system

Abstract

The system according to this embodiment aims to help seniors visualize and experience their own goals and dreams in a concrete way. [Solution] The system according to the embodiment comprises an input unit, a collection unit, an analysis unit, a generation unit, and an experience unit. The input unit takes the user's goals and dreams as input. The collection unit collects the information input by the input unit. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a VR scenario based on the information analyzed by the analysis unit. The experience unit experiences the VR scenario generated by the 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] <000002⑤>In the conventional technology, there is no sufficient means for seniors to specifically visualize and experience their own goals and dreams, and there is room for improvement.

[0005] The system according to the embodiment aims to enable seniors to specifically visualize and experience their own goals and dreams.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a collection unit, an analysis unit, a generation unit, and an experience unit. The input unit takes the user's goals and dreams as input. The collection unit collects the information input by the input unit. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a VR scenario based on the information analyzed by the analysis unit. The experience unit experiences the VR scenario generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows seniors to concretely visualize and experience their own goals and dreams. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0016] [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 system according to an embodiment of the present invention is a platform that allows seniors to create and experience their future, life plan goals, and dreams in a VR space. This platform includes a mechanism where the user inputs their future goals and dreams, a generating AI analyzes the input information, and recreates the user's future goals and dreams in a VR space. The generated VR scenario can be experienced by the user wearing a VR headset. This mechanism allows seniors to visualize and experience a concrete vision of their future, thereby encouraging them to remain active throughout their lives and taking further steps forward. Furthermore, the experience within the VR space can be improved based on user feedback, providing a more realistic and engaging experience. For example, when a user inputs their future goals and dreams, they can input specific career goals, personal dreams, short-term goals, long-term goals, etc. Next, the generating AI analyzes this information and recreates the user's future goals and dreams in a VR space. The generated VR scenario can be experienced by the user wearing a VR headset. This experience allows the user to visualize and experience a concrete vision of their future, thereby encouraging them to remain active throughout their lives and taking further steps forward. Furthermore, the experience within the VR space can be improved based on user feedback, providing a more realistic and engaging experience. For example, by collecting user feedback such as their impressions and opinions during the experience and incorporating them into the next experience, a more satisfying experience can be provided to users. This allows seniors to visualize and experience a concrete vision of the future, which can encourage them to remain active throughout their lives and take a step forward. In this way, the system can enhance users' motivation to remain active throughout their lives by creating and allowing them to experience seniors' future, life plan goals, and dreams in a VR space.

[0029] The system according to this embodiment comprises an input unit, a collection unit, an analysis unit, a generation unit, and an experience unit. The input unit receives the user's goals and dreams. For example, when a user enters their future goals and dreams, they can enter specific career goals, personal dreams, short-term goals, long-term goals, etc. The collection unit collects the information entered by the input unit. For example, the collection unit collects the goal and dream data entered by the user and transmits it to the analysis unit. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and applies an algorithm to understand the user's future goals and dreams. The generation unit generates a VR scenario based on the information analyzed by the analysis unit. For example, the generation unit generates a VR scenario that reproduces the user's future goals and dreams based on the analysis results. The experience unit experiences the VR scenario generated by the generation unit. For example, the experience unit allows the user to experience the generated VR scenario by wearing a VR headset. As a result, the system according to this embodiment allows the user to experience their goals and dreams in a VR space. Furthermore, the experience unit can collect user feedback and reflect it in the next experience. For example, the experience department can collect feedback on users' impressions and opinions during the experience and incorporate them into future experiences, thereby providing users with a more satisfying experience. This allows the system to enable users to experience their goals and dreams in a VR space.

[0030] The analysis unit includes a design unit that analyzes the collected information and designs a VR scenario based on the analysis results. The design unit analyzes the collected information and designs a VR scenario based on the analysis results. For example, the design unit analyzes data on the user's goals and dreams and designs a VR scenario based on that data. For example, the design unit determines the specific content and structure of the scenario based on the user's goals and dreams. For example, if the user's goal is a career goal, the design unit can design a scenario that simulates career success based on that goal. Also, if the user's dream is a personal dream, the design unit can design a scenario that simulates the realization of that dream based on that dream. This allows the design unit to design a VR scenario based on the analysis results. Some or all of the above processing in the design unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the design unit can input the user's goals and dreams into a generative AI, and the generative AI can design a VR scenario based on that data. This allows the design unit to determine the specific content and structure of the scenario based on the user's goals and dreams.

[0031] The experience unit includes a feedback collection unit that collects user feedback. The feedback collection unit collects user feedback. For example, the feedback collection unit collects the user's impressions and opinions during the experience. For example, the feedback collection unit can collect the results of a survey conducted by the user after the experience is completed. The feedback collection unit can also provide an interface that allows the user to provide feedback in real time during the experience. For example, the feedback collection unit provides an interface that allows the user to input their impressions and opinions in real time during the experience. This enables the collection of user feedback. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input user feedback data into a generative AI, which can then analyze the data and reflect it in the next experience. This enables the feedback collection unit to collect user feedback and reflect it in the next experience.

[0032] The feedback collection unit includes a feedback analysis unit that analyzes the collected feedback and reflects it in the next experience. The feedback analysis unit analyzes the collected feedback and reflects it in the next experience. For example, the feedback analysis unit analyzes the feedback data provided by the user and reflects the results in the next experience. For example, the feedback analysis unit can analyze the impressions and opinions that the user felt during the experience and improve the content of the next experience based on the results. For example, the feedback analysis unit can analyze the results of a questionnaire provided by the user after the experience and adjust the content of the next experience based on the results. In this way, the collected feedback can be reflected in the next experience. Some or all of the above processing in the feedback analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback analysis unit can input the user's feedback data into a generative AI, which can analyze the data and reflect it in the next experience. In this way, the feedback analysis unit can reflect the collected feedback in the next experience.

[0033] The input unit analyzes the user's past input history and proposes the optimal input method. For example, the input unit automatically displays goals and dreams that the user has frequently entered in the past as suggestions. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest goals and dreams to be entered at a specific time based on the user's past input history. This allows the input unit to propose the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input the user's past input history data into a generative AI, which can then analyze that data and propose the optimal input method. This allows the input unit to propose the optimal input method based on the user's past input history.

[0034] The input unit guides the user's input based on their current lifestyle and areas of interest. For example, when the user inputs their current lifestyle, the input unit suggests relevant goals and dreams. The input unit can also present specific examples of goals and dreams based on the user's areas of interest. Furthermore, the input unit can customize the input content according to the user's lifestyle and areas of interest. This allows the input unit to guide the user's input content according to their lifestyle and areas of interest. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then analyze that data to guide the input content. This allows the input unit to guide the user's input content according to their lifestyle and areas of interest.

[0035] The input unit prioritizes displaying highly relevant input content, taking into account the user's geographical location. For example, if the user lives in a specific region, the input unit can suggest goals and dreams related to that region. If the user is traveling, the input unit can also suggest goals and dreams related to their travel destination. Furthermore, the input unit can prioritize displaying region-specific goals and dreams based on the user's geographical location. This allows the input unit to prioritize displaying input content based on the user's geographical location. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without one. For example, the input unit can input the user's geographical location data into a generative AI, which can then analyze the data and prioritize displaying highly relevant input content. This allows the input unit to prioritize displaying input content based on the user's geographical location.

[0036] The input unit analyzes the user's social media activity during input and suggests relevant input content. For example, the input unit suggests relevant input content based on goals and dreams shared by the user on social media. The input unit can also identify areas of interest from the user's social media activity and suggest relevant goals and dreams. Furthermore, the input unit can analyze the user's social media activity history and suggest optimal input content. This allows the input unit to suggest input content based on the user's social media activity. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input the user's social media activity data into a generative AI, which can then analyze the data and suggest relevant input content. This allows the input unit to suggest input content based on the user's social media activity.

[0037] The data collection unit analyzes the user's past data collection history and selects the optimal collection method. For example, the data collection unit prioritizes data collection methods previously used by the user (e.g., surveys, interviews). The data collection unit can also select the most effective collection method based on the user's past data collection history. Furthermore, the data collection unit can analyze the user's past data collection history and select the optimal collection timing. This allows the data collection unit to select the optimal collection method based on the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past data collection history data into a generative AI, which can then analyze the data and select the optimal collection method. This allows the data collection unit to select the optimal collection method based on the user's past data collection history.

[0038] The data collection unit filters data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit prioritizes collecting relevant data based on the user's current lifestyle. The data collection unit can also filter out unnecessary data based on the user's areas of interest. Furthermore, the data collection unit can adjust the types of data collected according to the user's lifestyle and areas of interest. This enables data filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then analyze and filter that data. This enables the data collection unit to filter data based on the user's lifestyle and areas of interest.

[0039] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user lives in a specific region, the data collection unit prioritizes collecting data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting data related to their travel destination. The data collection unit can also prioritize collecting region-specific data based on the user's geographical location information. This allows for the priority collection of data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location data into a generative AI, which can then analyze the data and prioritize collecting highly relevant data. This allows the data collection unit to prioritize collecting data based on the user's geographical location information.

[0040] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects relevant data based on information shared by the user on social media. The data collection unit can also identify areas of interest from the user's social media activity and collect relevant data. Furthermore, the data collection unit can analyze the user's social media activity history and collect the most relevant data. This allows the data collection to be based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then analyze the data and collect relevant data. This allows the data collection unit to collect data based on the user's social media activity.

[0041] The analysis unit adjusts the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a standard analysis on general data. Furthermore, the analysis unit can perform a simplified analysis on unnecessary data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then adjust the level of detail of the analysis based on the importance of that data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected data.

[0042] The analysis unit applies different analysis methods depending on the data category during analysis. For example, the analysis unit applies a natural language processing analysis method to text data. The analysis unit can also apply an image recognition analysis method to image data. The analysis unit can also apply a speech recognition analysis method to speech data. This allows the analysis unit to apply an analysis method appropriate to the data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then apply an appropriate analysis method according to the data category. This allows the analysis unit to apply an analysis method appropriate to the data category.

[0043] The analysis unit determines the priority of analysis based on the data submission date during the analysis. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. The analysis unit can also adjust the analysis schedule based on the submission date. This allows the analysis unit to determine the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then determine the priority of analysis based on the data submission date. This allows the analysis unit to determine the priority of analysis based on the data submission date.

[0044] The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analyzing highly relevant data. It can also postpone analyzing less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI, and the generative AI can adjust the order of analysis based on the relevance of that data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0045] The generation unit adjusts the level of detail of the VR scenario based on the importance of the analysis results during generation. For example, the generation unit generates a detailed scenario based on important analysis results. The generation unit can also generate a standard scenario based on general analysis results. Furthermore, the generation unit can generate a simplified scenario based on unnecessary analysis results. This allows the level of detail of the VR scenario to be adjusted based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input analysis result data into a generation AI, which can then adjust the level of detail of the VR scenario based on the importance of that data. This allows the generation unit to adjust the level of detail of the VR scenario based on the importance of the analysis results.

[0046] The generation unit applies different generation algorithms depending on the scenario category during generation. For example, the generation unit applies a generation algorithm that emphasizes educational elements to educational scenarios. It can also apply a generation algorithm that emphasizes entertainment elements to entertainment scenarios. It can also apply a generation algorithm that emphasizes health promotion elements to health scenarios. This allows the generation unit to apply a generation algorithm appropriate to the scenario category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scenario category data into a generation AI, and the generation AI can apply an appropriate generation algorithm based on that data. This allows the generation unit to apply a generation algorithm appropriate to the scenario category.

[0047] The generation unit determines the generation priority based on the scenario submission date during generation. For example, the generation unit prioritizes generating the most recent scenarios. It can also postpone older scenarios. Furthermore, the generation unit can adjust the generation schedule based on the submission date. This allows the generation unit to determine the generation priority based on the scenario submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scenario submission date data into a generation AI, which can then determine the generation priority based on that data. This allows the generation unit to determine the generation priority based on the scenario submission date.

[0048] The generation unit adjusts the generation order based on the relevance of the scenarios during generation. For example, the generation unit prioritizes generating highly relevant scenarios. It can also postpone generating less relevant scenarios. The generation unit can also adjust the generation order based on the relevance of the scenarios. This allows the generation order to be adjusted based on the relevance of the scenarios. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scenario relevance data into a generation AI, and the generation AI can adjust the generation order based on that data. This allows the generation unit to adjust the generation order based on the relevance of the scenarios.

[0049] The experience unit provides the most suitable experience content by referring to the user's past experience history during the experience. For example, the experience unit provides relevant experience content based on the user's past experiences. The experience unit can also provide the most effective experience content based on the user's past experience history. Furthermore, the experience unit can analyze the user's past experience history and provide the most suitable experience content. This allows the experience unit to provide the most suitable experience content based on the user's past experience history. Some or all of the above processing in the experience unit may be performed using, for example, a generative AI, or without a generative AI. For example, the experience unit can input the user's past experience history data into a generative AI, which can then analyze that data to provide the most suitable experience content. This allows the experience unit to provide the most suitable experience content based on the user's past experience history.

[0050] The experience unit customizes the experience content based on the user's current living situation during the experience. For example, the experience unit provides relevant experience content based on the user's current living situation. The experience unit can also customize the experience content according to the user's living situation. Furthermore, the experience unit can provide optimal experience content considering the user's current living situation. This allows the experience content to be customized based on the user's current living situation. Some or all of the above processing in the experience unit may be performed using, for example, a generative AI, or without a generative AI. For example, the experience unit can input the user's current living situation data into a generative AI, which can then analyze that data to customize the experience content. This allows the experience unit to customize the experience content based on the user's current living situation.

[0051] The experience unit provides the most suitable experience content during the experience, taking into account the user's geographical location. For example, if the user lives in a specific region, the experience unit will provide an experience related to that region. If the user is traveling, the experience unit can also provide an experience related to their travel destination. Furthermore, the experience unit can provide region-specific experience content based on the user's geographical location. This allows the experience unit to provide the most suitable experience content based on the user's geographical location. Some or all of the above processing in the experience unit may be performed using, for example, a generative AI, or without a generative AI. For example, the experience unit can input the user's geographical location data into a generative AI, which can then analyze the data to provide the most suitable experience content. This allows the experience unit to provide the most suitable experience content based on the user's geographical location.

[0052] The Experience Department analyzes the user's social media activity during the experience and proposes relevant experiences. For example, the Experience Department proposes relevant experiences based on experiences the user has shared on social media. The Experience Department can also identify areas of interest from the user's social media activity and propose relevant experiences. Furthermore, the Experience Department can analyze the user's social media activity history and propose the most suitable experiences. This allows the Experience Department to propose experiences based on the user's social media activity. Some or all of the above processing in the Experience Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Experience Department can input the user's social media activity data into a generative AI, which can then analyze the data and propose relevant experiences. This allows the Experience Department to propose experiences based on the user's social media activity.

[0053] The design department adjusts the level of detail of the design based on the importance of the collected data during the design process. For example, the design department can perform a detailed design based on important data. Alternatively, the design department can perform a standard design based on general data. Furthermore, the design department can perform a simplified design based on unnecessary data. This allows the design department to adjust the level of detail of the design based on the importance of the collected data. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not. For example, the design department can input collected data into a generative AI, which can then adjust the level of detail of the design based on the importance of that data. This allows the design department to adjust the level of detail of the design based on the importance of the collected data.

[0054] The design department applies different design methods depending on the data category during the design phase. For example, the design department may apply a design method emphasizing educational elements to educational data. It may also apply a design method emphasizing entertainment elements to entertainment data. Furthermore, it may apply a design method emphasizing health promotion elements to health data. This allows the design department to apply design methods appropriate to the data category. Some or all of the above processing in the design department may be performed using, for example, a generative AI, or without a generative AI. For example, the design department can input collected data into a generative AI, which can then apply an appropriate design method according to the data category. This allows the design department to apply design methods appropriate to the data category.

[0055] The design department determines design priorities based on the data submission timing during the design phase. For example, the design department prioritizes the most recent data. Alternatively, the design department may postpone older data submissions. The design department may also adjust the design schedule based on the submission timing. This allows the design department to determine design priorities based on data submission timing. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not. For example, the design department can input collected data into a generative AI, which can then determine design priorities based on the data submission timing. This allows the design department to determine design priorities based on data submission timing.

[0056] The design department adjusts the order of design based on the relevance of the data during the design process. For example, the design department prioritizes designing highly relevant data. The design department can also postpone designing less relevant data. The design department can also adjust the order of design based on the relevance of the data. This allows the design department to adjust the order of design based on the relevance of the data. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not using a generative AI. For example, the design department can input collected data into a generative AI, which can then adjust the order of design based on the relevance of that data. This allows the design department to adjust the order of design based on the relevance of the data.

[0057] The feedback collection unit selects the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit prioritizes selecting feedback collection methods (such as surveys and interviews) that the user has used in the past. The feedback collection unit can also select the most effective collection method from the user's past feedback history. Furthermore, the feedback collection unit can analyze the user's past feedback history and select the optimal collection timing. This allows the feedback collection unit to select the optimal collection method based on the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's past feedback history data into a generative AI, which can then analyze the data to select the optimal collection method. This allows the feedback collection unit to select the optimal collection method based on the user's past feedback history.

[0058] The feedback collection unit customizes the collected content based on the user's current living situation when collecting feedback. For example, the feedback collection unit collects relevant feedback content based on the user's current living situation. The feedback collection unit can also customize the feedback content according to the user's living situation. Furthermore, the feedback collection unit can collect optimal feedback content considering the user's current living situation. This allows the collected content to be customized based on the user's current living situation. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's current living situation data into a generative AI, which can then analyze the data and customize the collected content. This allows the feedback collection unit to customize the collected content based on the user's current living situation.

[0059] The feedback collection unit selects the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, if the user lives in a specific region, the feedback collection unit collects feedback related to that region. Furthermore, if the user is traveling, the feedback collection unit can collect feedback related to their travel destination. The feedback collection unit can also collect region-specific feedback based on the user's geographical location information. This allows the feedback collection unit to select the optimal collection method based on the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's geographical location data into a generative AI, which can then analyze the data to select the optimal collection method. This allows the feedback collection unit to select the optimal collection method based on the user's geographical location information.

[0060] The feedback collection unit analyzes the user's social media activity and proposes content to collect when collecting feedback. For example, the feedback collection unit proposes relevant feedback based on information shared by the user on social media. The feedback collection unit can also identify areas of interest from the user's social media activity and propose relevant feedback. Furthermore, the feedback collection unit can analyze the user's social media activity history and propose optimal feedback. This allows the feedback collection unit to propose content based on the user's social media activity. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's social media activity data into a generative AI, which can then analyze the data and propose content to collect. This allows the feedback collection unit to propose content based on the user's social media activity.

[0061] The feedback analysis unit adjusts the level of detail of the analysis based on the importance of the collected feedback during the feedback analysis. For example, the feedback analysis unit performs a detailed analysis on important feedback. The feedback analysis unit can also perform a standard analysis on general feedback. Furthermore, the feedback analysis unit can perform a simplified analysis on unnecessary feedback. This allows the level of detail of the analysis to be adjusted based on the importance of the collected feedback. Some or all of the above processing in the feedback analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the feedback analysis unit can input the collected feedback data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on the importance of that data. This allows the feedback analysis unit to adjust the level of detail of the analysis based on the importance of the collected feedback.

[0062] The feedback analysis unit applies different analysis methods depending on the category of the feedback during feedback analysis. For example, the feedback analysis unit applies a natural language processing analysis method to text feedback. The feedback analysis unit can also apply an image recognition analysis method to image feedback. The feedback analysis unit can also apply a speech recognition analysis method to speech feedback. This allows the feedback analysis unit to apply an analysis method appropriate to the category of the feedback. Some or all of the above processing in the feedback analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback analysis unit can input collected feedback data into a generative AI, and the generative AI can apply an appropriate analysis method according to the category of the data. This allows the feedback analysis unit to apply an analysis method appropriate to the category of the feedback.

[0063] The feedback analysis unit determines the priority of analysis based on the submission date of the feedback during the feedback analysis process. For example, the feedback analysis unit prioritizes the analysis of the most recent feedback. The feedback analysis unit can also postpone the analysis of older feedback. Furthermore, the feedback analysis unit can adjust the analysis schedule based on the submission date. This allows the feedback analysis unit to determine the priority of analysis based on the submission date of the feedback. Some or all of the above-described processes in the feedback analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the feedback analysis unit can input the collected feedback data into a generating AI, which can then determine the priority of analysis based on the submission date of that data. This allows the feedback analysis unit to determine the priority of analysis based on the submission date of the feedback.

[0064] The feedback analysis unit adjusts the order of analysis based on the relevance of the feedback during feedback analysis. For example, the feedback analysis unit prioritizes analyzing highly relevant feedback. The feedback analysis unit can also postpone analyzing less relevant feedback. Furthermore, the feedback analysis unit can adjust the order of analysis based on the relevance of the feedback. This allows the order of analysis to be adjusted based on the relevance of the feedback. Some or all of the above processing in the feedback analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the feedback analysis unit can input the collected feedback data into a generating AI, and the generating AI can adjust the order of analysis based on the relevance of that data. This allows the feedback analysis unit to adjust the order of analysis based on the relevance of the feedback.

[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 input section can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display goals and dreams that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest goals and dreams that the user might enter at specific times based on their past input history. This allows the system to suggest the optimal input method based on the user's past input history.

[0067] The analysis unit may also include a design unit that analyzes the collected information and designs VR scenarios based on the analysis results. The design unit determines the specific content and structure of the scenario based on the user's goals and dreams. For example, if the user's goal is a career goal, the design unit can design a scenario that simulates career success based on that goal. Also, if the user's dream is a personal dream, the design unit can design a scenario that simulates the realization of that dream based on that dream. This allows for the design of VR scenarios based on the analysis results.

[0068] The experience section can also include a feedback collection section to gather user feedback. The feedback collection section collects the user's impressions and opinions during the experience. For example, the feedback collection section can conduct a survey after the user completes the experience and collect the results. Furthermore, the feedback collection section can provide an interface that allows users to provide real-time feedback during the experience, thereby enabling the collection of user feedback.

[0069] The feedback collection unit may also include a feedback analysis unit that analyzes the collected feedback and incorporates it into the next experience. The feedback analysis unit analyzes the feedback data provided by the user and reflects the results in the next experience. For example, it can analyze the impressions and opinions that the user felt during the experience and use the results to improve the content of the next experience. In this way, the collected feedback can be reflected in the next experience.

[0070] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display goals and dreams that the user has frequently entered in the past as suggestions. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest goals and dreams that the user might enter at a specific time based on their past input history. This allows the system to suggest the optimal input method based on the user's past input history.

[0071] The input section can also guide the user's input based on their current life circumstances and areas of interest. For example, when a user enters their current life circumstances, it can suggest relevant goals and dreams. It can also present specific examples of goals and dreams based on the user's areas of interest. This allows the input content to be guided according to the user's life circumstances and areas of interest.

[0072] The input section can also prioritize displaying highly relevant input content by considering the user's geographical location. For example, if the user lives in a specific region, it can suggest goals and dreams related to that region. Similarly, if the user is traveling, it can suggest goals and dreams related to their travel destination. This allows for the display of input content based on the user's geographical location.

[0073] The input section can also analyze the user's social media activity during input and suggest relevant input content. For example, it can suggest relevant input content based on goals and dreams the user has shared on social media. It can also identify areas of interest from the user's social media activity and suggest related goals and dreams. This allows the system to suggest input content based on the user's social media activity.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The input section is where users enter their goals and dreams. For example, when users enter their future goals and dreams, they can enter specific career goals, personal dreams, short-term goals, long-term goals, etc. Step 2: The collection unit collects the information entered by the input unit. For example, the collection unit collects data on goals and dreams entered by the user and sends it to the analysis unit. Step 3: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and applies algorithms to understand the user's future goals and dreams. Step 4: The generation unit generates a VR scenario based on the information analyzed by the analysis unit. For example, the generation unit generates a VR scenario that recreates the user's future goals and dreams based on the analysis results. Step 5: The experience unit experiences the VR scenario generated by the generation unit. For example, the experience unit allows the user to experience the generated VR scenario by putting on a VR headset. Furthermore, the experience unit can collect user feedback and incorporate it into the next experience. For example, the experience unit can collect the user's feelings and opinions during the experience as feedback and incorporate it into the next experience to provide a more satisfying experience for the user.

[0076] (Example of form 2) The system according to an embodiment of the present invention is a platform that allows seniors to create and experience their future, life plan goals, and dreams in a VR space. This platform includes a mechanism where the user inputs their future goals and dreams, a generating AI analyzes the input information, and recreates the user's future goals and dreams in a VR space. The generated VR scenario can be experienced by the user wearing a VR headset. This mechanism allows seniors to visualize and experience a concrete vision of their future, thereby encouraging them to remain active throughout their lives and taking further steps forward. Furthermore, the experience within the VR space can be improved based on user feedback, providing a more realistic and engaging experience. For example, when a user inputs their future goals and dreams, they can input specific career goals, personal dreams, short-term goals, long-term goals, etc. Next, the generating AI analyzes this information and recreates the user's future goals and dreams in a VR space. The generated VR scenario can be experienced by the user wearing a VR headset. This experience allows the user to visualize and experience a concrete vision of their future, thereby encouraging them to remain active throughout their lives and taking further steps forward. Furthermore, the experience within the VR space can be improved based on user feedback, providing a more realistic and engaging experience. For example, by collecting user feedback such as their impressions and opinions during the experience and incorporating them into the next experience, a more satisfying experience can be provided to users. This allows seniors to visualize and experience a concrete vision of the future, which can encourage them to remain active throughout their lives and take a step forward. In this way, the system can enhance users' motivation to remain active throughout their lives by creating and allowing them to experience seniors' future, life plan goals, and dreams in a VR space.

[0077] The system according to this embodiment comprises an input unit, a collection unit, an analysis unit, a generation unit, and an experience unit. The input unit receives the user's goals and dreams. For example, when a user enters their future goals and dreams, they can enter specific career goals, personal dreams, short-term goals, long-term goals, etc. The collection unit collects the information entered by the input unit. For example, the collection unit collects the goal and dream data entered by the user and transmits it to the analysis unit. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and applies an algorithm to understand the user's future goals and dreams. The generation unit generates a VR scenario based on the information analyzed by the analysis unit. For example, the generation unit generates a VR scenario that reproduces the user's future goals and dreams based on the analysis results. The experience unit experiences the VR scenario generated by the generation unit. For example, the experience unit allows the user to experience the generated VR scenario by wearing a VR headset. As a result, the system according to this embodiment allows the user to experience their goals and dreams in a VR space. Furthermore, the experience unit can collect user feedback and reflect it in the next experience. For example, the experience department can collect feedback on users' impressions and opinions during the experience and incorporate them into future experiences, thereby providing users with a more satisfying experience. This allows the system to enable users to experience their goals and dreams in a VR space.

[0078] The analysis unit includes a design unit that analyzes the collected information and designs a VR scenario based on the analysis results. The design unit analyzes the collected information and designs a VR scenario based on the analysis results. For example, the design unit analyzes data on the user's goals and dreams and designs a VR scenario based on that data. For example, the design unit determines the specific content and structure of the scenario based on the user's goals and dreams. For example, if the user's goal is a career goal, the design unit can design a scenario that simulates career success based on that goal. Also, if the user's dream is a personal dream, the design unit can design a scenario that simulates the realization of that dream based on that dream. This allows the design unit to design a VR scenario based on the analysis results. Some or all of the above processing in the design unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the design unit can input the user's goals and dreams into a generative AI, and the generative AI can design a VR scenario based on that data. This allows the design unit to determine the specific content and structure of the scenario based on the user's goals and dreams.

[0079] The experience unit includes a feedback collection unit that collects user feedback. The feedback collection unit collects user feedback. For example, the feedback collection unit collects the user's impressions and opinions during the experience. For example, the feedback collection unit can collect the results of a survey conducted by the user after the experience is completed. The feedback collection unit can also provide an interface that allows the user to provide feedback in real time during the experience. For example, the feedback collection unit provides an interface that allows the user to input their impressions and opinions in real time during the experience. This enables the collection of user feedback. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input user feedback data into a generative AI, which can then analyze the data and reflect it in the next experience. This enables the feedback collection unit to collect user feedback and reflect it in the next experience.

[0080] The feedback collection unit includes a feedback analysis unit that analyzes the collected feedback and reflects it in the next experience. The feedback analysis unit analyzes the collected feedback and reflects it in the next experience. For example, the feedback analysis unit analyzes the feedback data provided by the user and reflects the results in the next experience. For example, the feedback analysis unit can analyze the impressions and opinions that the user felt during the experience and improve the content of the next experience based on the results. For example, the feedback analysis unit can analyze the results of a questionnaire provided by the user after the experience and adjust the content of the next experience based on the results. In this way, the collected feedback can be reflected in the next experience. Some or all of the above processing in the feedback analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the feedback analysis unit can input the user's feedback data into a generative AI, which can analyze the data and reflect it in the next experience. In this way, the feedback analysis unit can reflect the collected feedback in the next experience.

[0081] The input unit estimates the user's emotions and adjusts the design of the input interface based on the estimated emotions. For example, if the user is tense, the input unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the input unit can provide an interface with bright colors to make the input process more enjoyable. If the user is tired, the input unit can provide a simple and highly visible interface to facilitate the input process. This allows the input unit to provide an input interface design that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the input unit may be performed using a generative AI, or not. For example, the input unit can input user emotion data into a generative AI, which can then adjust the design of the input interface based on that data. This allows the input unit to provide an input interface design that responds to the user's emotions.

[0082] The input unit analyzes the user's past input history and proposes the optimal input method. For example, the input unit automatically displays goals and dreams that the user has frequently entered in the past as suggestions. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest goals and dreams to be entered at a specific time based on the user's past input history. This allows the input unit to propose the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input the user's past input history data into a generative AI, which can then analyze that data and propose the optimal input method. This allows the input unit to propose the optimal input method based on the user's past input history.

[0083] The input unit guides the user's input based on their current lifestyle and areas of interest. For example, when the user inputs their current lifestyle, the input unit suggests relevant goals and dreams. The input unit can also present specific examples of goals and dreams based on the user's areas of interest. Furthermore, the input unit can customize the input content according to the user's lifestyle and areas of interest. This allows the input unit to guide the user's input content according to their lifestyle and areas of interest. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then analyze that data to guide the input content. This allows the input unit to guide the user's input content according to their lifestyle and areas of interest.

[0084] The input unit estimates the user's emotions and prioritizes the input content based on the estimated emotions. For example, if the user is stressed, the input unit prioritizes content that can be entered quickly and easily. If the user is relaxed, the input unit may also prioritize detailed input content. If the user is in a hurry, the input unit may also prioritize input of important goals or dreams. This allows the input unit to determine the priority of input content 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. Some or all of the above processing in the input unit may be performed using a generative AI, or not. For example, the input unit can input user emotion data into a generative AI, which can then determine the priority of input content based on that data. This allows the input unit to determine the priority of input content according to the user's emotions.

[0085] The input unit prioritizes displaying highly relevant input content, taking into account the user's geographical location. For example, if the user lives in a specific region, the input unit can suggest goals and dreams related to that region. If the user is traveling, the input unit can also suggest goals and dreams related to their travel destination. Furthermore, the input unit can prioritize displaying region-specific goals and dreams based on the user's geographical location. This allows the input unit to prioritize displaying input content based on the user's geographical location. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without one. For example, the input unit can input the user's geographical location data into a generative AI, which can then analyze the data and prioritize displaying highly relevant input content. This allows the input unit to prioritize displaying input content based on the user's geographical location.

[0086] The input unit analyzes the user's social media activity during input and suggests relevant input content. For example, the input unit suggests relevant input content based on goals and dreams shared by the user on social media. The input unit can also identify areas of interest from the user's social media activity and suggest relevant goals and dreams. Furthermore, the input unit can analyze the user's social media activity history and suggest optimal input content. This allows the input unit to suggest input content based on the user's social media activity. Some or all of the above processing in the input unit may be performed using, for example, a generative AI, or without a generative AI. For example, the input unit can input the user's social media activity data into a generative AI, which can then analyze the data and suggest relevant input content. This allows the input unit to suggest input content based on the user's social media activity.

[0087] The data collection unit estimates the user's emotions and adjusts the data collection timing based on the estimated emotions. For example, if the user is relaxed, the data collection unit can delay the data collection timing to collect detailed information. If the user is in a hurry, the data collection unit can also speed up the data collection timing to collect only the minimum necessary information. If the user is excited, the data collection unit can adjust the data collection timing to collect appropriate information. This allows the data collection unit to adjust the data collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the data collection timing based on that data. This allows the data collection unit to adjust the data collection timing according to the user's emotions.

[0088] The data collection unit analyzes the user's past data collection history and selects the optimal collection method. For example, the data collection unit prioritizes data collection methods previously used by the user (e.g., surveys, interviews). The data collection unit can also select the most effective collection method based on the user's past data collection history. Furthermore, the data collection unit can analyze the user's past data collection history and select the optimal collection timing. This allows the data collection unit to select the optimal collection method based on the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past data collection history data into a generative AI, which can then analyze the data and select the optimal collection method. This allows the data collection unit to select the optimal collection method based on the user's past data collection history.

[0089] The data collection unit filters data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit prioritizes collecting relevant data based on the user's current lifestyle. The data collection unit can also filter out unnecessary data based on the user's areas of interest. Furthermore, the data collection unit can adjust the types of data collected according to the user's lifestyle and areas of interest. This enables data filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then analyze and filter that data. This enables the data collection unit to filter data based on the user's lifestyle and areas of interest.

[0090] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting only the essential data. This allows the data collection unit to determine the priority of data 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. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of data to collect based on that data. This allows the data collection unit to determine the priority of data according to the user's emotions.

[0091] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user lives in a specific region, the data collection unit prioritizes collecting data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting data related to their travel destination. The data collection unit can also prioritize collecting region-specific data based on the user's geographical location information. This allows for the priority collection of data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location data into a generative AI, which can then analyze the data and prioritize collecting highly relevant data. This allows the data collection unit to prioritize collecting data based on the user's geographical location information.

[0092] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects relevant data based on information shared by the user on social media. The data collection unit can also identify areas of interest from the user's social media activity and collect relevant data. Furthermore, the data collection unit can analyze the user's social media activity history and collect the most relevant data. This allows the data collection to be based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then analyze the data and collect relevant data. This allows the data collection unit to collect data based on the user's social media activity.

[0093] The analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit applies an algorithm that performs a detailed analysis. If the user is in a hurry, the analysis unit can also apply an algorithm that performs a rapid analysis. If the user is excited, the analysis unit can also apply an algorithm that provides visually stimulating analysis results. This allows the analysis algorithm to be adjusted 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. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the analysis algorithm based on that data. This allows the analysis unit to adjust the analysis algorithm according to the user's emotions.

[0094] The analysis unit adjusts the level of detail of the analysis based on the importance of the collected data during the analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a standard analysis on general data. Furthermore, the analysis unit can perform a simplified analysis on unnecessary data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then adjust the level of detail of the analysis based on the importance of that data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected data.

[0095] The analysis unit applies different analysis methods depending on the data category during analysis. For example, the analysis unit applies a natural language processing analysis method to text data. The analysis unit can also apply an image recognition analysis method to image data. The analysis unit can also apply a speech recognition analysis method to speech data. This allows the analysis unit to apply an analysis method appropriate to the data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then apply an appropriate analysis method according to the data category. This allows the analysis unit to apply an analysis method appropriate to the data category.

[0096] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the analysis results based on that data. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions.

[0097] The analysis unit determines the priority of analysis based on the data submission date during the analysis. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. The analysis unit can also adjust the analysis schedule based on the submission date. This allows the analysis unit to determine the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then determine the priority of analysis based on the data submission date. This allows the analysis unit to determine the priority of analysis based on the data submission date.

[0098] The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analyzing highly relevant data. It can also postpone analyzing less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected data into a generative AI, and the generative AI can adjust the order of analysis based on the relevance of that data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0099] The generation unit estimates the user's emotions and adjusts the content of the VR scenario it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a calm scenario. If the user is excited, the generation unit can also generate a stimulating scenario. If the user is stressed, the generation unit can also generate a relaxing scenario. This allows the content of the VR scenario to be adjusted 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can adjust the content of the VR scenario based on that data. This allows the generation unit to adjust the content of the VR scenario according to the user's emotions.

[0100] The generation unit adjusts the level of detail of the VR scenario based on the importance of the analysis results during generation. For example, the generation unit generates a detailed scenario based on important analysis results. The generation unit can also generate a standard scenario based on general analysis results. Furthermore, the generation unit can generate a simplified scenario based on unnecessary analysis results. This allows the level of detail of the VR scenario to be adjusted based on the importance of the analysis results. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input analysis result data into a generation AI, which can then adjust the level of detail of the VR scenario based on the importance of that data. This allows the generation unit to adjust the level of detail of the VR scenario based on the importance of the analysis results.

[0101] The generation unit applies different generation algorithms depending on the scenario category during generation. For example, the generation unit applies a generation algorithm that emphasizes educational elements to educational scenarios. It can also apply a generation algorithm that emphasizes entertainment elements to entertainment scenarios. It can also apply a generation algorithm that emphasizes health promotion elements to health scenarios. This allows the generation unit to apply a generation algorithm appropriate to the scenario category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scenario category data into a generation AI, and the generation AI can apply an appropriate generation algorithm based on that data. This allows the generation unit to apply a generation algorithm appropriate to the scenario category.

[0102] The generation unit estimates the user's emotions and adjusts the length of the VR scenario it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a short, concise scenario. If the user is relaxed, the generation unit can also generate a longer scenario with detailed explanations. If the user is excited, the generation unit can also generate a scenario with visually stimulating effects. This allows the length of the VR scenario to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the length of the VR scenario based on that data. This allows the generation unit to adjust the length of the VR scenario according to the user's emotions.

[0103] The generation unit determines the generation priority based on the scenario submission date during generation. For example, the generation unit prioritizes generating the most recent scenarios. It can also postpone older scenarios. Furthermore, the generation unit can adjust the generation schedule based on the submission date. This allows the generation unit to determine the generation priority based on the scenario submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scenario submission date data into a generation AI, which can then determine the generation priority based on that data. This allows the generation unit to determine the generation priority based on the scenario submission date.

[0104] The generation unit adjusts the generation order based on the relevance of the scenarios during generation. For example, the generation unit prioritizes generating highly relevant scenarios. It can also postpone generating less relevant scenarios. The generation unit can also adjust the generation order based on the relevance of the scenarios. This allows the generation order to be adjusted based on the relevance of the scenarios. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input scenario relevance data into a generation AI, and the generation AI can adjust the generation order based on that data. This allows the generation unit to adjust the generation order based on the relevance of the scenarios.

[0105] The experience unit estimates the user's emotions and adjusts the experience content based on the estimated emotions. For example, if the user is relaxed, the experience unit provides a calm experience. If the user is excited, the experience unit can also provide a stimulating experience. If the user is stressed, the experience unit can also provide a relaxing experience. This allows the experience content to be adjusted 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. Some or all of the above processing in the experience unit may be performed using a generative AI, or not using a generative AI. For example, the experience unit can input user emotion data into a generative AI, and the generative AI can adjust the experience content based on that data. This allows the experience unit to adjust the experience content according to the user's emotions.

[0106] The experience unit provides the most suitable experience content by referring to the user's past experience history during the experience. For example, the experience unit provides relevant experience content based on the user's past experiences. The experience unit can also provide the most effective experience content based on the user's past experience history. Furthermore, the experience unit can analyze the user's past experience history and provide the most suitable experience content. This allows the experience unit to provide the most suitable experience content based on the user's past experience history. Some or all of the above processing in the experience unit may be performed using, for example, a generative AI, or without a generative AI. For example, the experience unit can input the user's past experience history data into a generative AI, which can then analyze that data to provide the most suitable experience content. This allows the experience unit to provide the most suitable experience content based on the user's past experience history.

[0107] The experience unit customizes the experience content based on the user's current living situation during the experience. For example, the experience unit provides relevant experience content based on the user's current living situation. The experience unit can also customize the experience content according to the user's living situation. Furthermore, the experience unit can provide optimal experience content considering the user's current living situation. This allows the experience content to be customized based on the user's current living situation. Some or all of the above processing in the experience unit may be performed using, for example, a generative AI, or without a generative AI. For example, the experience unit can input the user's current living situation data into a generative AI, which can then analyze that data to customize the experience content. This allows the experience unit to customize the experience content based on the user's current living situation.

[0108] The experience unit estimates the user's emotions and determines the priority of experiences based on those estimated emotions. For example, if the user is stressed, the experience unit will prioritize relaxing experiences. If the user is relaxed, the experience unit may also prioritize detailed experiences. Furthermore, if the user is in a hurry, the experience unit may prioritize important experiences. This allows the experience unit to determine the priority of experiences in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the experience unit may be performed using, for example, generative AI, or not. For example, the experience unit can input user emotion data into a generative AI, which can then determine the priority of experiences based on that data. This allows the experience unit to determine the priority of experiences in accordance with the user's emotions.

[0109] The experience unit provides the most suitable experience content during the experience, taking into account the user's geographical location. For example, if the user lives in a specific region, the experience unit will provide an experience related to that region. If the user is traveling, the experience unit can also provide an experience related to their travel destination. Furthermore, the experience unit can provide region-specific experience content based on the user's geographical location. This allows the experience unit to provide the most suitable experience content based on the user's geographical location. Some or all of the above processing in the experience unit may be performed using, for example, a generative AI, or without a generative AI. For example, the experience unit can input the user's geographical location data into a generative AI, which can then analyze the data to provide the most suitable experience content. This allows the experience unit to provide the most suitable experience content based on the user's geographical location.

[0110] The Experience Department analyzes the user's social media activity during the experience and proposes relevant experiences. For example, the Experience Department proposes relevant experiences based on experiences the user has shared on social media. The Experience Department can also identify areas of interest from the user's social media activity and propose relevant experiences. Furthermore, the Experience Department can analyze the user's social media activity history and propose the most suitable experiences. This allows the Experience Department to propose experiences based on the user's social media activity. Some or all of the above processing in the Experience Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Experience Department can input the user's social media activity data into a generative AI, which can then analyze the data and propose relevant experiences. This allows the Experience Department to propose experiences based on the user's social media activity.

[0111] The design department estimates the user's emotions and adjusts the VR scenario design method based on the estimated user emotions. For example, if the user is relaxed, the design department will design a calm scenario. If the user is excited, the design department may design a stimulating scenario. If the user is stressed, the design department may design a relaxing scenario. This allows the design department to adjust the VR scenario design method 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. Some or all of the above processing in the design department may be performed using a generative AI, or not using a generative AI. For example, the design department can input user emotion data into a generative AI, and the generative AI can adjust the VR scenario design method based on that data. This allows the design department to adjust the VR scenario design method according to the user's emotions.

[0112] The design department adjusts the level of detail of the design based on the importance of the collected data during the design process. For example, the design department can perform a detailed design based on important data. Alternatively, the design department can perform a standard design based on general data. Furthermore, the design department can perform a simplified design based on unnecessary data. This allows the design department to adjust the level of detail of the design based on the importance of the collected data. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not. For example, the design department can input collected data into a generative AI, which can then adjust the level of detail of the design based on the importance of that data. This allows the design department to adjust the level of detail of the design based on the importance of the collected data.

[0113] The design department applies different design methods depending on the data category during the design phase. For example, the design department may apply a design method emphasizing educational elements to educational data. It may also apply a design method emphasizing entertainment elements to entertainment data. Furthermore, it may apply a design method emphasizing health promotion elements to health data. This allows the design department to apply design methods appropriate to the data category. Some or all of the above processing in the design department may be performed using, for example, a generative AI, or without a generative AI. For example, the design department can input collected data into a generative AI, which can then apply an appropriate design method according to the data category. This allows the design department to apply design methods appropriate to the data category.

[0114] The design department estimates the user's emotions and determines design priorities based on those estimated emotions. For example, if the user is stressed, the design department will prioritize relaxing designs. If the user is relaxed, the design department may also prioritize detailed designs. If the user is in a hurry, the design department may also prioritize important designs. This allows the design department to determine design priorities in accordance with the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using generative AI, or not. For example, the design department can input user emotion data into a generative AI, which can then determine design priorities based on that data. This allows the design department to determine design priorities in accordance with the user's emotions.

[0115] The design department determines design priorities based on the data submission timing during the design phase. For example, the design department prioritizes the most recent data. Alternatively, the design department may postpone older data submissions. The design department may also adjust the design schedule based on the submission timing. This allows the design department to determine design priorities based on data submission timing. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not. For example, the design department can input collected data into a generative AI, which can then determine design priorities based on the data submission timing. This allows the design department to determine design priorities based on data submission timing.

[0116] The design department adjusts the order of design based on the relevance of the data during the design process. For example, the design department prioritizes designing highly relevant data. The design department can also postpone designing less relevant data. The design department can also adjust the order of design based on the relevance of the data. This allows the design department to adjust the order of design based on the relevance of the data. Some or all of the above processes in the design department may be performed using, for example, a generative AI, or not using a generative AI. For example, the design department can input collected data into a generative AI, which can then adjust the order of design based on the relevance of that data. This allows the design department to adjust the order of design based on the relevance of the data.

[0117] The feedback collection unit estimates the user's emotions and adjusts the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit may apply a method for collecting detailed feedback. If the user is in a hurry, the feedback collection unit may apply a method for collecting concise feedback. If the user is excited, the feedback collection unit may apply a method for collecting visually stimulating feedback. This allows the feedback collection method to be adjusted 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. Some or all of the above processing in the feedback collection unit may be performed using a generative AI, or not using a generative AI. For example, the feedback collection unit can input user emotion data into a generative AI, which can then adjust the feedback collection method based on that data. This allows the feedback collection unit to adjust the feedback collection method according to the user's emotions.

[0118] The feedback collection unit selects the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit prioritizes selecting feedback collection methods (such as surveys and interviews) that the user has used in the past. The feedback collection unit can also select the most effective collection method from the user's past feedback history. Furthermore, the feedback collection unit can analyze the user's past feedback history and select the optimal collection timing. This allows the feedback collection unit to select the optimal collection method based on the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's past feedback history data into a generative AI, which can then analyze the data to select the optimal collection method. This allows the feedback collection unit to select the optimal collection method based on the user's past feedback history.

[0119] The feedback collection unit customizes the collected content based on the user's current living situation when collecting feedback. For example, the feedback collection unit collects relevant feedback content based on the user's current living situation. The feedback collection unit can also customize the feedback content according to the user's living situation. Furthermore, the feedback collection unit can collect optimal feedback content considering the user's current living situation. This allows the collected content to be customized based on the user's current living situation. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's current living situation data into a generative AI, which can then analyze the data and customize the collected content. This allows the feedback collection unit to customize the collected content based on the user's current living situation.

[0120] The feedback collection unit estimates the user's emotions and determines the priority of feedback collection based on the estimated emotions. For example, if the user is stressed, the feedback collection unit will prioritize collecting important feedback. If the user is relaxed, the feedback collection unit may also prioritize collecting detailed feedback. Furthermore, if the user is in a hurry, the feedback collection unit may prioritize collecting only the essential feedback. This allows for the determination of feedback collection priorities 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback collection unit may be performed using, for example, a generative AI, or not. For example, the feedback collection unit can input user emotion data into a generative AI, which can then determine the priority of feedback collection based on that data. This allows the feedback collection unit to determine the priority of feedback collection based on the user's emotions.

[0121] The feedback collection unit selects the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, if the user lives in a specific region, the feedback collection unit collects feedback related to that region. Furthermore, if the user is traveling, the feedback collection unit can collect feedback related to their travel destination. The feedback collection unit can also collect region-specific feedback based on the user's geographical location information. This allows the feedback collection unit to select the optimal collection method based on the user's geographical location information. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's geographical location data into a generative AI, which can then analyze the data to select the optimal collection method. This allows the feedback collection unit to select the optimal collection method based on the user's geographical location information.

[0122] The feedback collection unit analyzes the user's social media activity and proposes content to collect when collecting feedback. For example, the feedback collection unit proposes relevant feedback based on information shared by the user on social media. The feedback collection unit can also identify areas of interest from the user's social media activity and propose relevant feedback. Furthermore, the feedback collection unit can analyze the user's social media activity history and propose optimal feedback. This allows the feedback collection unit to propose content based on the user's social media activity. Some or all of the above processing in the feedback collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback collection unit can input the user's social media activity data into a generative AI, which can then analyze the data and propose content to collect. This allows the feedback collection unit to propose content based on the user's social media activity.

[0123] The feedback analysis unit estimates the user's emotions and adjusts the feedback analysis method based on the estimated emotions. For example, if the user is relaxed, the feedback analysis unit performs a detailed feedback analysis. If the user is in a hurry, the feedback analysis unit can also perform a rapid feedback analysis. If the user is excited, the feedback analysis unit can also perform a visually stimulating feedback analysis. This allows the feedback analysis unit to adjust the feedback analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the feedback analysis unit may be performed using a generative AI, or not using a generative AI. For example, the feedback analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the feedback analysis method based on that data. This allows the feedback analysis unit to adjust the feedback analysis method according to the user's emotions.

[0124] The feedback analysis unit adjusts the level of detail of the analysis based on the importance of the collected feedback during the feedback analysis. For example, the feedback analysis unit performs a detailed analysis on important feedback. The feedback analysis unit can also perform a standard analysis on general feedback. Furthermore, the feedback analysis unit can perform a simplified analysis on unnecessary feedback. This allows the level of detail of the analysis to be adjusted based on the importance of the collected feedback. Some or all of the above processing in the feedback analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the feedback analysis unit can input the collected feedback data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on the importance of that data. This allows the feedback analysis unit to adjust the level of detail of the analysis based on the importance of the collected feedback.

[0125] The feedback analysis unit applies different analysis methods depending on the category of the feedback during feedback analysis. For example, the feedback analysis unit applies a natural language processing analysis method to text feedback. The feedback analysis unit can also apply an image recognition analysis method to image feedback. The feedback analysis unit can also apply a speech recognition analysis method to speech feedback. This allows the feedback analysis unit to apply an analysis method appropriate to the category of the feedback. Some or all of the above processing in the feedback analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback analysis unit can input collected feedback data into a generative AI, and the generative AI can apply an appropriate analysis method according to the category of the data. This allows the feedback analysis unit to apply an analysis method appropriate to the category of the feedback.

[0126] The feedback analysis unit estimates the user's emotions and determines the priority of feedback analysis based on the estimated emotions. For example, if the user is stressed, the feedback analysis unit prioritizes analyzing important feedback. If the user is relaxed, the feedback analysis unit may also prioritize analyzing detailed feedback. If the user is in a hurry, the feedback analysis unit may also prioritize analyzing only the essential feedback. This allows for the determination of feedback analysis priorities 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback analysis unit can input user emotion data into a generative AI, which can then determine the priority of feedback analysis based on that data. This allows the feedback analysis unit to determine the priority of feedback analysis based on the user's emotions.

[0127] The feedback analysis unit determines the priority of analysis based on the submission date of the feedback during the feedback analysis process. For example, the feedback analysis unit prioritizes the analysis of the most recent feedback. The feedback analysis unit can also postpone the analysis of older feedback. Furthermore, the feedback analysis unit can adjust the analysis schedule based on the submission date. This allows the feedback analysis unit to determine the priority of analysis based on the submission date of the feedback. Some or all of the above-described processes in the feedback analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the feedback analysis unit can input the collected feedback data into a generating AI, which can then determine the priority of analysis based on the submission date of that data. This allows the feedback analysis unit to determine the priority of analysis based on the submission date of the feedback.

[0128] The feedback analysis unit adjusts the order of analysis based on the relevance of the feedback during feedback analysis. For example, the feedback analysis unit prioritizes analyzing highly relevant feedback. The feedback analysis unit can also postpone analyzing less relevant feedback. Furthermore, the feedback analysis unit can adjust the order of analysis based on the relevance of the feedback. This allows the order of analysis to be adjusted based on the relevance of the feedback. Some or all of the above processing in the feedback analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the feedback analysis unit can input the collected feedback data into a generating AI, and the generating AI can adjust the order of analysis based on the relevance of that data. This allows the feedback analysis unit to adjust the order of analysis based on the relevance of the feedback. === Hard Collateral 1-1 === Each of the multiple elements described above, including the input unit, collection unit, analysis unit, generation unit, and experience unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit allows the user to input their goals and dreams using the receiving device 38 of the smart device 14. The collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and collects the input information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a VR scenario based on the analysis results. The experience unit allows the user to experience the generated VR scenario using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the input unit, collection unit, analysis unit, generation unit, and experience unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit can input the user's goals and dreams using the microphone 238 of the smart glasses 214. The collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 and collects the input information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a VR scenario based on the analysis results. The experience unit allows the user to experience the generated VR scenario using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the input unit, collection unit, analysis unit, generation unit, and experience unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit allows the user to input their goals and dreams using the microphone 238 of the headset terminal 314. The collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and collects the input information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and generates a VR scenario based on the analysis results. The experience unit allows the user to experience the generated VR scenario using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the input unit, collection unit, analysis unit, generation unit, and experience unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the input unit can input the user's goals and dreams using the microphone 238 of the robot 414. The collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and collects the input information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and generates a VR scenario based on the analysis results. The experience unit allows the user to experience the generated VR scenario using the display of the robot 414.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The input section can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display goals and dreams that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest goals and dreams that the user might enter at specific times based on their past input history. This allows the system to suggest the optimal input method based on the user's past input history.

[0131] The analysis unit may also include a design unit that analyzes the collected information and designs VR scenarios based on the analysis results. The design unit determines the specific content and structure of the scenario based on the user's goals and dreams. For example, if the user's goal is a career goal, the design unit can design a scenario that simulates career success based on that goal. Also, if the user's dream is a personal dream, the design unit can design a scenario that simulates the realization of that dream based on that dream. This allows for the design of VR scenarios based on the analysis results.

[0132] The experience section can also include a feedback collection section to gather user feedback. The feedback collection section collects the user's impressions and opinions during the experience. For example, the feedback collection section can conduct a survey after the user completes the experience and collect the results. Furthermore, the feedback collection section can provide an interface that allows users to provide real-time feedback during the experience, thereby enabling the collection of user feedback.

[0133] The feedback collection unit may also include a feedback analysis unit that analyzes the collected feedback and incorporates it into the next experience. The feedback analysis unit analyzes the feedback data provided by the user and reflects the results in the next experience. For example, it can analyze the impressions and opinions that the user felt during the experience and use the results to improve the content of the next experience. In this way, the collected feedback can be reflected in the next experience.

[0134] The input unit can also estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is stressed, it can provide an interface with calming colors to reduce visual stress. Conversely, if the user is enjoying themselves, it can provide an interface with bright colors to make the input process more enjoyable. This allows for the design of an input interface that responds to the user's emotions.

[0135] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display goals and dreams that the user has frequently entered in the past as suggestions. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest goals and dreams that the user might enter at a specific time based on their past input history. This allows the system to suggest the optimal input method based on the user's past input history.

[0136] The input section can also guide the user's input based on their current life circumstances and areas of interest. For example, when a user enters their current life circumstances, it can suggest relevant goals and dreams. It can also present specific examples of goals and dreams based on the user's areas of interest. This allows the input content to be guided according to the user's life circumstances and areas of interest.

[0137] The input unit can also estimate the user's emotions and prioritize input content based on those emotions. For example, if the user is stressed, it will prioritize simple, quick input. Conversely, if the user is relaxed, it can prioritize more detailed input. This allows the system to prioritize input content according to the user's emotions.

[0138] The input section can also prioritize displaying highly relevant input content by considering the user's geographical location. For example, if the user lives in a specific region, it can suggest goals and dreams related to that region. Similarly, if the user is traveling, it can suggest goals and dreams related to their travel destination. This allows for the display of input content based on the user's geographical location.

[0139] The input section can also analyze the user's social media activity during input and suggest relevant input content. For example, it can suggest relevant input content based on goals and dreams the user has shared on social media. It can also identify areas of interest from the user's social media activity and suggest related goals and dreams. This allows the system to suggest input content based on the user's social media activity.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The input section is where users enter their goals and dreams. For example, when users enter their future goals and dreams, they can enter specific career goals, personal dreams, short-term goals, long-term goals, etc. Step 2: The collection unit collects the information entered by the input unit. For example, the collection unit collects data on goals and dreams entered by the user and sends it to the analysis unit. Step 3: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and applies algorithms to understand the user's future goals and dreams. Step 4: The generation unit generates a VR scenario based on the information analyzed by the analysis unit. For example, the generation unit generates a VR scenario that recreates the user's future goals and dreams based on the analysis results. Step 5: The experience unit experiences the VR scenario generated by the generation unit. For example, the experience unit allows the user to experience the generated VR scenario by putting on a VR headset. Furthermore, the experience unit can collect user feedback and incorporate it into the next experience. For example, the experience unit can collect the user's feelings and opinions during the experience as feedback and incorporate it into the next experience to provide a more satisfying experience for the user.

[0142] 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.

[0143] 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.

[0144] 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.

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

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In 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.

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

[0158] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0160] The data processing system 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.

[0161] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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."

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Explanation of Symbols]

[0214] 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. An input section where the user enters their goals and dreams, A collection unit that collects information input by the aforementioned input unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates a VR scenario based on the information analyzed by the analysis unit, The system includes an experience unit for experiencing VR scenarios generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, The company includes a design department that analyzes collected information and designs VR scenarios based on the analysis results. The system according to feature 1.

3. The aforementioned experience section is, It includes a feedback collection unit for collecting user feedback. The system according to feature 1.

4. The aforementioned feedback collection unit is It includes a feedback analysis unit that analyzes the collected feedback and incorporates it into the next experience. The system according to claim 3.

5. The aforementioned input unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

6. The aforementioned input unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

7. The aforementioned input unit is During input, the system guides the user based on their current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned input unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

9. The aforementioned input unit is When users input data, the system prioritizes displaying relevant input content by considering their geographical location. The system according to feature 1.

Images