system

Abstract

The system according to this embodiment aims to help users visualize their future lives concretely and reduce anxiety. [Solution] The system according to this embodiment comprises a learning unit, a simulation unit, and a provision unit. The learning unit learns the user's lifestyle, hobbies, and values. The simulation unit simulates a future life based on the information learned by the learning unit. The provision unit provides the future life simulated by the simulation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to have a specific image of future life and child-rearing, and there is a lack of means to reduce anxiety.

[0005] The system according to the embodiment aims to enable a user to specifically imagine future life and reduce anxiety.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a simulation unit, and a provision unit. The learning unit learns the user's lifestyle, hobbies, and values. The simulation unit simulates a future life based on the information learned by the learning unit. The provision unit provides the future life simulated by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to concretely imagine their future life and reduce anxiety. [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, etc. 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 future simulation system according to an embodiment of the present invention is a system that learns the lifestyle, hobbies, and values ​​of a user or couple, and based on that, visually and experientially simulates their future life. The future simulation system learns the lifestyle, hobbies, and values ​​of a user or couple, and based on that, visually and experientially simulates their future life. For example, the future simulation system learns the lifestyle, hobbies, and values ​​of a user or couple using a multimodal LLM. Next, based on the learned information, the future simulation system simulates the future if the user continues with a lifestyle of remaining unmarried or childless. In parallel, the future simulation system also simulates the future life if the user has children. This simulation includes specific scenes of child-rearing and the workings of support networks. This allows the user to have a concrete image of the future and reduce anxiety about their own life. This mechanism contributes to improving the aging society with a declining birthrate. Having a concrete image of the future increases positive attitudes towards marriage and child-rearing, contributing to measures against the declining birthrate. Thus, the future simulation system can learn the user's lifestyle, hobbies, and values, and based on that, simulate and provide their future life.

[0029] The future simulation system according to this embodiment comprises a learning unit, a simulation unit, and a provision unit. The learning unit learns the user's lifestyle, hobbies, and values. For example, the learning unit learns the user's daily behavior patterns. The learning unit can also learn the user's consumption behavior. The learning unit can also learn the user's health habits. For example, the learning unit collects user behavior data and analyzes it using a multimodal LLM. The simulation unit simulates future life based on the information learned by the learning unit. For example, the simulation unit simulates the future if the user continues a lifestyle of being single or childless. The simulation unit can also simulate future life if the user has children. The simulation unit can also simulate specific scenes of child-rearing and the workings of support networks. For example, the simulation unit uses a multimodal LLM to visually and experientially simulate future life. The provision unit provides the future life simulated by the simulation unit. For example, the provision unit visually displays the simulation results. The provision unit can also provide specific advice based on the simulation results. Furthermore, the provisioning unit can also provide feedback on the simulation results to the user. For example, the provisioning unit can display the simulation results as graphs or charts. It can also display the simulation results as animations. In addition, the provisioning unit can make suggestions for lifestyle improvements to the user based on the simulation results. Thus, the future simulation system according to the embodiment can learn the user's lifestyle, hobbies, and values, and simulate and provide a future life based on them. Some or all of the above-described processes in the learning unit, simulation unit, and provisioning unit may be performed using, for example, a multimodal LLM, or without using a multimodal LLM. For example, the learning unit can input the user's behavioral data into a multimodal LLM and have the multimodal LLM perform the learning of lifestyle, hobbies, and values.The simulation unit inputs information learned by the learning unit into the multimodal LLM and allows the multimodal LLM to run a simulation of future life. The provision unit inputs the future life simulated by the simulation unit into the multimodal LLM and allows the multimodal LLM to provide the simulation results.

[0030] The learning unit learns the user's lifestyle, hobbies, and values. Specifically, it utilizes location information, exercise data, and sleep data collected from smartphones and wearable devices to learn the user's daily behavior patterns. This data is used to understand the user's travel routes, activity levels, and sleep quality in detail. It also analyzes online shopping history, credit card usage history, and purchase history to learn the user's consumption behavior. This allows it to understand what kinds of products and services the user is interested in. Furthermore, to learn the user's health habits, it collects and analyzes food records, exercise frequency, and medical data. For example, it can understand what kind of diet the user eats, how much exercise they do, and whether they regularly visit medical institutions. The learning unit inputs this data into a multimodal LLM to comprehensively learn the user's lifestyle, hobbies, and values. Because the multimodal LLM can integrate and analyze data in different formats such as text data, image data, and audio data, it can understand various aspects of the user in detail. For example, the content of social media posts, photos taken, and audio recordings made by the user can also be analyzed. This allows the learning unit to analyze user behavior data from multiple perspectives and obtain more accurate learning results.

[0031] The simulation unit simulates future life based on information learned by the learning unit. Specifically, to simulate the future if the user continues a single or childless lifestyle, it considers fluctuations in income, changes in living expenses, and changes in health status. For example, it simulates predicted income if the user continues their current occupation and the possibility of future promotions or job changes. Regarding living expenses, it considers not only basic expenses such as rent, utilities, and food, but also expenses for leisure activities such as hobbies and travel. Furthermore, regarding health status, it simulates future health risks if current healthy habits are maintained and the effectiveness of preventive medicine. On the other hand, to simulate future life with children, it considers the costs and time involved in raising children, education expenses, and the division of roles within the family. For example, it simulates changes in education expenses as children grow, the choice of nursery school or school, and the division of childcare responsibilities within the family. It also considers the support systems of family, friends, and the local community to simulate specific childcare scenarios and the workings of support networks. The simulation unit inputs this information into a multimodal LLM to visually and experientially simulate future life. Multimodal LLM can integrate and analyze not only text and numerical data, but also multimedia data such as images, videos, and audio, enabling it to provide users with more realistic simulations of the future. For example, it can visually recreate future life using 3D graphics and VR technology, providing users with an experience as if they were actually there. As a result, the simulation unit can present users with concrete and realistic future scenarios and provide useful information for considering future options.

[0032] The service provider offers a simulated future lifestyle developed by the simulation department. Specifically, it uses graphs, charts, and animations to visually display the simulation results. For example, it displays income trends and fluctuations in living expenses as graphs so that users can understand them at a glance. It also displays changes in health status and the effects of preventive medicine as charts so that users can grasp future health risks. Furthermore, it can provide specific advice based on the simulation results. For example, it can propose career plans aimed at increasing future income, ways to save on living expenses, and measures to improve health habits. It can also provide advice on childcare, such as planning for education expenses, sharing childcare responsibilities, and how to utilize local communities. The service provider provides feedback on this advice to the user and supports them in creating a concrete action plan. For example, it makes suggestions for lifestyle improvements based on the simulation results and shows specific steps that the user should actually take. The service provider can also display the simulation results as animations. For example, it can recreate future lifestyle scenarios as animations and provide them in a way that is easy for users to understand visually. This allows users to visualize their future life more concretely. Furthermore, the service provider can also make suggestions for lifestyle improvements to the user based on the simulation results. For example, it suggests specific actions that users should actually take, such as improving their health habits, reviewing their consumption patterns, or reconsidering their career plans. This allows the service provider to offer users concrete and practical advice and support them in improving their future lives.

[0033] The simulation unit can simulate the future if one continues a single or childless lifestyle. For example, the simulation unit can simulate the future if one continues to live as a single person. The simulation unit can also simulate the future if one continues to live as a couple only. The simulation unit can also simulate the future if one builds a career while remaining single. For example, the simulation unit can simulate the economic impact of living as a single person. The simulation unit can also simulate the social impact of living as a couple only. The simulation unit can also simulate the quality of life if one builds a career while remaining single. This allows the simulation to simulate the future if one continues a single or childless lifestyle. Some or all of the above processing in the simulation unit may be performed using, for example, a multimodal LLM, or without using a multimodal LLM. For example, the simulation unit can input information about a single or childless lifestyle into a multimodal LLM and have the multimodal LLM perform a simulation of future life.

[0034] The simulation unit can simulate future life with children. For example, the simulation unit can simulate the burden of childcare. It can also simulate educational expenses. Furthermore, the simulation unit can simulate changes in life as children grow. For example, the simulation unit can simulate specific childcare scenarios. It can also simulate the operation of support networks. Furthermore, the simulation unit can simulate the quality of life as children grow. In this way, it is possible to simulate future life with children. Some or all of the above processing in the simulation unit may be performed using, for example, a multimodal LLM, or without a multimodal LLM. For example, the simulation unit can input information about life with children into a multimodal LLM and have the multimodal LLM execute a simulation of future life.

[0035] The service provider can visually display the simulation results. For example, the service provider can display the simulation results as a graph. It can also display the simulation results as a chart. Furthermore, the service provider can display the simulation results as an animation. For example, the service provider can display the simulation results as 3D graphics. The service provider can also display the simulation results in an interactive format. Furthermore, the service provider can update the simulation results in real time. This allows for a visual display of the simulation results. Some or all of the above processing in the service provider may be performed using, for example, a multimodal LLM, or without a multimodal LLM. For example, the service provider can input the simulation results into a multimodal LLM and have the multimodal LLM perform the visual display.

[0036] The service provider can provide specific advice based on the simulation results. For example, the service provider can make suggestions for improving lifestyle. The service provider can also provide investment advice. The service provider can also provide health management advice. For example, the service provider can propose a specific action plan to the user based on the simulation results. The service provider can also provide risk management advice to the user based on the simulation results. The service provider can also provide career planning advice to the user based on the simulation results. This allows the service provider to provide specific advice based on the simulation results. Some or all of the above processing in the service provider may be performed using, for example, a multimodal LLM, or not using a multimodal LLM. For example, the service provider can input the simulation results into a multimodal LLM and have the multimodal LLM perform the provision of specific advice.

[0037] The learning unit can analyze the user's past behavior history and select the optimal learning algorithm. For example, the learning unit prioritizes learning relevant information based on activities the user has frequently performed in the past. The learning unit can also predict activities to be performed during specific time periods based on the user's past behavior history and learn information appropriate for those time periods. Furthermore, the learning unit can analyze the user's past behavior history and select the optimal learning algorithm based on behavioral patterns. For example, the learning unit collects user behavior history data and analyzes it using a multimodal LLM. This improves the accuracy of learning by selecting the optimal learning algorithm based on the user's past behavior history. Some or all of the above processing in the learning unit may be performed using a multimodal LLM, or not. For example, the learning unit can input user behavior history data into a multimodal LLM and have the multimodal LLM perform the selection of the optimal learning algorithm.

[0038] The learning unit can filter information based on the user's current living situation and areas of interest during the learning process. For example, the learning unit can prioritize learning relevant information based on the user's current living situation. It can also filter and learn relevant information based on the user's areas of interest. Furthermore, the learning unit can select and learn the most relevant information considering the user's current living situation and areas of interest. For example, the learning unit can collect user living situation data and analyze it using a multimodal LLM. This allows it to learn more relevant information by filtering it based on the user's current living situation and areas of interest. Some or all of the above processing in the learning unit may be performed using a multimodal LLM, or not. For example, the learning unit can input user living situation data into a multimodal LLM and have the multimodal LLM perform the information filtering.

[0039] The learning unit can prioritize learning highly relevant information by considering the user's geographical location during the learning process. For example, the learning unit can prioritize learning relevant information based on the user's current location. The learning unit can also select and learn the most relevant information by considering the user's geographical location. Furthermore, the learning unit can prioritize learning region-specific information based on the user's geographical location. For example, the learning unit can collect user location data and analyze it using a multimodal LLM. This allows the learning unit to provide more appropriate information by learning highly relevant information based on the user's geographical location. Some or all of the above processing in the learning unit may be performed using a multimodal LLM, or not. For example, the learning unit can input user location data into a multimodal LLM and have the multimodal LLM perform the learning of highly relevant information.

[0040] The learning unit can analyze the user's social media activity and learn relevant information during the learning process. For example, the learning unit can extract topics of interest from the user's social media activity and learn relevant information. The learning unit can also analyze the user's statements and posts on social media, select the most relevant information, and learn it. Furthermore, the learning unit can prioritize learning relevant information based on the user's social media activity history. For example, the learning unit can collect the user's social media data and analyze it using a multimodal LLM. This allows it to provide more appropriate information by learning relevant information based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using a multimodal LLM, or not. For example, the learning unit can input the user's social media data into a multimodal LLM and have the multimodal LLM perform the learning of relevant information.

[0041] The simulation unit can adjust the level of detail of the simulation based on the importance of lifestyles during the simulation. For example, the simulation unit can perform a detailed simulation based on lifestyles that the user considers important. The simulation unit can also perform a simplified simulation based on lifestyles that the user considers less important. Furthermore, the simulation unit can adjust the level of detail of the simulation according to the importance of the user's lifestyle. For example, the simulation unit can collect user lifestyle data and analyze it using a multimodal LLM. This allows for a more appropriate simulation by adjusting the level of detail of the simulation based on the importance of lifestyles. Some or all of the above processing in the simulation unit may be performed using a multimodal LLM, or not. For example, the simulation unit can input user lifestyle data into a multimodal LLM and have the multimodal LLM perform the adjustment of the level of detail of the simulation.

[0042] The simulation unit can apply different simulation algorithms depending on the lifestyle category during the simulation. For example, the simulation unit applies the optimal simulation algorithm based on the lifestyle category selected by the user. The simulation unit can also select different simulation algorithms depending on the user's lifestyle category. Furthermore, the simulation unit can adjust the simulation algorithm based on the user's lifestyle category. For example, the simulation unit collects the user's lifestyle category data and analyzes it using a multimodal LLM. This allows for the application of different simulation algorithms depending on the lifestyle category, thereby providing a more appropriate simulation. Some or all of the above processing in the simulation unit may be performed using a multimodal LLM, or not. For example, the simulation unit can input the user's lifestyle category data into a multimodal LLM and have the multimodal LLM execute the application of the simulation algorithm.

[0043] The simulation unit can determine the priority of simulations based on the timing of lifestyle changes during the simulation. For example, the simulation unit can determine the priority of simulations based on when the user experiences a lifestyle change. The simulation unit can also adjust the order of simulations according to the timing of the user's lifestyle changes. Furthermore, the simulation unit can set the priority of simulations considering the timing of the user's lifestyle changes. For example, the simulation unit collects data on the timing of the user's lifestyle changes and analyzes it using a multimodal LLM. This allows for the provision of more appropriate simulations by determining the priority of simulations based on the timing of lifestyle changes. Some or all of the above processing in the simulation unit may be performed using a multimodal LLM, or not. For example, the simulation unit can input data on the timing of the user's lifestyle changes into a multimodal LLM and have the multimodal LLM perform the determination of the simulation priority.

[0044] The simulation unit can adjust the order of simulations based on lifestyle relevance during the simulation. For example, the simulation unit adjusts the order of simulations based on the user's lifestyle relevance. The simulation unit can also determine the order of simulations based on the lifestyles the user is interested in. Furthermore, the simulation unit can set the order of simulations considering the user's lifestyle relevance. For example, the simulation unit collects the user's lifestyle relevance data and analyzes it using a multimodal LLM. This allows for more appropriate simulations by adjusting the order of simulations based on lifestyle relevance. Some or all of the above processing in the simulation unit may be performed using a multimodal LLM, or not. For example, the simulation unit can input the user's lifestyle relevance data into a multimodal LLM and have the multimodal LLM perform the adjustment of the simulation order.

[0045] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can select the optimal display method based on the display methods the user has used in the past. The service provider can also select a display method with high visibility from the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and propose the optimal display method. For example, the service provider can collect user operation history data and analyze it using a multimodal LLM. This allows for the provision of a more appropriate display by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using a multimodal LLM, or not. For example, the service provider can input user operation history data into a multimodal LLM and have the multimodal LLM perform the selection of the optimal display method.

[0046] The service provider can customize the displayed simulation results based on the user's current living situation at the time of delivery. For example, the service provider can display relevant information based on the user's current living situation. The service provider can also select the most appropriate display content considering the user's current living situation. Furthermore, the service provider can customize the displayed simulation results according to the user's current living situation. For example, the service provider can collect user living situation data and analyze it using a multimodal LLM. This allows for a more appropriate display by customizing the displayed simulation results based on the user's current living situation. Some or all of the above processing in the service provider may be performed using a multimodal LLM, or not. For example, the service provider can input user living situation data into a multimodal LLM and have the multimodal LLM perform the customization of the displayed content.

[0047] The service provider can select the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can display relevant information based on the user's current location. The service provider can also select the optimal display method considering the user's geographical location information. Furthermore, the service provider can display region-specific information based on the user's geographical location information. For example, the service provider can collect user location data and analyze it using a multimodal LLM. This allows for the selection of the optimal display method based on the user's geographical location information, thereby providing a more appropriate display. Some or all of the above processing in the service provider may be performed using a multimodal LLM, or without using a multimodal LLM. For example, the service provider can input user location data into a multimodal LLM and have the multimodal LLM select the optimal display method.

[0048] The service provider can analyze the user's social media activity and adjust the displayed simulation results at the time of delivery. For example, the service provider can extract topics of interest from the user's social media activity and display the relevant simulation results. The service provider can also analyze the user's statements and posts on social media and select the most appropriate simulation results. Furthermore, the service provider can prioritize the display of relevant simulation results based on the user's social media activity history. For example, the service provider can collect the user's social media data and analyze it using a multimodal LLM. This allows for a more appropriate display by adjusting the displayed simulation results based on the user's social media activity. Some or all of the above processing in the service provider may be performed using a multimodal LLM, or not. For example, the service provider can input the user's social media data into a multimodal LLM and have the multimodal LLM perform the adjustment of the displayed content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The future simulation system can further acquire user health data and simulate future life based on their health status. For example, the learning unit collects user health data and analyzes their health status. The simulation unit can then simulate future quality of life and health risks based on that health status. The provision unit can also provide health management advice based on the simulation results. This allows users to concretely visualize their future life considering their own health status, thereby increasing their awareness of health management.

[0051] The future simulation system can further acquire the user's economic data and simulate future life based on their economic situation. For example, the learning unit collects the user's income and expenditure data and analyzes their economic situation. The simulation unit can then simulate future asset status and quality of life based on that economic situation. The provision unit can also provide investment and saving advice based on the simulation results. This allows users to concretely visualize their future life considering their own economic situation, thereby increasing their awareness of financial management.

[0052] The future simulation system can further acquire the user's social network data and simulate future life based on social relationships. For example, the learning unit collects data on the user's relationships with friends and family and analyzes their social network. The simulation unit can then simulate future relationships and social support situations based on this social network. The provision unit can also provide advice on improving and maintaining social relationships based on the simulation results. This allows users to concretely visualize their future life considering their social relationships, thereby increasing their awareness of social connections.

[0053] The future simulation system can further acquire user environmental data and simulate future life based on environmental factors. For example, the learning unit collects the user's living environment and climate data and analyzes environmental factors. The simulation unit can then simulate future living environments and the impact of climate change based on these environmental factors. The provision unit can also provide advice on environmental improvement and adaptation based on the simulation results. This allows users to concretely visualize their future life considering their own environmental factors, thereby increasing their environmental awareness.

[0054] The future simulation system can further simulate the level of fulfillment in a user's future life based on their hobbies and interests. For example, the learning unit collects and analyzes data related to the user's hobbies and interests. The simulation unit can then simulate the level of fulfillment and satisfaction in a user's future life based on those hobbies and interests. The provision unit can also provide suggestions for a lifestyle that leverages the user's hobbies and interests based on the simulation results. This allows users to concretely imagine a future life that takes their hobbies and interests into account, thereby increasing their sense of fulfillment in life.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The learning unit learns the user's lifestyle, hobbies, and values. For example, it can learn the user's daily behavior patterns, consumption behavior, and health habits. The learning unit collects user behavior data and analyzes it using a multimodal LLM. Step 2: The simulation unit simulates future life based on the information learned by the learning unit. For example, it can simulate future life if one continues a single or childless lifestyle, or if one has children. The simulation unit uses a multimodal LLM to visually and experientially simulate future life. Step 3: The service provider delivers the future life simulated by the simulation service provider. For example, it can visually display the simulation results and provide specific advice. It can also display the simulation results as graphs, charts, or animations and offer suggestions for improving the user's life.

[0057] (Example of form 2) The future simulation system according to an embodiment of the present invention is a system that learns the lifestyle, hobbies, and values ​​of a user or couple, and based on that, visually and experientially simulates their future life. The future simulation system learns the lifestyle, hobbies, and values ​​of a user or couple, and based on that, visually and experientially simulates their future life. For example, the future simulation system learns the lifestyle, hobbies, and values ​​of a user or couple using a multimodal LLM. Next, based on the learned information, the future simulation system simulates the future if the user continues with a lifestyle of remaining unmarried or childless. In parallel, the future simulation system also simulates the future life if the user has children. This simulation includes specific scenes of child-rearing and the workings of support networks. This allows the user to have a concrete image of the future and reduce anxiety about their own life. This mechanism contributes to improving the aging society with a declining birthrate. Having a concrete image of the future increases positive attitudes towards marriage and child-rearing, contributing to measures against the declining birthrate. Thus, the future simulation system can learn the user's lifestyle, hobbies, and values, and based on that, simulate and provide their future life.

[0058] The future simulation system according to this embodiment comprises a learning unit, a simulation unit, and a provision unit. The learning unit learns the user's lifestyle, hobbies, and values. For example, the learning unit learns the user's daily behavior patterns. The learning unit can also learn the user's consumption behavior. The learning unit can also learn the user's health habits. For example, the learning unit collects user behavior data and analyzes it using a multimodal LLM. The simulation unit simulates future life based on the information learned by the learning unit. For example, the simulation unit simulates the future if the user continues a lifestyle of being single or childless. The simulation unit can also simulate future life if the user has children. The simulation unit can also simulate specific scenes of child-rearing and the workings of support networks. For example, the simulation unit uses a multimodal LLM to visually and experientially simulate future life. The provision unit provides the future life simulated by the simulation unit. For example, the provision unit visually displays the simulation results. The provision unit can also provide specific advice based on the simulation results. Furthermore, the provisioning unit can also provide feedback on the simulation results to the user. For example, the provisioning unit can display the simulation results as graphs or charts. It can also display the simulation results as animations. In addition, the provisioning unit can make suggestions for lifestyle improvements to the user based on the simulation results. Thus, the future simulation system according to the embodiment can learn the user's lifestyle, hobbies, and values, and simulate and provide a future life based on them. Some or all of the above-described processes in the learning unit, simulation unit, and provisioning unit may be performed using, for example, a multimodal LLM, or without using a multimodal LLM. For example, the learning unit can input the user's behavioral data into a multimodal LLM and have the multimodal LLM perform the learning of lifestyle, hobbies, and values.The simulation unit inputs information learned by the learning unit into the multimodal LLM and allows the multimodal LLM to run a simulation of future life. The provision unit inputs the future life simulated by the simulation unit into the multimodal LLM and allows the multimodal LLM to provide the simulation results.

[0059] The learning unit learns the user's lifestyle, hobbies, and values. Specifically, it utilizes location information, exercise data, and sleep data collected from smartphones and wearable devices to learn the user's daily behavior patterns. This data is used to understand the user's travel routes, activity levels, and sleep quality in detail. It also analyzes online shopping history, credit card usage history, and purchase history to learn the user's consumption behavior. This allows it to understand what kinds of products and services the user is interested in. Furthermore, to learn the user's health habits, it collects and analyzes food records, exercise frequency, and medical data. For example, it can understand what kind of diet the user eats, how much exercise they do, and whether they regularly visit medical institutions. The learning unit inputs this data into a multimodal LLM to comprehensively learn the user's lifestyle, hobbies, and values. Because the multimodal LLM can integrate and analyze data in different formats such as text data, image data, and audio data, it can understand various aspects of the user in detail. For example, the content of social media posts, photos taken, and audio recordings made by the user can also be analyzed. This allows the learning unit to analyze user behavior data from multiple perspectives and obtain more accurate learning results.

[0060] The simulation unit simulates future life based on information learned by the learning unit. Specifically, to simulate the future if the user continues a single or childless lifestyle, it considers fluctuations in income, changes in living expenses, and changes in health status. For example, it simulates predicted income if the user continues their current occupation and the possibility of future promotions or job changes. Regarding living expenses, it considers not only basic expenses such as rent, utilities, and food, but also expenses for leisure activities such as hobbies and travel. Furthermore, regarding health status, it simulates future health risks if current healthy habits are maintained and the effectiveness of preventive medicine. On the other hand, to simulate future life with children, it considers the costs and time involved in raising children, education expenses, and the division of roles within the family. For example, it simulates changes in education expenses as children grow, the choice of nursery school or school, and the division of childcare responsibilities within the family. It also considers the support systems of family, friends, and the local community to simulate specific childcare scenarios and the workings of support networks. The simulation unit inputs this information into a multimodal LLM to visually and experientially simulate future life. Multimodal LLM can integrate and analyze not only text and numerical data, but also multimedia data such as images, videos, and audio, enabling it to provide users with more realistic simulations of the future. For example, it can visually recreate future life using 3D graphics and VR technology, providing users with an experience as if they were actually there. As a result, the simulation unit can present users with concrete and realistic future scenarios and provide useful information for considering future options.

[0061] The service provider offers a simulated future lifestyle developed by the simulation department. Specifically, it uses graphs, charts, and animations to visually display the simulation results. For example, it displays income trends and fluctuations in living expenses as graphs so that users can understand them at a glance. It also displays changes in health status and the effects of preventive medicine as charts so that users can grasp future health risks. Furthermore, it can provide specific advice based on the simulation results. For example, it can propose career plans aimed at increasing future income, ways to save on living expenses, and measures to improve health habits. It can also provide advice on childcare, such as planning for education expenses, sharing childcare responsibilities, and how to utilize local communities. The service provider provides feedback on this advice to the user and supports them in creating a concrete action plan. For example, it makes suggestions for lifestyle improvements based on the simulation results and shows specific steps that the user should actually take. The service provider can also display the simulation results as animations. For example, it can recreate future lifestyle scenarios as animations and provide them in a way that is easy for users to understand visually. This allows users to visualize their future life more concretely. Furthermore, the service provider can also make suggestions for lifestyle improvements to the user based on the simulation results. For example, it suggests specific actions that users should actually take, such as improving their health habits, reviewing their consumption patterns, or reconsidering their career plans. This allows the service provider to offer users concrete and practical advice and support them in improving their future lives.

[0062] The simulation unit can simulate the future if one continues a single or childless lifestyle. For example, the simulation unit can simulate the future if one continues to live as a single person. The simulation unit can also simulate the future if one continues to live as a couple only. The simulation unit can also simulate the future if one builds a career while remaining single. For example, the simulation unit can simulate the economic impact of living as a single person. The simulation unit can also simulate the social impact of living as a couple only. The simulation unit can also simulate the quality of life if one builds a career while remaining single. This allows the simulation to simulate the future if one continues a single or childless lifestyle. Some or all of the above processing in the simulation unit may be performed using, for example, a multimodal LLM, or without using a multimodal LLM. For example, the simulation unit can input information about a single or childless lifestyle into a multimodal LLM and have the multimodal LLM perform a simulation of future life.

[0063] The simulation unit can simulate future life with children. For example, the simulation unit can simulate the burden of childcare. It can also simulate educational expenses. Furthermore, the simulation unit can simulate changes in life as children grow. For example, the simulation unit can simulate specific childcare scenarios. It can also simulate the operation of support networks. Furthermore, the simulation unit can simulate the quality of life as children grow. In this way, it is possible to simulate future life with children. Some or all of the above processing in the simulation unit may be performed using, for example, a multimodal LLM, or without a multimodal LLM. For example, the simulation unit can input information about life with children into a multimodal LLM and have the multimodal LLM execute a simulation of future life.

[0064] The service provider can visually display the simulation results. For example, the service provider can display the simulation results as a graph. It can also display the simulation results as a chart. Furthermore, the service provider can display the simulation results as an animation. For example, the service provider can display the simulation results as 3D graphics. The service provider can also display the simulation results in an interactive format. Furthermore, the service provider can update the simulation results in real time. This allows for a visual display of the simulation results. Some or all of the above processing in the service provider may be performed using, for example, a multimodal LLM, or without a multimodal LLM. For example, the service provider can input the simulation results into a multimodal LLM and have the multimodal LLM perform the visual display.

[0065] The service provider can provide specific advice based on the simulation results. For example, the service provider can make suggestions for improving lifestyle. The service provider can also provide investment advice. The service provider can also provide health management advice. For example, the service provider can propose a specific action plan to the user based on the simulation results. The service provider can also provide risk management advice to the user based on the simulation results. The service provider can also provide career planning advice to the user based on the simulation results. This allows the service provider to provide specific advice based on the simulation results. Some or all of the above processing in the service provider may be performed using, for example, a multimodal LLM, or not using a multimodal LLM. For example, the service provider can input the simulation results into a multimodal LLM and have the multimodal LLM perform the provision of specific advice.

[0066] The learning unit can estimate the user's emotions and adjust its learning methods for lifestyle, hobbies, and values ​​based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize learning information about relaxing hobbies and lifestyles. Similarly, if the user is excited, the learning unit can prioritize learning information about active hobbies and lifestyles. Furthermore, if the user is depressed, the learning unit can prioritize learning information about mood-boosting hobbies and lifestyles. For example, the learning unit collects user emotion data and estimates emotions using an emotion engine or generative AI. This allows it to learn more appropriate information by adjusting its learning methods based on 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 processing in the learning unit may be performed using, for example, a multimodal LLM, or without a multimodal LLM. For example, the learning unit can input user emotion data into a multimodal LLM and have the multimodal LLM perform adjustments to the learning method based on emotion.

[0067] The learning unit can analyze the user's past behavior history and select the optimal learning algorithm. For example, the learning unit prioritizes learning relevant information based on activities the user has frequently performed in the past. The learning unit can also predict activities to be performed during specific time periods based on the user's past behavior history and learn information appropriate for those time periods. Furthermore, the learning unit can analyze the user's past behavior history and select the optimal learning algorithm based on behavioral patterns. For example, the learning unit collects user behavior history data and analyzes it using a multimodal LLM. This improves the accuracy of learning by selecting the optimal learning algorithm based on the user's past behavior history. Some or all of the above processing in the learning unit may be performed using a multimodal LLM, or not. For example, the learning unit can input user behavior history data into a multimodal LLM and have the multimodal LLM perform the selection of the optimal learning algorithm.

[0068] The learning unit can filter information based on the user's current living situation and areas of interest during the learning process. For example, the learning unit can prioritize learning relevant information based on the user's current living situation. It can also filter and learn relevant information based on the user's areas of interest. Furthermore, the learning unit can select and learn the most relevant information considering the user's current living situation and areas of interest. For example, the learning unit can collect user living situation data and analyze it using a multimodal LLM. This allows it to learn more relevant information by filtering it based on the user's current living situation and areas of interest. Some or all of the above processing in the learning unit may be performed using a multimodal LLM, or not. For example, the learning unit can input user living situation data into a multimodal LLM and have the multimodal LLM perform the information filtering.

[0069] The learning unit can estimate the user's emotions and determine the priority of information to learn based on the estimated user emotions. For example, if the user is stressed, the learning unit will prioritize learning information that helps them relax. It can also prioritize learning active information if the user is excited. Furthermore, if the user is depressed, it can prioritize learning information that helps them feel better. For example, the learning unit collects user emotion data and estimates emotions using an emotion engine or generative AI. This allows it to learn more appropriate information by prioritizing it based on 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 learning unit may be performed using, for example, a multimodal LLM, or not. For example, the learning unit can input user emotion data into a multimodal LLM and have the multimodal LLM perform the emotion-based information prioritization.

[0070] The learning unit can prioritize learning highly relevant information by considering the user's geographical location during the learning process. For example, the learning unit can prioritize learning relevant information based on the user's current location. The learning unit can also select and learn the most relevant information by considering the user's geographical location. Furthermore, the learning unit can prioritize learning region-specific information based on the user's geographical location. For example, the learning unit can collect user location data and analyze it using a multimodal LLM. This allows the learning unit to provide more appropriate information by learning highly relevant information based on the user's geographical location. Some or all of the above processing in the learning unit may be performed using a multimodal LLM, or not. For example, the learning unit can input user location data into a multimodal LLM and have the multimodal LLM perform the learning of highly relevant information.

[0071] The learning unit can analyze the user's social media activity and learn relevant information during the learning process. For example, the learning unit can extract topics of interest from the user's social media activity and learn relevant information. The learning unit can also analyze the user's statements and posts on social media, select the most relevant information, and learn it. Furthermore, the learning unit can prioritize learning relevant information based on the user's social media activity history. For example, the learning unit can collect the user's social media data and analyze it using a multimodal LLM. This allows it to provide more appropriate information by learning relevant information based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using a multimodal LLM, or not. For example, the learning unit can input the user's social media data into a multimodal LLM and have the multimodal LLM perform the learning of relevant information.

[0072] The simulation unit can estimate the user's emotions and adjust the simulation's presentation based on the estimated emotions. For example, if the user is relaxed, the simulation unit will perform the simulation using a calm presentation. If the user is excited, the simulation unit can also perform the simulation using a visually stimulating presentation. If the user is stressed, the simulation unit can also perform the simulation using a calm presentation. For example, the simulation unit collects user emotion data and estimates emotions using an emotion engine or generative AI. This allows for a more appropriate simulation by adjusting the simulation's presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 simulation unit may be performed using, for example, a multimodal LLM, or not. For example, the simulation unit can input user emotion data into a multimodal LLM and have the multimodal LLM perform the adjustment of the presentation based on the emotions.

[0073] The simulation unit can adjust the level of detail of the simulation based on the importance of lifestyles during the simulation. For example, the simulation unit can perform a detailed simulation based on lifestyles that the user considers important. The simulation unit can also perform a simplified simulation based on lifestyles that the user considers less important. Furthermore, the simulation unit can adjust the level of detail of the simulation according to the importance of the user's lifestyle. For example, the simulation unit can collect user lifestyle data and analyze it using a multimodal LLM. This allows for a more appropriate simulation by adjusting the level of detail of the simulation based on the importance of lifestyles. Some or all of the above processing in the simulation unit may be performed using a multimodal LLM, or not. For example, the simulation unit can input user lifestyle data into a multimodal LLM and have the multimodal LLM perform the adjustment of the level of detail of the simulation.

[0074] The simulation unit can apply different simulation algorithms depending on the lifestyle category during the simulation. For example, the simulation unit applies the optimal simulation algorithm based on the lifestyle category selected by the user. The simulation unit can also select different simulation algorithms depending on the user's lifestyle category. Furthermore, the simulation unit can adjust the simulation algorithm based on the user's lifestyle category. For example, the simulation unit collects the user's lifestyle category data and analyzes it using a multimodal LLM. This allows for the application of different simulation algorithms depending on the lifestyle category, thereby providing a more appropriate simulation. Some or all of the above processing in the simulation unit may be performed using a multimodal LLM, or not. For example, the simulation unit can input the user's lifestyle category data into a multimodal LLM and have the multimodal LLM execute the application of the simulation algorithm.

[0075] The simulation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated emotions. For example, if the user is in a hurry, the simulation unit can provide a short simulation. It can also provide a detailed simulation if the user is relaxed. Furthermore, if the user is excited, the simulation unit can provide a visually stimulating simulation. For example, the simulation unit collects user emotion data and estimates emotions using an emotion engine or generative AI. This allows for a more appropriate simulation by adjusting the length of the simulation based on 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 processing in the simulation unit may be performed using, for example, a multimodal LLM, or not. For example, the simulation unit can input user emotion data into a multimodal LLM and have the multimodal LLM perform the emotion-based adjustment of the simulation length.

[0076] The simulation unit can determine the priority of simulations based on the timing of lifestyle changes during the simulation. For example, the simulation unit can determine the priority of simulations based on when the user experiences a lifestyle change. The simulation unit can also adjust the order of simulations according to the timing of the user's lifestyle changes. Furthermore, the simulation unit can set the priority of simulations considering the timing of the user's lifestyle changes. For example, the simulation unit collects data on the timing of the user's lifestyle changes and analyzes it using a multimodal LLM. This allows for the provision of more appropriate simulations by determining the priority of simulations based on the timing of lifestyle changes. Some or all of the above processing in the simulation unit may be performed using a multimodal LLM, or not. For example, the simulation unit can input data on the timing of the user's lifestyle changes into a multimodal LLM and have the multimodal LLM perform the determination of the simulation priority.

[0077] The simulation unit can adjust the order of simulations based on lifestyle relevance during the simulation. For example, the simulation unit adjusts the order of simulations based on the user's lifestyle relevance. The simulation unit can also determine the order of simulations based on the lifestyles the user is interested in. Furthermore, the simulation unit can set the order of simulations considering the user's lifestyle relevance. For example, the simulation unit collects the user's lifestyle relevance data and analyzes it using a multimodal LLM. This allows for more appropriate simulations by adjusting the order of simulations based on lifestyle relevance. Some or all of the above processing in the simulation unit may be performed using a multimodal LLM, or not. For example, the simulation unit can input the user's lifestyle relevance data into a multimodal LLM and have the multimodal LLM perform the adjustment of the simulation order.

[0078] The service provider can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. For example, the service provider can collect user emotion data and estimate emotions using an emotion engine or generative AI. This allows for a more appropriate display by adjusting the display method of the simulation results based on 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 service provider may be performed using, for example, a multimodal LLM, or not using a multimodal LLM. For example, the service provider can input user emotion data into a multimodal LLM and have the multimodal LLM perform the adjustment of the display method based on emotions.

[0079] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can select the optimal display method based on the display methods the user has used in the past. The service provider can also select a display method with high visibility from the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and propose the optimal display method. For example, the service provider can collect user operation history data and analyze it using a multimodal LLM. This allows for the provision of a more appropriate display by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using a multimodal LLM, or not. For example, the service provider can input user operation history data into a multimodal LLM and have the multimodal LLM perform the selection of the optimal display method.

[0080] The service provider can customize the displayed simulation results based on the user's current living situation at the time of delivery. For example, the service provider can display relevant information based on the user's current living situation. The service provider can also select the most appropriate display content considering the user's current living situation. Furthermore, the service provider can customize the displayed simulation results according to the user's current living situation. For example, the service provider can collect user living situation data and analyze it using a multimodal LLM. This allows for a more appropriate display by customizing the displayed simulation results based on the user's current living situation. Some or all of the above processing in the service provider may be performed using a multimodal LLM, or not. For example, the service provider can input user living situation data into a multimodal LLM and have the multimodal LLM perform the customization of the displayed content.

[0081] The service provider can estimate the user's emotions and prioritize simulation results based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize displaying relaxing simulation results. It can also prioritize displaying active simulation results if the user is excited, or prioritize displaying mood-enhancing simulation results if the user is depressed. For example, the service provider can collect user emotion data and estimate emotions using an emotion engine or generative AI. This allows for more appropriate displays by prioritizing simulation results based on 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 service provider may be performed using, for example, a multimodal LLM, or not. For example, the service provider can input user emotion data into a multimodal LLM and have the multimodal LLM perform the emotion-based priority determination.

[0082] The service provider can select the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can display relevant information based on the user's current location. The service provider can also select the optimal display method considering the user's geographical location information. Furthermore, the service provider can display region-specific information based on the user's geographical location information. For example, the service provider can collect user location data and analyze it using a multimodal LLM. This allows for the selection of the optimal display method based on the user's geographical location information, thereby providing a more appropriate display. Some or all of the above processing in the service provider may be performed using a multimodal LLM, or without using a multimodal LLM. For example, the service provider can input user location data into a multimodal LLM and have the multimodal LLM select the optimal display method.

[0083] The service provider can analyze the user's social media activity and adjust the displayed simulation results at the time of delivery. For example, the service provider can extract topics of interest from the user's social media activity and display the relevant simulation results. The service provider can also analyze the user's statements and posts on social media and select the most appropriate simulation results. Furthermore, the service provider can prioritize the display of relevant simulation results based on the user's social media activity history. For example, the service provider can collect the user's social media data and analyze it using a multimodal LLM. This allows for a more appropriate display by adjusting the displayed simulation results based on the user's social media activity. Some or all of the above processing in the service provider may be performed using a multimodal LLM, or not. For example, the service provider can input the user's social media data into a multimodal LLM and have the multimodal LLM perform the adjustment of the displayed content.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The future simulation system can further acquire user health data and simulate future life based on their health status. For example, the learning unit collects user health data and analyzes their health status. The simulation unit can then simulate future quality of life and health risks based on that health status. The provision unit can also provide health management advice based on the simulation results. This allows users to concretely visualize their future life considering their own health status, thereby increasing their awareness of health management.

[0086] The future simulation system can further acquire the user's economic data and simulate future life based on their economic situation. For example, the learning unit collects the user's income and expenditure data and analyzes their economic situation. The simulation unit can then simulate future asset status and quality of life based on that economic situation. The provision unit can also provide investment and saving advice based on the simulation results. This allows users to concretely visualize their future life considering their own economic situation, thereby increasing their awareness of financial management.

[0087] The future simulation system can further acquire the user's social network data and simulate future life based on social relationships. For example, the learning unit collects data on the user's relationships with friends and family and analyzes their social network. The simulation unit can then simulate future relationships and social support situations based on this social network. The provision unit can also provide advice on improving and maintaining social relationships based on the simulation results. This allows users to concretely visualize their future life considering their social relationships, thereby increasing their awareness of social connections.

[0088] The future simulation system can further acquire user environmental data and simulate future life based on environmental factors. For example, the learning unit collects the user's living environment and climate data and analyzes environmental factors. The simulation unit can then simulate future living environments and the impact of climate change based on these environmental factors. The provision unit can also provide advice on environmental improvement and adaptation based on the simulation results. This allows users to concretely visualize their future life considering their own environmental factors, thereby increasing their environmental awareness.

[0089] The future simulation system can further simulate the level of fulfillment in a user's future life based on their hobbies and interests. For example, the learning unit collects and analyzes data related to the user's hobbies and interests. The simulation unit can then simulate the level of fulfillment and satisfaction in a user's future life based on those hobbies and interests. The provision unit can also provide suggestions for a lifestyle that leverages the user's hobbies and interests based on the simulation results. This allows users to concretely imagine a future life that takes their hobbies and interests into account, thereby increasing their sense of fulfillment in life.

[0090] The future simulation system can further estimate the user's emotions and adjust the simulation scenario based on those emotions. For example, if the user is feeling stressed, the simulation unit will prioritize simulating relaxing scenarios. Conversely, if the user is excited, the simulation unit can prioritize simulating active scenarios. The service unit can also provide emotionally appropriate advice based on the simulation results. This allows the user to concretely visualize a future life that aligns with their emotions, thereby promoting emotional stability.

[0091] The future simulation system can further estimate the user's emotions and adjust the timing of the simulation based on those emotions. For example, the simulation unit can provide a detailed simulation if the user is relaxed. Alternatively, if the user is in a hurry, the simulation unit can provide a concise simulation that gets straight to the point. The service provider can also offer emotionally appropriate advice based on the simulation results. This allows the user to visualize their future life concretely at a time that suits their emotions, enhancing the effectiveness of the simulation.

[0092] The future simulation system can further estimate the user's emotions and customize the simulation content based on those emotions. For example, if the user is feeling down, the simulation unit will prioritize simulating positive future scenarios. Conversely, if the user is excited, the simulation unit can prioritize simulating challenging future scenarios. The service unit can also provide emotionally appropriate advice based on the simulation results. This allows the user to concretely visualize their future life in line with their emotions, promoting emotional stability.

[0093] The future simulation system can further estimate the user's emotions and adjust the simulation's feedback based on those estimated emotions. For example, if the user is feeling stressed, the system will provide relaxing feedback. Conversely, if the user is feeling excited, the system can provide active feedback. This allows the user to receive feedback tailored to their emotions, promoting emotional stability.

[0094] The future simulation system can further estimate the user's emotions and personalize the simulation results based on those emotions. For example, if the user is relaxed, the system can provide detailed simulation results. Conversely, if the user is in a hurry, the system can provide concise simulation results. This allows the user to receive simulation results that match their emotions, thereby enhancing the effectiveness of the simulation.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The learning unit learns the user's lifestyle, hobbies, and values. For example, it can learn the user's daily behavior patterns, consumption behavior, and health habits. The learning unit collects user behavior data and analyzes it using a multimodal LLM. Step 2: The simulation unit simulates future life based on the information learned by the learning unit. For example, it can simulate future life if one continues a single or childless lifestyle, or if one has children. The simulation unit uses a multimodal LLM to visually and experientially simulate future life. Step 3: The service provider delivers the future life simulated by the simulation service provider. For example, it can visually display the simulation results and provide specific advice. It can also display the simulation results as graphs, charts, or animations and offer suggestions for improving the user's life.

[0097] 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.

[0098] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] 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.

[0100] Each of the multiple elements described above, including the learning unit, simulation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit can collect user behavior data using the camera 42 and microphone 38B of the smart device 14 and analyze it using the control unit 46A. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates future life based on the information learned by the learning unit. The provision unit can visually display the simulation results using the display 40A and speaker 40B of the smart device 14 and provide feedback to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the learning unit, simulation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit can collect user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and analyze it using the control unit 46A. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates future life based on the information learned by the learning unit. The provision unit can visually display the simulation results using the display and speaker 240 of the smart glasses 214 and provide feedback to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the learning unit, simulation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit can collect user behavior data using the camera 42 and microphone 238 of the headset terminal 314 and analyze it using the control unit 46A. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates future life based on the information learned by the learning unit. The provision unit can visually display the simulation results using the display 343 and speaker 240 of the headset terminal 314 and provide feedback to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the learning unit, simulation unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the learning unit can collect user behavior data using the camera 42 and microphone 238 of the robot 414 and analyze it using the control unit 46A. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates future life based on the information learned by the learning unit. The provision unit can visually display the simulation results using the display and speaker 240 of the robot 414 and provide feedback to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A learning section that learns about the user's lifestyle, hobbies, and values, A simulation unit that simulates future life based on the information learned by the learning unit, The system comprises a provisioning unit that provides a future lifestyle simulated by the aforementioned simulation unit. A system characterized by the following features. (Note 2) The aforementioned simulation unit, Simulate the future if you continue with a single or childless lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned simulation unit, Simulate future life with children. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Visually display the simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide specific advice based on the simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, It estimates the user's emotions and adjusts how it learns lifestyle, hobbies, and values ​​based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, Analyze the user's past behavior history and select the optimal learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, During learning, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, It estimates the user's emotions and determines the priority of information to learn based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, During learning, the system prioritizes learning highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During learning, the system analyzes users' social media activity and learns relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned simulation unit, The system estimates the user's emotions and adjusts the simulation's representation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned simulation unit, During the simulation, adjust the level of detail based on the importance of each lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned simulation unit, During the simulation, different simulation algorithms are applied depending on the lifestyle category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned simulation unit, During the simulation, the simulation priorities are determined based on the timing of lifestyle changes. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned simulation unit, During the simulation, the order of simulations is adjusted based on the relevance of lifestyles. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the simulation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When provided, the displayed simulation results will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the simulation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the optimal display method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust the displayed simulation results accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning section that learns about the user's lifestyle, hobbies, and values, A simulation unit that simulates future life based on the information learned by the learning unit, The system comprises a provisioning unit that provides a future lifestyle simulated by the aforementioned simulation unit. A system characterized by the following features.

2. The aforementioned simulation unit, Simulate the future if you continue with a single or childless lifestyle. The system according to feature 1.

3. The aforementioned simulation unit, Simulate future life with children. The system according to feature 1.

4. The aforementioned supply unit is, Visually display the simulation results. The system according to feature 1.

5. The aforementioned supply unit is, Provide specific advice based on the simulation results. The system according to feature 1.

6. The aforementioned learning unit, It estimates the user's emotions and adjusts how it learns lifestyle, hobbies, and values ​​based on those estimated emotions. The system according to feature 1.

7. The aforementioned learning unit, Analyze the user's past behavior history and select the optimal learning algorithm. The system according to feature 1.

8. The aforementioned learning unit, During learning, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned learning unit, It estimates the user's emotions and determines the priority of information to learn based on the estimated user emotions. The system according to feature 1.

10. The aforementioned learning unit, During learning, the system prioritizes learning highly relevant information by considering the user's geographical location. The system according to feature 1.

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