generated images. The feedback collection unit can also store the evaluations entered by users in a database. In this way, the feedback collection unit can collect user feedback on the generated images.

[0032] Furthermore, the partner image generation system includes an evaluation unit that evaluates the accuracy of the generated prompts. The evaluation unit evaluates the accuracy of the generated prompts. For example, the evaluation unit evaluates whether the generated prompts are accurately generated based on the user's input information. For example, the evaluation unit verifies whether the generated prompts match the user's input information. The evaluation unit can also set up metrics for evaluating the accuracy of the generated prompts. For example, the evaluation unit sets up a scoring system for evaluating the accuracy of the generated prompts. This allows the evaluation unit to evaluate the accuracy of the generated prompts.

[0033] Furthermore, the partner image generation system includes an improvement unit that has the function of ensuring that the generated images meet user expectations. The improvement unit improves the generated images so that they meet user expectations. For example, the improvement unit adjusts the image generation algorithm based on feedback given by the user to the generated images. For example, the improvement unit adjusts the image generation parameters based on evaluations given by the user to the generated images. The improvement unit can also set criteria for ensuring that the generated images meet user expectations. For example, the improvement unit sets up a scoring system for evaluating the quality of image generation based on user feedback. This allows the improvement unit to improve the generated images so that they meet user expectations.

[0034] Furthermore, the partner image generation system includes a detailed input section that has a function to improve the specificity and detail of the information entered by the user. The detailed input section improves the specificity and detail of the information entered by the user. For example, the detailed input section provides an interface for improving the level of detail of the information entered by the user. For example, the detailed input section provides a form for adding specific items of information entered by the user. The detailed input section can also provide guidelines for improving the specificity of the information entered by the user. For example, the detailed input section provides guidelines that show specific examples of information entered by the user. In this way, the detailed input section can improve the specificity and detail of the information entered by the user.

[0035] The data collection unit can analyze the user's past input history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information that the user has frequently entered in the past. For example, the data collection unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the data collection unit can predict and suggest information collection methods to be used during specific time periods based on the user's past input history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past input history.

[0036] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit can prioritize collecting highly relevant information based on the user's current living situation. For example, the data collection unit can filter out unnecessary information based on the user's areas of interest. For example, the data collection unit can adjust the scope of information collection based on the user's living situation and areas of interest. As a result, the data collection unit can collect highly relevant information by filtering information based on the user's current living situation and areas of interest.

[0037] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current location. For example, the data collection unit can filter out unnecessary information based on the user's geographical location. For example, the data collection unit can adjust the scope of data collection based on the user's geographical location. As a result, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location.

[0038] The data collection unit analyzes the user's social media activity during information gathering and collects relevant information. For example, the data collection unit analyzes the user's social media activity and prioritizes the collection of highly relevant information. For example, the data collection unit can filter out unnecessary information based on the user's social media activity. For example, the data collection unit can adjust the scope of information collection based on the user's social media activity. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity.

[0039] The slot filling unit can select the optimal filling method by referring to the user's past input data during slot filling. For example, the slot filling unit can suggest the optimal question based on data previously entered by the user. For example, the slot filling unit can extract specific patterns from the user's past input data and select the optimal filling method. For example, the slot filling unit can analyze the user's past input data and suggest an efficient filling method. In this way, the slot filling unit can select the optimal filling method by referring to the user's past input data.

[0040] The slot filling unit can customize the content of the slot filling based on the user's current interests. For example, the slot filling unit can prioritize relevant questions based on the user's current interests. For example, the slot filling unit can omit unnecessary questions based on the user's interests. For example, the slot filling unit can customize the content of questions based on the user's interests. As a result, the slot filling unit can perform more appropriate slot filling by customizing the content of the slot filling based on the user's current interests.

[0041] The slot filling unit can select the optimal filling method by considering the user's geographical location information during slot filling. For example, the slot filling unit can prioritize relevant questions based on the user's geographical location information. For example, the slot filling unit can omit unnecessary questions based on the user's geographical location information. For example, the slot filling unit can customize the content of questions based on the user's geographical location information. As a result, the slot filling unit can select the optimal filling method by considering the user's geographical location information.

[0042] The slot-filling unit can analyze the user's social media activity and adjust the content of the slot-filling process. For example, the slot-filling unit can analyze the user's social media activity and prioritize relevant questions. For example, the slot-filling unit can omit unnecessary questions based on the user's social media activity. For example, the slot-filling unit can customize the content of questions based on the user's social media activity. In this way, the slot-filling unit can adjust the content of the slot-filling process by analyzing the user's social media activity.

[0043] The conversion unit can select the optimal conversion method by referring to the user's past input data during conversion. For example, the conversion unit can propose the optimal conversion method based on data previously entered by the user. For example, the conversion unit can extract specific patterns from the user's past input data and select the optimal conversion method. For example, the conversion unit can analyze the user's past input data and propose an efficient conversion method. Thus, the conversion unit can select the optimal conversion method by referring to the user's past input data.

[0044] The conversion unit can customize the conversion content based on the user's current interests during the conversion process. For example, the conversion unit can prioritize the conversion of relevant information based on the user's current interests. For example, the conversion unit can omit unnecessary information based on the user's interests. For example, the conversion unit can customize the conversion content based on the user's interests. This allows the conversion unit to perform more appropriate conversions by customizing the conversion content based on the user's current interests.

[0045] The conversion unit can select the optimal conversion method by considering the user's geographical location information during conversion. For example, the conversion unit can prioritize the conversion of relevant information based on the user's geographical location information. For example, the conversion unit can omit unnecessary information based on the user's geographical location information. For example, the conversion unit can customize the content of the conversion based on the user's geographical location information. As a result, the conversion unit can select the optimal conversion method by considering the user's geographical location information.

[0046] The conversion unit can analyze the user's social media activity during the conversion process and adjust the content of the conversion accordingly. For example, the conversion unit can analyze the user's social media activity and prioritize the conversion of relevant information. For example, the conversion unit can omit unnecessary information based on the user's social media activity. For example, the conversion unit can customize the content of the conversion based on the user's social media activity. In this way, the conversion unit can adjust the content of the conversion by analyzing the user's social media activity.

[0047] The prompt generation unit can select the optimal generation method by referring to the user's past input data when generating prompts. For example, the prompt generation unit can propose the optimal prompt based on data previously entered by the user. For example, the prompt generation unit can extract specific patterns from the user's past input data and select the optimal generation method. For example, the prompt generation unit can analyze the user's past input data and propose an efficient generation method. In this way, the prompt generation unit can select the optimal generation method by referring to the user's past input data.

[0048] The prompt generation unit can customize the content of the generated prompts based on the user's current interests. For example, the prompt generation unit can prioritize generating relevant prompts based on the user's current interests. For example, the prompt generation unit can omit unnecessary prompts based on the user's interests. For example, the prompt generation unit can customize the content of the generated prompts based on the user's interests. As a result, the prompt generation unit can generate more appropriate prompts by customizing the content of the generated prompts based on the user's current interests.

[0049] The prompt generation unit can select the optimal generation method when generating prompts, taking into account the user's geographical location information. For example, the prompt generation unit can prioritize the generation of relevant prompts based on the user's geographical location information. For example, the prompt generation unit can omit unnecessary prompts based on the user's geographical location information. For example, the prompt generation unit can customize the content of the generated prompts based on the user's geographical location information. As a result, the prompt generation unit can select the optimal generation method by taking into account the user's geographical location information.

[0050] The prompt generation unit can analyze the user's social media activity and adjust the content of the generated prompts. For example, the prompt generation unit can analyze the user's social media activity and prioritize the generation of relevant prompts. For example, the prompt generation unit can omit unnecessary prompts based on the user's social media activity. For example, the prompt generation unit can customize the content of the generated prompts based on the user's social media activity. In this way, the prompt generation unit can adjust the content of the generated prompts by analyzing the user's social media activity.

[0051] The image generation unit can select the optimal generation method by referring to the user's past input data during image generation. For example, the image generation unit can propose the optimal image generation method based on data previously entered by the user. For example, the image generation unit can extract specific patterns from the user's past input data and select the optimal generation method. For example, the image generation unit can analyze the user's past input data and propose an efficient generation method. In this way, the image generation unit can select the optimal generation method by referring to the user's past input data.

[0052] The image generation unit can customize the content of the generated images based on the user's current interests. For example, the image generation unit can prioritize generating relevant images based on the user's current interests. For example, the image generation unit can omit the generation of unnecessary images based on the user's interests. For example, the image generation unit can customize the content of the generated images based on the user's interests. As a result, the image generation unit can generate more appropriate images by customizing the content of the generated images based on the user's current interests.

[0053] The image generation unit can select the optimal generation method by considering the user's geographical location information during image generation. For example, the image generation unit can prioritize the generation of relevant images based on the user's geographical location information. For example, the image generation unit can omit the generation of unnecessary images based on the user's geographical location information. For example, the image generation unit can customize the content of the generated images based on the user's geographical location information. As a result, the image generation unit can select the optimal generation method by considering the user's geographical location information.

[0054] The image generation unit can analyze the user's social media activity and adjust the content of the generated images. For example, the image generation unit can analyze the user's social media activity and prioritize the generation of relevant images. For example, the image generation unit can omit the generation of unnecessary images based on the user's social media activity. For example, the image generation unit can customize the content of the generated images based on the user's social media activity. In this way, the image generation unit can adjust the content of the generated images by analyzing the user's social media activity.

[0055] The security department can select the optimal security measures by referring to the user's past security history. For example, the security department can propose the optimal measures based on security issues the user has encountered in the past. For example, the security department can extract specific patterns from the user's past security history and select the optimal measures. For example, the security department can analyze the user's past security history and propose efficient measures. In this way, the security department can select the optimal security measures by referring to the user's past security history.

[0056] The security department can select the optimal security measures by considering the user's geographical location. For example, the security department can prioritize relevant security measures based on the user's geographical location. For example, the security department can omit unnecessary security measures based on the user's geographical location. For example, the security department can customize the content of security measures based on the user's geographical location. This allows the security department to select the optimal security measures by considering the user's geographical location.

[0057] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit can propose the optimal collection method based on the feedback the user has provided in the past. For example, the feedback collection unit can extract specific patterns from the user's past feedback history and select the optimal collection method. For example, the feedback collection unit can analyze the user's past feedback history and propose an efficient collection method. In this way, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history.

[0058] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can prioritize collecting relevant feedback based on the user's geographical location information. For example, the feedback collection unit can omit the collection of unnecessary feedback based on the user's geographical location information. For example, the feedback collection unit can customize the content of the collection based on the user's geographical location information. As a result, the feedback collection unit can select the optimal collection method by taking into account the user's geographical location information.

[0059] The evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history during the evaluation process. For example, the evaluation unit can propose the optimal evaluation method based on the user's past evaluations. For example, the evaluation unit can extract specific patterns from the user's past evaluation history and select the optimal evaluation method. For example, the evaluation unit can analyze the user's past evaluation history and propose an efficient evaluation method. In this way, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history.

[0060] The evaluation unit can select the optimal evaluation method by considering the user's geographical location information during the evaluation process. For example, the evaluation unit can prioritize relevant evaluations based on the user's geographical location information. For example, the evaluation unit can omit unnecessary evaluations based on the user's geographical location information. For example, the evaluation unit can customize the content of the evaluation based on the user's geographical location information. As a result, the evaluation unit can select the optimal evaluation method by considering the user's geographical location information.

[0061] The improvement unit can select the optimal improvement method by referring to the user's past improvement history during the improvement process. For example, the improvement unit can propose the optimal improvement method based on the improvements the user has made in the past. For example, the improvement unit can extract specific patterns from the user's past improvement history and select the optimal improvement method. For example, the improvement unit can analyze the user's past improvement history and propose an efficient improvement method. In this way, the improvement unit can select the optimal improvement method by referring to the user's past improvement history.

[0062] The improvement unit can select the optimal improvement method by considering the user's geographical location information during the improvement process. For example, the improvement unit can prioritize relevant improvements based on the user's geographical location information. For example, the improvement unit can omit unnecessary improvements based on the user's geographical location information. For example, the improvement unit can customize the content of improvements based on the user's geographical location information. As a result, the improvement unit can select the optimal improvement method by considering the user's geographical location information.

[0063] The detailed input section can select the optimal input method by referring to the user's past input history during detailed input. For example, the detailed input section can suggest the optimal input method based on data previously entered by the user. For example, the detailed input section can extract specific patterns from the user's past input history and select the optimal input method. For example, the detailed input section can analyze the user's past input history and suggest an efficient input method. In this way, the detailed input section can select the optimal input method by referring to the user's past input history.

[0064] The detailed input section can select the optimal input method by considering the user's geographical location information during detailed input. For example, the detailed input section can prioritize relevant inputs based on the user's geographical location information. For example, the detailed input section can omit unnecessary inputs based on the user's geographical location information. For example, the detailed input section can customize the content of inputs based on the user's geographical location information. As a result, the detailed input section can select the optimal input method by considering the user's geographical location information.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The data collection unit can analyze the user's past behavior history when collecting user input information and suggest the most appropriate question format. For example, it can reduce the user's burden by prioritizing the collection of information that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information collection methods to be used at specific times based on the user's past input history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past behavior history.

[0067] The feedback collection unit can filter user feedback on generated images based on the user's current lifestyle and areas of interest. For example, it can prioritize collecting highly relevant feedback based on the user's current lifestyle. It can also filter out unnecessary feedback based on the user's areas of interest. Furthermore, it can adjust the scope of feedback collection based on the user's lifestyle and areas of interest. As a result, the feedback collection unit can collect highly relevant feedback by filtering based on the user's current lifestyle and areas of interest.

[0068] The improvement unit can select the optimal improvement method by referring to the user's past feedback history when improving the generated image to meet user expectations. For example, it can propose the optimal improvement method based on the user's past feedback. It can extract specific patterns from the user's past feedback history and select the optimal improvement method. It can also analyze the user's past feedback history and propose efficient improvement methods. In this way, the improvement unit can select the optimal improvement method by referring to the user's past feedback history.

[0069] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting user input information. For example, it can prioritize the collection of highly relevant information based on the user's current location. It can also filter out unnecessary information based on the user's geographical location. Furthermore, it can adjust the scope of information collection based on the user's geographical location. As a result, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location.

[0070] The conversion unit can select the optimal conversion method when converting user input information to English by referring to the user's past input data. For example, it can suggest the optimal conversion method based on data the user has previously entered. It can extract specific patterns from the user's past input data and select the optimal conversion method. It can also analyze the user's past input data and suggest an efficient conversion method. In this way, the conversion unit can select the optimal conversion method by referring to the user's past input data.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The collection unit collects user input information. For example, the collection unit provides an interface for users to input the conditions they seek in a marriage partner in a question-and-answer format. Specifically, it provides a form for inputting items such as age, occupation, hobbies, and personality, and can also store the information entered by the user in a database. Step 2: The slot filling unit fills the slots with the information collected by the collection unit. The slot filling unit uses, for example, a generation AI (e.g., Langchain) to fill the slots with information such as age, occupation, hobbies, and personality entered by the user. Step 3: The conversion unit converts the information slot-filled by the slot-filling unit into English. The conversion unit converts the slot-filled information into English, for example, using a generation AI. Specifically, it converts information such as "He is a 30-year-old engineer, his hobby is hiking, and he has a kind personality" into English. Step 4: The prompt generation unit converts the information converted by the conversion unit into a prompt. The prompt generation unit uses, for example, a generation AI to convert the converted information into a prompt to be input into Stable Diffusion. Specifically, it converts information such as "a 30-year-old engineer, whose hobby is hiking and who has a kind personality" into a prompt. Step 5: The image generation unit generates an image based on the prompt generated by the prompt generation unit. The image generation unit generates a person image based on the prompt, for example, using Stable Diffusion. Specifically, it generates a person image based on a prompt such as "a 30-year-old engineer whose hobby is hiking and who has a kind personality."

[0073] (Example of form 2) The partner image generation system according to an embodiment of the present invention is a system that makes it easier for users to concretely visualize the qualities they seek in a marriage partner. This partner image generation system takes the user's desired qualities in a marriage partner as input in the form of questions and uses a generation AI (e.g., Langchain) to perform slot-filling. Next, once the slot-filled information is complete, the generation AI converts it into English and then into prompts to be input into Stable Diffusion. For example, information such as "a 30-year-old engineer, whose hobby is hiking, and who has a gentle personality" is converted into prompts. Finally, Stable Diffusion generates a person image based on the prompts. This image is intended to make it easier for the user to visualize their ideal marriage partner. This system is expected to enable users to concretely visualize their ideal marriage partner and increase their awareness of marriage. Furthermore, user privacy protection and feedback functions for the generated image are also considered. For example, a detailed input section can be provided to improve the specificity and detail of the information input by the user. Additionally, an improvement section can be provided to ensure that the generated image meets the user's expectations. As a result, the partner image generation system can make it easier for users to concretely visualize the qualities they seek in a marriage partner and increase their awareness of marriage. This allows the partner image generation system to help users visualize the specific qualities they seek in a marriage partner, thereby increasing their awareness of marriage.

[0074] The partner image generation system according to the embodiment comprises a collection unit, a slot filling unit, a conversion unit, a prompt generation unit, and an image generation unit. The collection unit collects user input information. The collection unit provides, for example, an interface for the user to input the conditions they seek in a marriage partner in the form of questions. For example, the collection unit provides a form for inputting items such as age, occupation, hobbies, and personality. The collection unit can also store the information entered by the user in a database. The slot filling unit slot-fills the information collected by the collection unit. The slot filling unit uses, for example, a generation AI (e.g., Langchain) to fill the information entered by the user into slots. For example, the slot filling unit fills the information entered by the user, such as age, occupation, hobbies, and personality, into slots. The conversion unit converts the information slot-filled by the slot filling unit into English. The conversion unit uses, for example, a generation AI to convert the slot-filled information into English. For example, the conversion unit converts information such as "a 30-year-old engineer, whose hobby is hiking, and who has a gentle personality" into English. The prompt generation unit converts the information converted by the conversion unit into a prompt. The prompt generation unit uses, for example, a generation AI to convert the converted information into a prompt to be input to Stable Diffusion. For example, the prompt generation unit converts information such as "a 30-year-old engineer, whose hobby is hiking, and who has a gentle personality" into a prompt. The image generation unit generates an image based on the prompt generated by the prompt generation unit. The image generation unit uses, for example, Stable Diffusion to generate a person image based on the prompt. For example, the image generation unit generates a person image based on a prompt such as "a 30-year-old engineer, whose hobby is hiking, and who has a gentle personality". As a result, the partner image generation system according to the embodiment can efficiently collect, slot-fill, convert, generate prompts, and generate images from user input information.

[0075] Furthermore, the partner image generation system includes a security unit with functions to protect user privacy. The security unit has functions to protect user privacy. For example, it encrypts data. For instance, it encrypts information entered by the user to prevent third parties from accessing it. The security unit can also perform access control. For example, it restricts who has the authority to access user information. In this way, the security unit can protect user privacy.

[0076] Furthermore, the partner image generation system includes a feedback collection unit that collects user feedback on the generated images. The feedback collection unit collects user feedback on the generated images. For example, the feedback collection unit provides an interface for users to evaluate the generated images. For example, the feedback collection unit provides a form for users to evaluate the generated images. The feedback collection unit can also store the evaluations entered by users in a database. In this way, the feedback collection unit can collect user feedback on the generated images.

[0077] Furthermore, the partner image generation system includes an evaluation unit that evaluates the accuracy of the generated prompts. The evaluation unit evaluates the accuracy of the generated prompts. For example, the evaluation unit evaluates whether the generated prompts are accurately generated based on the user's input information. For example, the evaluation unit verifies whether the generated prompts match the user's input information. The evaluation unit can also set up metrics for evaluating the accuracy of the generated prompts. For example, the evaluation unit sets up a scoring system for evaluating the accuracy of the generated prompts. This allows the evaluation unit to evaluate the accuracy of the generated prompts.

[0078] Furthermore, the partner image generation system includes an improvement unit that has the function of ensuring that the generated images meet user expectations. The improvement unit improves the generated images so that they meet user expectations. For example, the improvement unit adjusts the image generation algorithm based on feedback given by the user to the generated images. For example, the improvement unit adjusts the image generation parameters based on evaluations given by the user to the generated images. The improvement unit can also set criteria for ensuring that the generated images meet user expectations. For example, the improvement unit sets up a scoring system for evaluating the quality of image generation based on user feedback. This allows the improvement unit to improve the generated images so that they meet user expectations.

[0079] Furthermore, the partner image generation system includes a detailed input section that has a function to improve the specificity and detail of the information entered by the user. The detailed input section improves the specificity and detail of the information entered by the user. For example, the detailed input section provides an interface for improving the level of detail of the information entered by the user. For example, the detailed input section provides a form for adding specific items of information entered by the user. The detailed input section can also provide guidelines for improving the specificity of the information entered by the user. For example, the detailed input section provides guidelines that show specific examples of information entered by the user. In this way, the detailed input section can improve the specificity and detail of the information entered by the user.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can delay the start of information collection to create an environment where the user can concentrate. For example, if the user is stressed, the data collection unit can temporarily suspend information collection and wait until the user relaxes. For example, if the user is excited, the data collection unit can collect information quickly to gather the necessary information before the user's excitement subsides. In this way, the data collection unit can collect more appropriate information by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The data collection unit can analyze the user's past input history and select the optimal data collection method. For example, the data collection unit can prioritize collecting information that the user has frequently entered in the past. For example, the data collection unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the data collection unit can predict and suggest information collection methods to be used during specific time periods based on the user's past input history. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past input history.

[0082] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit can prioritize collecting highly relevant information based on the user's current living situation. For example, the data collection unit can filter out unnecessary information based on the user's areas of interest. For example, the data collection unit can adjust the scope of information collection based on the user's living situation and areas of interest. As a result, the data collection unit can collect highly relevant information by filtering information based on the user's current living situation and areas of interest.

[0083] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. If the user is stressed, for example, the data collection unit may prioritize collecting concise information. If the user is excited, for example, the data collection unit may prioritize collecting information that is of interest. This allows the data collection unit to collect more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current location. For example, the data collection unit can filter out unnecessary information based on the user's geographical location. For example, the data collection unit can adjust the scope of data collection based on the user's geographical location. As a result, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location.

[0085] The data collection unit analyzes the user's social media activity during information gathering and collects relevant information. For example, the data collection unit analyzes the user's social media activity and prioritizes the collection of highly relevant information. For example, the data collection unit can filter out unnecessary information based on the user's social media activity. For example, the data collection unit can adjust the scope of information collection based on the user's social media activity. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity.

[0086] The slot-filling unit can estimate the user's emotions and adjust the order of slot-filling based on the estimated emotions. For example, if the user is relaxed, the slot-filling unit will ask detailed questions first. If the user is stressed, the slot-filling unit will ask simple questions first. If the user is excited, the slot-filling unit will ask interesting questions first. In this way, the slot-filling unit can provide more appropriate slot-filling by adjusting the order of slot-filling according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The slot filling unit can select the optimal filling method by referring to the user's past input data during slot filling. For example, the slot filling unit can suggest the optimal question based on data previously entered by the user. For example, the slot filling unit can extract specific patterns from the user's past input data and select the optimal filling method. For example, the slot filling unit can analyze the user's past input data and suggest an efficient filling method. In this way, the slot filling unit can select the optimal filling method by referring to the user's past input data.

[0088] The slot filling unit can customize the content of the slot filling based on the user's current interests. For example, the slot filling unit can prioritize relevant questions based on the user's current interests. For example, the slot filling unit can omit unnecessary questions based on the user's interests. For example, the slot filling unit can customize the content of questions based on the user's interests. As a result, the slot filling unit can perform more appropriate slot filling by customizing the content of the slot filling based on the user's current interests.

[0089] The slot-filling unit can estimate the user's emotions and determine the priority of slot-filling based on the estimated emotions. For example, if the user is relaxed, the slot-filling unit will prioritize detailed questions. For example, if the user is stressed, the slot-filling unit will prioritize simple questions. For example, if the user is excited, the slot-filling unit will prioritize questions that pique their interest. In this way, the slot-filling unit can provide more appropriate slot-filling by determining the priority of slot-filling according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The slot filling unit can select the optimal filling method by considering the user's geographical location information during slot filling. For example, the slot filling unit can prioritize relevant questions based on the user's geographical location information. For example, the slot filling unit can omit unnecessary questions based on the user's geographical location information. For example, the slot filling unit can customize the content of questions based on the user's geographical location information. As a result, the slot filling unit can select the optimal filling method by considering the user's geographical location information.

[0091] The slot-filling unit can analyze the user's social media activity and adjust the content of the slot-filling process. For example, the slot-filling unit can analyze the user's social media activity and prioritize relevant questions. For example, the slot-filling unit can omit unnecessary questions based on the user's social media activity. For example, the slot-filling unit can customize the content of questions based on the user's social media activity. In this way, the slot-filling unit can adjust the content of the slot-filling process by analyzing the user's social media activity.

[0092] The conversion unit can estimate the user's emotions and adjust the timing of the conversion based on the estimated emotions. For example, if the user is relaxed, the conversion unit can delay the conversion to create an environment where the user can concentrate. For example, if the user is stressed, the conversion unit can temporarily suspend the conversion and wait until the user relaxes. For example, if the user is excited, the conversion unit can perform the conversion quickly to convert the necessary information before the user's excitement subsides. In this way, the conversion unit can perform a more appropriate conversion by adjusting the timing of the conversion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The conversion unit can select the optimal conversion method by referring to the user's past input data during conversion. For example, the conversion unit can propose the optimal conversion method based on data previously entered by the user. For example, the conversion unit can extract specific patterns from the user's past input data and select the optimal conversion method. For example, the conversion unit can analyze the user's past input data and propose an efficient conversion method. Thus, the conversion unit can select the optimal conversion method by referring to the user's past input data.

[0094] The conversion unit can customize the conversion content based on the user's current interests during the conversion process. For example, the conversion unit can prioritize the conversion of relevant information based on the user's current interests. For example, the conversion unit can omit unnecessary information based on the user's interests. For example, the conversion unit can customize the conversion content based on the user's interests. This allows the conversion unit to perform more appropriate conversions by customizing the conversion content based on the user's current interests.

[0095] The transformation unit can estimate the user's emotions and determine the priority of transformations based on the estimated emotions. For example, if the user is relaxed, the transformation unit can prioritize transforming detailed information. If the user is stressed, the transformation unit can prioritize transforming concise information. If the user is excited, the transformation unit can prioritize transforming information that is of interest. In this way, the transformation unit can perform more appropriate transformations by determining the priority of transformations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The conversion unit can select the optimal conversion method by considering the user's geographical location information during conversion. For example, the conversion unit can prioritize the conversion of relevant information based on the user's geographical location information. For example, the conversion unit can omit unnecessary information based on the user's geographical location information. For example, the conversion unit can customize the content of the conversion based on the user's geographical location information. As a result, the conversion unit can select the optimal conversion method by considering the user's geographical location information.

[0097] The conversion unit can analyze the user's social media activity during the conversion process and adjust the content of the conversion accordingly. For example, the conversion unit can analyze the user's social media activity and prioritize the conversion of relevant information. For example, the conversion unit can omit unnecessary information based on the user's social media activity. For example, the conversion unit can customize the content of the conversion based on the user's social media activity. In this way, the conversion unit can adjust the content of the conversion by analyzing the user's social media activity.

[0098] The prompt generation unit can estimate the user's emotions and adjust the order of prompt generation based on the estimated emotions. For example, if the user is relaxed, the prompt generation unit may generate detailed prompts first. If the user is stressed, for example, the prompt generation unit may generate concise prompts first. If the user is excited, for example, the prompt generation unit may generate engaging prompts first. In this way, the prompt generation unit can generate more appropriate prompts by adjusting the order of prompt generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The prompt generation unit can select the optimal generation method by referring to the user's past input data when generating prompts. For example, the prompt generation unit can propose the optimal prompt based on data previously entered by the user. For example, the prompt generation unit can extract specific patterns from the user's past input data and select the optimal generation method. For example, the prompt generation unit can analyze the user's past input data and propose an efficient generation method. In this way, the prompt generation unit can select the optimal generation method by referring to the user's past input data.

[0100] The prompt generation unit can customize the content of the generated prompts based on the user's current interests. For example, the prompt generation unit can prioritize generating relevant prompts based on the user's current interests. For example, the prompt generation unit can omit unnecessary prompts based on the user's interests. For example, the prompt generation unit can customize the content of the generated prompts based on the user's interests. As a result, the prompt generation unit can generate more appropriate prompts by customizing the content of the generated prompts based on the user's current interests.

[0101] The prompt generation unit can estimate the user's emotions and determine the priority of prompt generation based on the estimated emotions. For example, if the user is relaxed, the prompt generation unit may prioritize generating detailed prompts. For example, if the user is stressed, the prompt generation unit may prioritize generating concise prompts. For example, if the user is excited, the prompt generation unit may prioritize generating engaging prompts. In this way, the prompt generation unit can generate more appropriate prompts by determining the priority of prompt generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The prompt generation unit can select the optimal generation method when generating prompts, taking into account the user's geographical location information. For example, the prompt generation unit can prioritize the generation of relevant prompts based on the user's geographical location information. For example, the prompt generation unit can omit unnecessary prompts based on the user's geographical location information. For example, the prompt generation unit can customize the content of the generated prompts based on the user's geographical location information. As a result, the prompt generation unit can select the optimal generation method by taking into account the user's geographical location information.

[0103] The prompt generation unit can analyze the user's social media activity and adjust the content of the generated prompts. For example, the prompt generation unit can analyze the user's social media activity and prioritize the generation of relevant prompts. For example, the prompt generation unit can omit unnecessary prompts based on the user's social media activity. For example, the prompt generation unit can customize the content of the generated prompts based on the user's social media activity. In this way, the prompt generation unit can adjust the content of the generated prompts by analyzing the user's social media activity.

[0104] The image generation unit can estimate the user's emotions and adjust the timing of image generation based on the estimated emotions. For example, if the user is relaxed, the image generation unit can delay the timing of image generation to create an environment where the user can concentrate. For example, if the user is stressed, the image generation unit can temporarily suspend image generation and wait until the user relaxes. For example, if the user is excited, the image generation unit can generate images quickly to produce the necessary images before the user's excitement subsides. In this way, the image generation unit can produce more appropriate images by adjusting the timing of image generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The image generation unit can select the optimal generation method by referring to the user's past input data during image generation. For example, the image generation unit can propose the optimal image generation method based on data previously entered by the user. For example, the image generation unit can extract specific patterns from the user's past input data and select the optimal generation method. For example, the image generation unit can analyze the user's past input data and propose an efficient generation method. In this way, the image generation unit can select the optimal generation method by referring to the user's past input data.

[0106] The image generation unit can customize the content of the generated images based on the user's current interests. For example, the image generation unit can prioritize generating relevant images based on the user's current interests. For example, the image generation unit can omit the generation of unnecessary images based on the user's interests. For example, the image generation unit can customize the content of the generated images based on the user's interests. As a result, the image generation unit can generate more appropriate images by customizing the content of the generated images based on the user's current interests.

[0107] The image generation unit can estimate the user's emotions and determine the priority of image generation based on the estimated emotions. For example, if the user is relaxed, the image generation unit may prioritize generating detailed images. For example, if the user is stressed, the image generation unit may prioritize generating concise images. For example, if the user is excited, the image generation unit may prioritize generating interesting images. In this way, the image generation unit can generate more appropriate images by determining the priority of image generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The image generation unit can select the optimal generation method by considering the user's geographical location information during image generation. For example, the image generation unit can prioritize the generation of relevant images based on the user's geographical location information. For example, the image generation unit can omit the generation of unnecessary images based on the user's geographical location information. For example, the image generation unit can customize the content of the generated images based on the user's geographical location information. As a result, the image generation unit can select the optimal generation method by considering the user's geographical location information.

[0109] The image generation unit can analyze the user's social media activity and adjust the content of the generated images. For example, the image generation unit can analyze the user's social media activity and prioritize the generation of relevant images. For example, the image generation unit can omit the generation of unnecessary images based on the user's social media activity. For example, the image generation unit can customize the content of the generated images based on the user's social media activity. In this way, the image generation unit can adjust the content of the generated images by analyzing the user's social media activity.

[0110] The security unit can estimate the user's emotions and adjust the security level based on the estimated emotions. For example, if the user is relaxed, the security unit will set the security level to normal. If the user is stressed, the security unit can increase the security level to ensure the user feels safe. If the user is excited, the security unit can adjust the security level to suppress the user's excitement. In this way, the security unit can implement more appropriate security measures by adjusting the security level according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The security department can select the optimal security measures by referring to the user's past security history. For example, the security department can propose the optimal measures based on security issues the user has encountered in the past. For example, the security department can extract specific patterns from the user's past security history and select the optimal measures. For example, the security department can analyze the user's past security history and propose efficient measures. In this way, the security department can select the optimal security measures by referring to the user's past security history.

[0112] The security unit can estimate the user's emotions and prioritize security measures based on those emotions. For example, if the user is relaxed, the security unit will prioritize standard security measures. If the user is stressed, the security unit will prioritize enhanced security measures. If the user is agitated, the security unit can adjust security measures to reduce the user's agitation. This allows the security unit to implement more appropriate security measures by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The security department can select the optimal security measures by considering the user's geographical location. For example, the security department can prioritize relevant security measures based on the user's geographical location. For example, the security department can omit unnecessary security measures based on the user's geographical location. For example, the security department can customize the content of security measures based on the user's geographical location. This allows the security department to select the optimal security measures by considering the user's geographical location.

[0114] The feedback collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit can delay the timing of feedback collection to create an environment where the user can concentrate. For example, if the user is stressed, the feedback collection unit can temporarily suspend feedback collection and wait until the user relaxes. For example, if the user is excited, the feedback collection unit can collect feedback quickly to gather necessary feedback before the user's excitement subsides. In this way, the feedback collection unit can collect more appropriate feedback by adjusting the timing of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit can propose the optimal collection method based on the feedback the user has provided in the past. For example, the feedback collection unit can extract specific patterns from the user's past feedback history and select the optimal collection method. For example, the feedback collection unit can analyze the user's past feedback history and propose an efficient collection method. In this way, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history.

[0116] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit may prioritize collecting detailed feedback. For example, if the user is stressed, the feedback collection unit may prioritize collecting concise feedback. For example, if the user is excited, the feedback collection unit may prioritize collecting interesting feedback. In this way, the feedback collection unit can collect more appropriate feedback by determining the priority of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback collection unit can prioritize collecting relevant feedback based on the user's geographical location information. For example, the feedback collection unit can omit the collection of unnecessary feedback based on the user's geographical location information. For example, the feedback collection unit can customize the content of the collection based on the user's geographical location information. As a result, the feedback collection unit can select the optimal collection method by taking into account the user's geographical location information.

[0118] The evaluation unit can estimate the user's emotions and adjust the timing of the evaluation based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can delay the evaluation to create an environment where the user can concentrate. For example, if the user is stressed, the evaluation unit can temporarily suspend the evaluation and wait until the user relaxes. For example, if the user is excited, the evaluation unit can perform the evaluation quickly to make the necessary assessments before the user's excitement subsides. In this way, the evaluation unit can make more appropriate evaluations by adjusting the timing of the evaluation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history during the evaluation process. For example, the evaluation unit can propose the optimal evaluation method based on the user's past evaluations. For example, the evaluation unit can extract specific patterns from the user's past evaluation history and select the optimal evaluation method. For example, the evaluation unit can analyze the user's past evaluation history and propose an efficient evaluation method. In this way, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history.

[0120] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is relaxed, the evaluation unit will prioritize detailed evaluations. If the user is stressed, the evaluation unit will prioritize concise evaluations. If the user is excited, the evaluation unit will prioritize evaluations that pique interest. In this way, the evaluation unit can provide more appropriate evaluations by determining the priority of evaluations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The evaluation unit can select the optimal evaluation method by considering the user's geographical location information during the evaluation process. For example, the evaluation unit can prioritize relevant evaluations based on the user's geographical location information. For example, the evaluation unit can omit unnecessary evaluations based on the user's geographical location information. For example, the evaluation unit can customize the content of the evaluation based on the user's geographical location information. As a result, the evaluation unit can select the optimal evaluation method by considering the user's geographical location information.

[0122] The improvement unit can estimate the user's emotions and adjust the timing of improvements based on the estimated emotions. For example, if the user is relaxed, the improvement unit can delay the timing of improvements to create an environment where the user can concentrate. For example, if the user is stressed, the improvement unit can temporarily suspend improvements and wait until the user relaxes. For example, if the user is excited, the improvement unit can perform improvements quickly to make the necessary improvements before the user's excitement subsides. In this way, the improvement unit can make more appropriate improvements by adjusting the timing of improvements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The improvement unit can select the optimal improvement method by referring to the user's past improvement history during the improvement process. For example, the improvement unit can propose the optimal improvement method based on the improvements the user has made in the past. For example, the improvement unit can extract specific patterns from the user's past improvement history and select the optimal improvement method. For example, the improvement unit can analyze the user's past improvement history and propose an efficient improvement method. In this way, the improvement unit can select the optimal improvement method by referring to the user's past improvement history.

[0124] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated emotions. For example, if the user is relaxed, the improvement unit will prioritize detailed improvements. If the user is stressed, the improvement unit will prioritize concise improvements. If the user is excited, the improvement unit will prioritize interesting improvements. This allows the improvement unit to make more appropriate improvements by determining the priority of improvements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The improvement unit can select the optimal improvement method by considering the user's geographical location information during the improvement process. For example, the improvement unit can prioritize relevant improvements based on the user's geographical location information. For example, the improvement unit can omit unnecessary improvements based on the user's geographical location information. For example, the improvement unit can customize the content of improvements based on the user's geographical location information. As a result, the improvement unit can select the optimal improvement method by considering the user's geographical location information.

[0126] The detailed input unit can estimate the user's emotions and adjust the timing of detailed input based on the estimated emotions. For example, if the user is relaxed, the detailed input unit can delay the timing of detailed input to create an environment where the user can concentrate. For example, if the user is stressed, the detailed input unit can temporarily suspend detailed input and wait until the user relaxes. For example, if the user is excited, the detailed input unit can perform detailed input quickly to provide the necessary input before the user's excitement subsides. In this way, the detailed input unit can provide more appropriate detailed input by adjusting the timing of detailed input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The detailed input section can select the optimal input method by referring to the user's past input history during detailed input. For example, the detailed input section can suggest the optimal input method based on data previously entered by the user. For example, the detailed input section can extract specific patterns from the user's past input history and select the optimal input method. For example, the detailed input section can analyze the user's past input history and suggest an efficient input method. In this way, the detailed input section can select the optimal input method by referring to the user's past input history.

[0128] The detailed input section can estimate the user's emotions and determine the priority of detailed input based on the estimated emotions. For example, if the user is relaxed, the detailed input section will prioritize detailed input. For example, if the user is stressed, the detailed input section will prioritize concise input. For example, if the user is excited, the detailed input section will prioritize interesting input. In this way, the detailed input section can provide more appropriate detailed input by determining the priority of detailed input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The detailed input section can select the optimal input method by considering the user's geographical location information during detailed input. For example, the detailed input section can prioritize relevant inputs based on the user's geographical location information. For example, the detailed input section can omit unnecessary inputs based on the user's geographical location information. For example, the detailed input section can customize the content of inputs based on the user's geographical location information. As a result, the detailed input section can select the optimal input method by considering the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, slot filling unit, conversion unit, prompt generation unit, image generation unit, security unit, feedback collection unit, evaluation unit, improvement unit, and detailed input unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user input information using the receiving device 38 of the smart device 14 and stores it in the database 24 using the specific processing unit 290 of the data processing unit 12. The slot filling unit is implemented by the specific processing unit 290 of the data processing unit 12 and fills the user input information into slots using generation AI. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the slot-filled information into English. The prompt generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the converted information into prompts to be input into Stable Diffusion. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a person image based on the prompt using Stable Diffusion. The security unit is implemented by the specific processing unit 290 of the data processing device 12, which encrypts user input information and performs access control. The feedback collection unit collects user feedback using the receiving device 38 of the smart device 14 and stores it in the database 24 using the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12, which evaluates the accuracy of the generated prompts. The improvement unit is implemented by the specific processing unit 290 of the data processing device 12, which adjusts the image generation algorithm based on user feedback. The detailed input unit improves the specificity and detail of the information entered by the user using the receiving device 38 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, slot filling unit, conversion unit, prompt generation unit, image generation unit, security unit, feedback collection unit, evaluation unit, improvement unit, and detailed input unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user input information using the microphone 238 of the smart glasses 214 and stores it in the database 24 by the specific processing unit 290 of the data processing unit 12. The slot filling unit is implemented by the specific processing unit 290 of the data processing unit 12 and fills the user input information into slots using generation AI. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the slot-filled information into English. The prompt generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the converted information into prompts to be input into Stable Diffusion. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a person image based on the prompt using Stable Diffusion. The security unit is implemented by the specific processing unit 290 of the data processing device 12, which encrypts user input information and performs access control. The feedback collection unit collects user feedback using the microphone 238 of the smart glasses 214 and stores it in the database 24 using the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12, which evaluates the accuracy of the generated prompts. The improvement unit is implemented by the specific processing unit 290 of the data processing device 12, which adjusts the image generation algorithm based on user feedback. The detailed input unit improves the specificity and detail of the information entered by the user using the microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, slot filling unit, conversion unit, prompt generation unit, image generation unit, security unit, feedback collection unit, evaluation unit, improvement unit, and detailed input unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user input information using the microphone 238 of the headset terminal 314 and stores it in the database 24 by the specific processing unit 290 of the data processing unit 12. The slot filling unit is implemented by the specific processing unit 290 of the data processing unit 12 and fills the user input information into slots using generation AI. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the slot-filled information into English. The prompt generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the converted information into prompts to be input into Stable Diffusion. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a person image based on the prompt using Stable Diffusion. The security unit is implemented by the specific processing unit 290 of the data processing device 12, which encrypts user input information and performs access control. The feedback collection unit collects user feedback using the microphone 238 of the headset terminal 314 and stores it in the database 24 using the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12, which evaluates the accuracy of the generated prompts. The improvement unit is implemented by the specific processing unit 290 of the data processing device 12, which adjusts the image generation algorithm based on user feedback. The detailed input unit improves the specificity and detail of the information entered by the user using the microphone 238 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, slot filling unit, conversion unit, prompt generation unit, image generation unit, security unit, feedback collection unit, evaluation unit, improvement unit, and detailed input unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user input information using the microphone 238 of the robot 414 and stores it in the database 24 by the specific processing unit 290 of the data processing unit 12. The slot filling unit is implemented by the specific processing unit 290 of the data processing unit 12 and fills the user input information into slots using a generation AI. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the slot-filled information into English. The prompt generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the converted information into prompts to be input into Stable Diffusion. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a person image based on the prompt using Stable Diffusion. The security unit is implemented by the specific processing unit 290 of the data processing device 12, which encrypts user input information and performs access control. The feedback collection unit collects user feedback using the microphone 238 of the robot 414 and stores it in the database 24 using the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12, which evaluates the accuracy of the generated prompts. The improvement unit is implemented by the specific processing unit 290 of the data processing device 12, which adjusts the image generation algorithm based on user feedback. The detailed input unit improves the specificity and detail of the information entered by the user using the microphone 238 of the robot 414.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] The data collection unit can analyze the user's past behavior history when collecting user input information and suggest the most appropriate question format. For example, it can reduce the user's burden by prioritizing the collection of information that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information collection methods to be used at specific times based on the user's past input history. In this way, the data collection unit can select the optimal collection method by analyzing the user's past behavior history.

[0132] The security department can estimate a user's emotions and adjust the security level based on those emotions to protect user privacy. For example, if a user is relaxed, the security level can be set to normal. If a user is stressed, the security level can be increased to ensure the user feels safe. Furthermore, if a user is agitated, the security level can be adjusted to calm the user. This allows the security department to implement more appropriate security measures by adjusting the security level according to the user's emotions.

[0133] The feedback collection unit can filter user feedback on generated images based on the user's current lifestyle and areas of interest. For example, it can prioritize collecting highly relevant feedback based on the user's current lifestyle. It can also filter out unnecessary feedback based on the user's areas of interest. Furthermore, it can adjust the scope of feedback collection based on the user's lifestyle and areas of interest. As a result, the feedback collection unit can collect highly relevant feedback by filtering based on the user's current lifestyle and areas of interest.

[0134] The evaluation unit can estimate the user's emotions when evaluating the accuracy of the generated prompts, and adjust the timing of the evaluation based on the estimated user emotions. For example, if the user is relaxed, the evaluation timing can be delayed to create an environment where the user can concentrate. If the user is stressed, the evaluation can be temporarily suspended and waited until the user relaxes. Also, if the user is excited, the evaluation can be performed quickly to make the necessary evaluations before the user's excitement subsides. In this way, the evaluation unit can make more appropriate evaluations by adjusting the timing of the evaluation according to the user's emotions.

[0135] The improvement unit can select the optimal improvement method by referring to the user's past feedback history when improving the generated image to meet user expectations. For example, it can propose the optimal improvement method based on the user's past feedback. It can extract specific patterns from the user's past feedback history and select the optimal improvement method. It can also analyze the user's past feedback history and propose efficient improvement methods. In this way, the improvement unit can select the optimal improvement method by referring to the user's past feedback history.

[0136] The detailed input section can estimate the user's emotions and adjust the timing of detailed input based on those emotions, thereby improving the specificity and detail of the information entered by the user. For example, if the user is relaxed, the timing of detailed input can be delayed to create an environment where the user can concentrate. If the user is stressed, detailed input can be temporarily suspended, and the system can wait until the user relaxes. Conversely, if the user is excited, detailed input can be performed quickly to ensure that the necessary information is entered before the user's excitement subsides. In this way, the detailed input section can adjust the timing of detailed input according to the user's emotions, enabling more appropriate detailed input.

[0137] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting user input information. For example, it can prioritize the collection of highly relevant information based on the user's current location. It can also filter out unnecessary information based on the user's geographical location. Furthermore, it can adjust the scope of information collection based on the user's geographical location. As a result, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location.

[0138] The slot-filling unit can estimate the user's emotions and adjust the order of slot-filling based on those emotions. For example, if the user is relaxed, detailed questions can be asked first. If the user is stressed, simpler questions can be asked first. Also, if the user is excited, questions that pique their interest can be asked first. In this way, the slot-filling unit can adjust the order of slot-filling according to the user's emotions, enabling more appropriate slot-filling.

[0139] The conversion unit can select the optimal conversion method when converting user input information to English by referring to the user's past input data. For example, it can suggest the optimal conversion method based on data the user has previously entered. It can extract specific patterns from the user's past input data and select the optimal conversion method. It can also analyze the user's past input data and suggest an efficient conversion method. In this way, the conversion unit can select the optimal conversion method by referring to the user's past input data.

[0140] The prompt generation unit can estimate the user's emotions and determine the priority of prompt generation based on those emotions. For example, if the user is relaxed, it can prioritize generating detailed prompts. If the user is stressed, it can prioritize generating concise prompts. If the user is excited, it can prioritize generating engaging prompts. In this way, the prompt generation unit can generate more appropriate prompts by determining the priority of prompt generation according to the user's emotions.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The collection unit collects user input information. For example, the collection unit provides an interface for users to input the conditions they seek in a marriage partner in a question-and-answer format. Specifically, it provides a form for inputting items such as age, occupation, hobbies, and personality, and can also store the information entered by the user in a database. Step 2: The slot filling unit fills the slots with the information collected by the collection unit. The slot filling unit uses, for example, a generation AI (e.g., Langchain) to fill the slots with information such as age, occupation, hobbies, and personality entered by the user. Step 3: The conversion unit converts the information slot-filled by the slot-filling unit into English. The conversion unit converts the slot-filled information into English, for example, using a generation AI. Specifically, it converts information such as "He is a 30-year-old engineer, his hobby is hiking, and he has a kind personality" into English. Step 4: The prompt generation unit converts the information converted by the conversion unit into a prompt. The prompt generation unit uses, for example, a generation AI to convert the converted information into a prompt to be input into Stable Diffusion. Specifically, it converts information such as "a 30-year-old engineer, whose hobby is hiking and who has a kind personality" into a prompt. Step 5: The image generation unit generates an image based on the prompt generated by the prompt generation unit. The image generation unit generates a person image based on the prompt, for example, using Stable Diffusion. Specifically, it generates a person image based on a prompt such as "a 30-year-old engineer whose hobby is hiking and who has a kind personality."

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0145] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0181] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0183] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0185] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0186] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0187] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0188] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0189] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0190] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0191] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0192] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0193] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0194] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0195] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0196] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0198] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0199] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0200] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0204] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0205] 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.

[0206] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0207] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0208] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0209] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0211] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0212] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0213] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0214] [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user input information, A slot-filling unit that slots the information collected by the aforementioned collection unit, A conversion unit that converts the information slot-filled by the slot-filling unit into English, A prompt generation unit converts the information converted by the conversion unit into a prompt, An image generation unit that generates an image based on a prompt generated by the prompt generation unit, Equipped with A system characterized by the following features.

2. It is equipped with a security section that has functions to protect user privacy. The system according to feature 1.

3. It includes a feedback collection unit that collects user feedback on the generated images. The system according to feature 1.

4. It includes an evaluation unit that evaluates the accuracy of the generated prompts. The system according to feature 1.

5. The generated image includes an improvement unit that has the functionality to meet user expectations. The system according to feature 1.

6. It features a detailed input section with a function to improve the specificity and detail of the information entered by the user. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past input history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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