system

Abstract

The system according to this embodiment aims to effectively share users' personal experiences and events. [Solution] The system according to the embodiment comprises an interview unit, a material collection unit, a generation unit, and a publishing unit. The interview unit collects user information in interview format. The material collection unit collects materials provided by the user. The generation unit generates a video based on the information collected by the interview unit and the material collection unit. The publishing unit publishes the generated video.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is no sufficient means for effectively sharing the personal experiences and events of users, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively share the personal experiences and events of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an interview unit, a material collection unit, a generation unit, and a publishing unit. The interview unit collects user information in interview format. The material collection unit collects materials provided by the user. The generation unit generates a video based on the information collected by the interview unit and the material collection unit. The publishing unit publishes the generated video. [Effects of the Invention]

[0007] The system according to this embodiment can effectively share users' personal experiences and events. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An SNS platform according to an embodiment of the present invention is a system that utilizes generative AI to create videos of memorable events and turning points in a user's life and share them with other users. The system begins with the user answering questions in an interview format with the generative AI about the period and events they want to include in the video. The user provides materials such as their own photos, audio, and favorite music, selects an output format, and sets the video length, image, and options. The generative AI analyzes the user's input information and materials to generate a video. The generated video is made public based on the user's set privacy settings. Furthermore, additional services such as AI multiverse functionality, AI simulation functionality, and VR experience tour functionality are provided to encourage continued use. This allows social media platforms to enable users to create videos of memorable events and turning points in their lives and share them with other users.

[0029] The SNS platform according to this embodiment comprises an interview unit, a material collection unit, a generation unit, and a publication unit. The interview unit collects user information in the form of an interview. The interview unit collects information, for example, by having the user answer questions from a generation AI. The generation AI, for example, uses a natural language generation AI or a conversational AI to ask questions to the user and collect their answers. The material collection unit collects materials provided by the user. The material collection unit collects materials such as photos, audio, and music provided by the user. The material collection unit collects, for example, photos in JPEG format, audio in MP3 format, and music in WAV format. The generation unit generates a video based on the information collected by the interview unit and the material collection unit. The generation unit generates a video based on an output format selected by the user, for example. The generation unit generates a video based on an output format such as MP4 format, AVI format, or GIF format. The generation unit uses a generation AI to analyze the user's input information and materials and generates a video. The generation AI generates a video using a generation algorithm such as deep learning or rule-based generation. The publishing unit publishes the generated video. The publishing unit publishes the video based on, for example, the user's set scope of access. The publishing unit publishes the video based on, for example, private access, public access, or limited access. This allows the SNS platform according to the embodiment to collect user information, generate videos, and publish them.

[0030] The interview department can collect information by having users answer questions generated by a generative AI. The generative AI uses, for example, natural language generation AI or conversational AI to ask users questions and collect their answers. For example, the generative AI might ask a user, "What is the most memorable event in your life?" and collect the user's answer. The generative AI can also ask more detailed questions based on the user's answer. For example, based on the user's answer, the generative AI might ask, "What impact did that event have on you?" and collect the user's answer. In this way, using generative AI makes information gathering from users more efficient.

[0031] The material collection unit can collect at least one of the following materials provided by the user: photos, audio, or music. For example, the material collection unit collects materials such as photos, audio, and music provided by the user. For example, the material collection unit collects photos in JPEG format, audio in MP3 format, and music in WAV format. For instance, the material collection unit collects photos provided by the user in JPEG format. It can also collect audio provided by the user in MP3 format. Furthermore, it can collect music provided by the user in WAV format. This allows for the collection of a diverse range of materials provided by the user.

[0032] The generation unit can generate a video based on the output format selected by the user. For example, the generation unit can generate a video based on the output format selected by the user. For example, the generation unit can generate a video based on output formats such as MP4, AVI, and GIF. For example, the generation unit can generate a video in the MP4 format selected by the user. The generation unit can also generate a video in the AVI format selected by the user. Furthermore, the generation unit can also generate a video in the GIF format selected by the user. This allows for the generation of videos according to the user's selection.

[0033] The public access feature can publish videos based on user-defined access settings. For example, the public access feature can publish videos based on user-defined access settings. For example, the public access feature can publish videos based on access settings such as private, public, or restricted access. For instance, the public access feature can publish videos with user-defined private access. It can also publish videos with user-defined public access. Furthermore, the public access feature can publish videos with user-defined restricted access. This allows videos to be published with access settings tailored to the user's preferences.

[0034] Furthermore, the SNS platform includes a multiverse generation unit that incorporates variables into past events or choices to generate life videos that differ from the present. For example, the multiverse generation unit can generate scenarios where the user has made choices they did not previously make. It can also generate scenarios where the user has experienced events they did not previously experience. Furthermore, the multiverse generation unit can generate life videos where the user has made different choices in the past. This allows for the generation of different life scenarios.

[0035] Furthermore, the SNS platform includes a simulation unit that combines AI personality assessments with the lives of great figures or other users to predict future life outcomes. For example, the simulation unit can generate scenarios based on the user's personality assessment results, referencing the lives of great figures or other users. The simulation unit can also predict future life outcomes based on the user's personality assessment results. Additionally, the simulation unit can generate future scenarios based on the user's personality assessment results, thereby predicting the user's future scenarios.

[0036] Furthermore, the SNS platform includes a VR generation unit that virtualizes created videos and allows users to virtually experience the past in VR. For example, the VR generation unit can virtualize past events based on materials provided by the user. It can also 3D model past events and virtualize them based on materials provided by the user. Moreover, the VR generation unit can allow users to experience past events using a VR headset based on materials provided by the user. This allows users to experience past events in VR.

[0037] The interview function can analyze a user's past response history and select the most appropriate questions. For example, the interview function's AI can generate relevant questions based on the user's past responses. The AI ​​can also customize questions based on topics the user has shown interest in in the past. Furthermore, the AI ​​can avoid questions the user has avoided in the past and instead select questions that approach the topic from a different angle. This allows the system to ask the most appropriate questions based on the user's past response history.

[0038] The interview function can customize questions during an interview based on the user's current situation and areas of interest. For example, if the user is interested in their current occupation, the AI ​​can generate questions related to that occupation. Similarly, if the user has recently traveled, the AI ​​can generate questions about that trip. Furthermore, if the user has started a new hobby, the AI ​​can generate questions related to that hobby. This allows the interviewer to ask questions tailored to the user's current situation and areas of interest.

[0039] The interview team can prioritize relevant questions during interviews based on the user's geographical location. For example, if the user lives in a particular area, the interview team can ask about events and culture related to that area. If the user is traveling, the interview team can also ask about tourist attractions and events related to their current location. Furthermore, if the user is considering moving, the interview team can ask questions seeking information and advice about the new area. This allows for questions to be asked based on the user's geographical location.

[0040] The interview team can ask relevant questions based on the user's social media activity during the interview. For example, the interview team can analyze the user's social media activity during the interview and ask relevant questions. For example, the interview team can ask questions based on events the user has recently shared on social media. The interview team can also ask questions related to topics and hashtags the user follows. Furthermore, the interview team can ask questions based on events and news that the user has shown interest in on social media. This allows the interview team to ask questions based on the user's social media activity.

[0041] The material collection unit can analyze the user's past material provision history and select the optimal collection method. For example, the material collection unit's generating AI can select the optimal collection method based on the types of materials the user has provided in the past. The material collection unit can also adjust the collection method based on the quantity of materials the user has provided in the past. Furthermore, the material collection unit can select the collection method based on the quality of materials the user has provided in the past. This allows the system to select the optimal collection method based on the user's past material provision history.

[0042] The material collection unit can customize how it collects materials based on the user's current situation and areas of interest. For example, if the user is interested in their current occupation, the material collection unit will collect materials related to that occupation. It can also collect materials related to a recent trip if the user has traveled. Furthermore, if the user has started a new hobby, the material collection unit can collect materials related to that hobby. This allows for the customization of material collection methods according to the user's current situation and areas of interest.

[0043] The material collection unit can prioritize collecting highly relevant materials based on the user's geographical location information during material collection. For example, the material collection unit prioritizes collecting highly relevant materials by considering the user's geographical location information during material collection. For instance, if the user lives in a specific region, the material collection unit will prioritize collecting materials related to that region. Furthermore, if the user is traveling, the material collection unit can prioritize collecting materials related to their current location. Additionally, if the user is considering moving, the material collection unit can prioritize collecting materials related to their new region. This allows for the priority collection of materials based on the user's geographical location information.

[0044] The material collection unit can collect relevant material based on the user's social media activity during material collection. For example, the material collection unit can analyze the user's social media activity and collect relevant material during material collection. For instance, the material collection unit can collect material based on events the user has recently shared on social media. The material collection unit can also collect material related to topics and hashtags the user follows. Furthermore, the material collection unit can collect material based on events and news the user has shown interest in on social media. This allows for the collection of material based on the user's social media activity.

[0045] The generation unit can adjust the level of detail in the video based on the importance of the materials provided by the user during video generation. For example, the generation unit can adjust the level of detail in the video based on the importance of the photos provided by the user. The generation unit can also adjust the level of detail in the video based on the importance of the audio provided by the user. Furthermore, the generation unit can adjust the level of detail in the video based on the importance of the music provided by the user. This allows the level of detail in the video to be adjusted according to the importance of the materials provided by the user.

[0046] The generation unit can apply different generation algorithms depending on the output format selected by the user when generating a video. For example, if the user selects a short video, the generation AI will apply a generation algorithm that focuses on the essential points in a short amount of time. If the user selects a long video, the generation AI can also apply a generation algorithm that includes detailed content. Furthermore, if the user selects a specific theme, the generation AI can apply a generation algorithm best suited to that theme. This allows the generation algorithm to be applied according to the output format selected by the user.

[0047] The generation unit can prioritize videos based on the submission timing of materials provided by the user during video generation. For example, the generation unit may prioritize using materials recently provided by the user when generating videos. The generation unit can also use the generation AI to prioritize videos based on materials previously provided by the user. Furthermore, if the user provides materials related to a specific event, the generation unit can prioritize using those materials when generating videos. This allows for the prioritization of videos according to the submission timing of materials provided by the user.

[0048] The generation unit can adjust the order of videos during video generation, taking into account the relevance of the materials provided by the user. For example, the generation unit can adjust the order of videos based on the relevance of the materials provided by the user. For instance, the generation unit's AI can adjust the order of videos based on the relevance of the photos provided by the user. The generation unit can also adjust the order of videos based on the relevance of the audio provided by the user. Furthermore, the generation unit can adjust the order of videos based on the relevance of the music provided by the user. This allows the order of videos to be adjusted according to the relevance of the materials provided by the user.

[0049] The publishing function can analyze a user's past publishing history to select the optimal publishing method at the time of publication. For example, the publishing function's generating AI can select the optimal publishing method based on the scope of videos the user has previously published. The publishing function can also adjust the publishing method based on the response to videos the user has previously published. Furthermore, the publishing function can select the publishing method based on the content of videos the user has previously published. This allows for the selection of the optimal publishing method based on the user's past publishing history.

[0050] The publishing function can customize the publishing method based on the user's current situation and areas of interest at the time of publication. For example, if the publishing function is interested in the user's current occupation, it can provide publishing methods related to that occupation. It can also provide publishing methods related to the user's recent travels. Furthermore, if the user has started a new hobby, it can provide publishing methods related to that hobby. This allows for the customization of publishing methods according to the user's current situation and areas of interest.

[0051] The publishing function can select the optimal publishing method based on the user's geographical location information at the time of publication. For example, the publishing function can select the optimal publishing method considering the user's geographical location information at the time of publication. For example, if the publishing function lives in a specific region, it can provide a publishing method relevant to that region. It can also provide a publishing method relevant to the user's current location if the user is traveling. Furthermore, if the publishing function is considering moving, it can provide a publishing method relevant to the new region. This allows for the selection of the optimal publishing method based on the user's geographical location information.

[0052] The publishing department can provide relevant publishing methods based on the user's social media activity at the time of publication. For example, the publishing department can analyze the user's social media activity at the time of publication and provide relevant publishing methods. For example, the publishing department can provide publishing methods based on events the user has recently shared on social media. The publishing department can also provide publishing methods related to topics and hashtags the user follows. Furthermore, the publishing department can provide publishing methods based on events and news that the user has shown interest in on social media. This allows the publishing department to provide publishing methods based on the user's social media activity.

[0053] The multiverse generation unit can analyze the user's past selection history to select the optimal generation method when generating a multiverse. For example, the multiverse generation unit uses the user's past selection history to generate an optimal multiverse using its generation AI. Furthermore, the multiverse generation unit can also use its generation AI to suggest routes that avoid congestion based on the user's past selection history. In addition, the multiverse generation unit can analyze the user's past selection history and have its generation AI suggest the most efficient route. This allows for the selection of the optimal generation method based on the user's past selection history.

[0054] The multiverse generation unit can customize the generation method based on the user's current situation when generating a multiverse. For example, if the user is interested in their current occupation, the multiverse generation unit can generate a multiverse related to that occupation. Furthermore, if the user has recently traveled, the multiverse generation unit can generate a multiverse related to that trip. Additionally, if the user has started a new hobby, the multiverse generation unit can generate a multiverse related to that hobby. This allows for customization of the generation method according to the user's current situation.

[0055] The multiverse generation unit can select the optimal generation method based on the user's geographical location information when generating a multiverse. For example, the multiverse generation unit selects the optimal generation method considering the user's geographical location information when generating a multiverse. For example, if the user lives in a specific region, the multiverse generation unit will generate a multiverse related to that region. Also, if the user is traveling, the multiverse generation unit can generate a multiverse related to their current location. Furthermore, if the user is considering moving, the multiverse generation unit can generate a multiverse related to the new region. This allows the unit to select the optimal generation method based on the user's geographical location information.

[0056] The multiverse generation unit can propose generation methods based on the user's social media activity when generating a multiverse. For example, the multiverse generation unit can analyze the user's social media activity and propose generation methods when generating a multiverse. For example, the multiverse generation unit can generate a multiverse based on events the user has recently shared on social media. The multiverse generation unit can also generate multiverses related to topics and hashtags the user follows. Furthermore, the multiverse generation unit can generate a multiverse based on events and news that the user has shown interest in on social media. This allows the unit to propose generation methods based on the user's social media activity.

[0057] The simulation unit can analyze the user's past behavior history during simulation to select the optimal simulation method. For example, the simulation unit can use the user's past behavior history to select the optimal simulation method. For example, the simulation unit can use the user's past actions to have the generating AI provide the optimal simulation. The simulation unit can also use the user's past behavior history to have the generating AI provide the most efficient simulation. Furthermore, the simulation unit can analyze the user's past behavior history to have the generating AI provide the most effective simulation. This allows for the selection of the optimal simulation method based on the user's past behavior history.

[0058] The simulation unit can customize the simulation methods based on the user's current situation during the simulation. For example, if the user is interested in their current occupation, the simulation unit can provide a simulation related to that occupation. It can also provide a simulation related to a recent trip if the user has traveled. Furthermore, if the user has started a new hobby, the simulation unit can provide a simulation related to that hobby. This allows the simulation methods to be customized according to the user's current situation.

[0059] The simulation unit can select the optimal simulation method based on the user's geographical location information during the simulation. For example, the simulation unit selects the optimal simulation method considering the user's geographical location information during the simulation. For instance, if the user lives in a specific region, the simulation unit provides a simulation related to that region. Furthermore, if the user is traveling, the simulation unit can provide a simulation related to their current location. Additionally, if the user is considering moving, the simulation unit can provide a simulation related to the new region. This allows the system to select the optimal simulation method based on the user's geographical location information.

[0060] The simulation unit can propose simulation methods based on the user's social media activity during the simulation. For example, the simulation unit can analyze the user's social media activity during the simulation and propose simulation methods. For instance, the simulation unit can provide a simulation based on events the user has recently shared on social media. The simulation unit can also provide simulations related to topics and hashtags the user follows. Furthermore, the simulation unit can provide simulations based on events and news that the user has shown interest in on social media. This allows the system to propose simulation methods based on the user's social media activity.

[0061] The VR generation unit can analyze the user's past behavior history to select the optimal generation method when generating VR. For example, the VR generation unit uses the user's past actions to generate the optimal VR using its generation AI. The VR generation unit can also use the user's past behavior history to generate the most efficient VR using its generation AI. Furthermore, the VR generation unit can analyze the user's past behavior history to generate the most effective VR using its generation AI. This allows the system to select the optimal generation method based on the user's past behavior history.

[0062] The VR generation unit can customize the generation method based on the user's current situation when generating VR content. For example, if the user is interested in their current occupation, the VR generation unit can generate VR content related to that occupation. It can also generate VR content related to a recent trip if the user has traveled. Furthermore, if the user has started a new hobby, the VR generation unit can generate VR content related to that hobby. This allows for customization of the generation method according to the user's current situation.

[0063] The VR generation unit can select the optimal generation method based on the user's geographical location information when generating VR. For example, the VR generation unit selects the optimal generation method by considering the user's geographical location information when generating VR. For example, if the user lives in a specific region, the VR generation unit will generate VR related to that region. Also, if the user is traveling, the VR generation unit can generate VR related to their current location. Furthermore, if the user is considering moving, the VR generation unit can generate VR related to the new region. This allows the system to select the optimal generation method based on the user's geographical location information.

[0064] The VR generation unit can suggest generation methods based on the user's social media activity during VR generation. For example, the VR generation unit analyzes the user's social media activity during VR generation and suggests generation methods. For instance, the VR generation unit generates VR based on events the user has recently shared on social media. It can also generate VR related to topics and hashtags the user follows. Furthermore, the VR generation unit can generate VR based on events and news the user has shown interest in on social media. This allows the VR generation unit to suggest generation methods based on the user's social media activity.

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

[0066] Social media platforms can also analyze a user's past behavior history and select the optimal video generation algorithm. For example, the generation AI can select the best algorithm based on the style and theme of videos the user has previously selected. It can also analyze the characteristics of videos that the user has previously rated highly and generate videos with similar characteristics. Furthermore, the generation AI can adjust to avoid video styles that the user has previously avoided. This allows for optimal video generation based on the user's past behavior history.

[0067] Social networking platforms can also incorporate relevant events and culture into videos based on the user's current geographical location. For example, if a user lives in a particular region, the video can include local festivals and events. If the user is traveling, the video can include tourist attractions and landmarks in their current location. Furthermore, if the user is considering moving, the video can include information and advice about the new area. This allows for the creation of videos that are tailored to the user's geographical location.

[0068] Social networking platforms can analyze users' social media activity and incorporate relevant topics and hashtags into videos. For example, they can generate videos based on events users have recently shared. They can also incorporate content related to topics and hashtags users follow. Furthermore, they can generate videos based on events and news that users have shown interest in on social media. This allows for the creation of videos that are tailored to users' social media activity.

[0069] Social media platforms can also analyze a user's past video viewing history and select the optimal video generation algorithm. For example, the generation AI can select the best algorithm based on the style and themes of videos the user has watched in the past. It can also analyze the characteristics of videos that the user has previously rated highly and generate videos with similar characteristics. Furthermore, the generation AI can adjust to avoid video styles that the user has previously avoided. This allows for optimal video generation based on the user's past video viewing history.

[0070] Social networking platforms can also incorporate relevant topics and themes into videos based on the user's current interests. For example, if a user is interested in their current profession, the video can include content related to that profession. Similarly, if a user has recently traveled, the video can include content related to that trip. Furthermore, if a user has started a new hobby, the video can include content related to that hobby. This allows for the creation of videos tailored to the user's current interests.

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

[0072] Step 1: The interview department collects user information in an interview format. For example, information is collected by having users answer questions generated by a generative AI. The generative AI uses natural language generation AI or conversational AI to ask questions to the user and collect their answers. Step 2: The material collection unit collects materials provided by the user. For example, it collects materials such as photos, audio, and music provided by the user. The material collection unit collects photos in JPEG format, audio in MP3 format, music in WAV format, etc. Step 3: The generation unit generates a video based on the information collected by the interview unit and the material collection unit. For example, it generates a video based on the output format selected by the user. The generation unit generates a video based on output formats such as MP4, AVI, and GIF. Using a generation AI, it analyzes the user's input information and materials, and generates a video using generation algorithms such as deep learning and rule-based generation. Step 4: The publishing unit publishes the generated video. For example, it publishes the video based on the user's set privacy settings. The publishing unit publishes the video based on privacy settings such as private, public, or restricted.

[0073] (Example of form 2) An SNS platform according to an embodiment of the present invention is a system that utilizes generative AI to create videos of memorable events and turning points in a user's life and share them with other users. The system begins with the user answering questions in an interview format with the generative AI about the period and events they want to include in the video. The user provides materials such as their own photos, audio, and favorite music, selects an output format, and sets the video length, image, and options. The generative AI analyzes the user's input information and materials to generate a video. The generated video is made public based on the user's set privacy settings. Furthermore, additional services such as AI multiverse functionality, AI simulation functionality, and VR experience tour functionality are provided to encourage continued use. This allows social media platforms to enable users to create videos of memorable events and turning points in their lives and share them with other users.

[0074] The SNS platform according to this embodiment comprises an interview unit, a material collection unit, a generation unit, and a publication unit. The interview unit collects user information in the form of an interview. The interview unit collects information, for example, by having the user answer questions from a generation AI. The generation AI, for example, uses a natural language generation AI or a conversational AI to ask questions to the user and collect their answers. The material collection unit collects materials provided by the user. The material collection unit collects materials such as photos, audio, and music provided by the user. The material collection unit collects, for example, photos in JPEG format, audio in MP3 format, and music in WAV format. The generation unit generates a video based on the information collected by the interview unit and the material collection unit. The generation unit generates a video based on an output format selected by the user, for example. The generation unit generates a video based on an output format such as MP4 format, AVI format, or GIF format. The generation unit uses a generation AI to analyze the user's input information and materials and generates a video. The generation AI generates a video using a generation algorithm such as deep learning or rule-based generation. The publishing unit publishes the generated video. The publishing unit publishes the video based on, for example, the user's set scope of access. The publishing unit publishes the video based on, for example, private access, public access, or limited access. This allows the SNS platform according to the embodiment to collect user information, generate videos, and publish them.

[0075] The interview department can collect information by having users answer questions generated by a generative AI. The generative AI uses, for example, natural language generation AI or conversational AI to ask users questions and collect their answers. For example, the generative AI might ask a user, "What is the most memorable event in your life?" and collect the user's answer. The generative AI can also ask more detailed questions based on the user's answer. For example, based on the user's answer, the generative AI might ask, "What impact did that event have on you?" and collect the user's answer. In this way, using generative AI makes information gathering from users more efficient.

[0076] The material collection unit can collect at least one of the following materials provided by the user: photos, audio, or music. For example, the material collection unit collects materials such as photos, audio, and music provided by the user. For example, the material collection unit collects photos in JPEG format, audio in MP3 format, and music in WAV format. For instance, the material collection unit collects photos provided by the user in JPEG format. It can also collect audio provided by the user in MP3 format. Furthermore, it can collect music provided by the user in WAV format. This allows for the collection of a diverse range of materials provided by the user.

[0077] The generation unit can generate a video based on the output format selected by the user. For example, the generation unit can generate a video based on the output format selected by the user. For example, the generation unit can generate a video based on output formats such as MP4, AVI, and GIF. For example, the generation unit can generate a video in the MP4 format selected by the user. The generation unit can also generate a video in the AVI format selected by the user. Furthermore, the generation unit can also generate a video in the GIF format selected by the user. This allows for the generation of videos according to the user's selection.

[0078] The public access feature can publish videos based on user-defined access settings. For example, the public access feature can publish videos based on user-defined access settings. For example, the public access feature can publish videos based on access settings such as private, public, or restricted access. For instance, the public access feature can publish videos with user-defined private access. It can also publish videos with user-defined public access. Furthermore, the public access feature can publish videos with user-defined restricted access. This allows videos to be published with access settings tailored to the user's preferences.

[0079] Furthermore, the SNS platform includes a multiverse generation unit that incorporates variables into past events or choices to generate life videos that differ from the present. For example, the multiverse generation unit can generate scenarios where the user has made choices they did not previously make. It can also generate scenarios where the user has experienced events they did not previously experience. Furthermore, the multiverse generation unit can generate life videos where the user has made different choices in the past. This allows for the generation of different life scenarios.

[0080] Furthermore, the SNS platform includes a simulation unit that combines AI personality assessments with the lives of great figures or other users to predict future life outcomes. For example, the simulation unit can generate scenarios based on the user's personality assessment results, referencing the lives of great figures or other users. The simulation unit can also predict future life outcomes based on the user's personality assessment results. Additionally, the simulation unit can generate future scenarios based on the user's personality assessment results, thereby predicting the user's future scenarios.

[0081] Furthermore, the SNS platform includes a VR generation unit that virtualizes created videos and allows users to virtually experience the past in VR. For example, the VR generation unit can virtualize past events based on materials provided by the user. It can also 3D model past events and virtualize them based on materials provided by the user. Moreover, the VR generation unit can allow users to experience past events using a VR headset based on materials provided by the user. This allows users to experience past events in VR.

[0082] The interview function can estimate the user's emotions and adjust the content and order of questions based on those estimations. For example, if the user is sad, the generative AI will ask questions in a gentle tone and prioritize questions that will soothe their emotions. If the user is excited, the generative AI can ask questions that will evoke that excitement and focus on positive events. Furthermore, if the user is nervous, the generative AI can start with simple, easy-to-answer questions to help them relax and gradually move to deeper questions. This allows for questioning that is tailored to the user's emotions.

[0083] The interview function can analyze a user's past response history and select the most appropriate questions. For example, the interview function's AI can generate relevant questions based on the user's past responses. The AI ​​can also customize questions based on topics the user has shown interest in in the past. Furthermore, the AI ​​can avoid questions the user has avoided in the past and instead select questions that approach the topic from a different angle. This allows the system to ask the most appropriate questions based on the user's past response history.

[0084] The interview function can customize questions during an interview based on the user's current situation and areas of interest. For example, if the user is interested in their current occupation, the AI ​​can generate questions related to that occupation. Similarly, if the user has recently traveled, the AI ​​can generate questions about that trip. Furthermore, if the user has started a new hobby, the AI ​​can generate questions related to that hobby. This allows the interviewer to ask questions tailored to the user's current situation and areas of interest.

[0085] The interview function can estimate the user's emotions and adjust the interview pace based on those emotions. For example, if the user is relaxed, the generating AI will conduct the interview at a slow pace. If the user is in a hurry, the generating AI can ask questions quickly to complete the interview in a short time. Furthermore, if the user is excited, the generating AI can ask questions at a brisk pace to maintain that excitement. This allows the interview pace to be adjusted according to the user's emotions.

[0086] The interview team can prioritize relevant questions during interviews based on the user's geographical location. For example, if the user lives in a particular area, the interview team can ask about events and culture related to that area. If the user is traveling, the interview team can also ask about tourist attractions and events related to their current location. Furthermore, if the user is considering moving, the interview team can ask questions seeking information and advice about the new area. This allows for questions to be asked based on the user's geographical location.

[0087] The interview team can ask relevant questions based on the user's social media activity during the interview. For example, the interview team can analyze the user's social media activity during the interview and ask relevant questions. For example, the interview team can ask questions based on events the user has recently shared on social media. The interview team can also ask questions related to topics and hashtags the user follows. Furthermore, the interview team can ask questions based on events and news that the user has shown interest in on social media. This allows the interview team to ask questions based on the user's social media activity.

[0088] The material collection unit can estimate the user's emotions and adjust the material collection method based on those estimated emotions. For example, if the user is relaxed, the generating AI may request detailed material. If the user is in a hurry, the generating AI may request simpler material. Furthermore, if the user is excited, the generating AI may request more material. This allows the material collection method to be adjusted according to the user's emotions.

[0089] The material collection unit can analyze the user's past material provision history and select the optimal collection method. For example, the material collection unit's generating AI can select the optimal collection method based on the types of materials the user has provided in the past. The material collection unit can also adjust the collection method based on the quantity of materials the user has provided in the past. Furthermore, the material collection unit can select the collection method based on the quality of materials the user has provided in the past. This allows the system to select the optimal collection method based on the user's past material provision history.

[0090] The material collection unit can customize how it collects materials based on the user's current situation and areas of interest. For example, if the user is interested in their current occupation, the material collection unit will collect materials related to that occupation. It can also collect materials related to a recent trip if the user has traveled. Furthermore, if the user has started a new hobby, the material collection unit can collect materials related to that hobby. This allows for the customization of material collection methods according to the user's current situation and areas of interest.

[0091] The material collection unit can estimate the user's emotions and determine the priority of materials to collect based on those estimated emotions. For example, if the user is relaxed, the generating AI will prioritize collecting detailed materials. If the user is in a hurry, the generating AI can also prioritize collecting important materials. Furthermore, if the user is excited, the generating AI can also prioritize collecting a large amount of material. This allows for the prioritization of materials to be determined according to the user's emotions.

[0092] The material collection unit can prioritize collecting highly relevant materials based on the user's geographical location information during material collection. For example, the material collection unit prioritizes collecting highly relevant materials by considering the user's geographical location information during material collection. For instance, if the user lives in a specific region, the material collection unit will prioritize collecting materials related to that region. Furthermore, if the user is traveling, the material collection unit can prioritize collecting materials related to their current location. Additionally, if the user is considering moving, the material collection unit can prioritize collecting materials related to their new region. This allows for the priority collection of materials based on the user's geographical location information.

[0093] The material collection unit can collect relevant material based on the user's social media activity during material collection. For example, the material collection unit can analyze the user's social media activity and collect relevant material during material collection. For instance, the material collection unit can collect material based on events the user has recently shared on social media. The material collection unit can also collect material related to topics and hashtags the user follows. Furthermore, the material collection unit can collect material based on events and news the user has shown interest in on social media. This allows for the collection of material based on the user's social media activity.

[0094] The generation unit can estimate the user's emotions and adjust the video's presentation based on those emotions. For example, if the user is relaxed, the generation AI will produce a video that progresses at a leisurely pace. If the user is in a hurry, the generation AI can also produce a video that emphasizes the shortest route. Furthermore, if the user is excited, the generation AI can produce a video with visually stimulating effects. This allows the video's presentation to be adjusted according to the user's emotions.

[0095] The generation unit can adjust the level of detail in the video based on the importance of the materials provided by the user during video generation. For example, the generation unit can adjust the level of detail in the video based on the importance of the photos provided by the user. The generation unit can also adjust the level of detail in the video based on the importance of the audio provided by the user. Furthermore, the generation unit can adjust the level of detail in the video based on the importance of the music provided by the user. This allows the level of detail in the video to be adjusted according to the importance of the materials provided by the user.

[0096] The generation unit can apply different generation algorithms depending on the output format selected by the user when generating a video. For example, if the user selects a short video, the generation AI will apply a generation algorithm that focuses on the essential points in a short amount of time. If the user selects a long video, the generation AI can also apply a generation algorithm that includes detailed content. Furthermore, if the user selects a specific theme, the generation AI can apply a generation algorithm best suited to that theme. This allows the generation algorithm to be applied according to the output format selected by the user.

[0097] The generation unit can estimate the user's emotions and adjust the video length based on those emotions. For example, if the user is in a hurry, the generation AI will produce a short, concise video. If the user is relaxed, the generation AI can produce a longer video with more detailed explanations. Furthermore, if the user is excited, the generation AI can produce a video with visually stimulating effects. This allows the video length to be adjusted according to the user's emotions.

[0098] The generation unit can prioritize videos based on the submission timing of materials provided by the user during video generation. For example, the generation unit may prioritize using materials recently provided by the user when generating videos. The generation unit can also use the generation AI to prioritize videos based on materials previously provided by the user. Furthermore, if the user provides materials related to a specific event, the generation unit can prioritize using those materials when generating videos. This allows for the prioritization of videos according to the submission timing of materials provided by the user.

[0099] The generation unit can adjust the order of videos during video generation, taking into account the relevance of the materials provided by the user. For example, the generation unit can adjust the order of videos based on the relevance of the materials provided by the user. For instance, the generation unit's AI can adjust the order of videos based on the relevance of the photos provided by the user. The generation unit can also adjust the order of videos based on the relevance of the audio provided by the user. Furthermore, the generation unit can adjust the order of videos based on the relevance of the music provided by the user. This allows the order of videos to be adjusted according to the relevance of the materials provided by the user.

[0100] The publishing function can estimate the user's emotions and adjust the publishing method based on those emotions. For example, if the user is relaxed, the generating AI can provide detailed publishing options. If the user is in a hurry, the generating AI can provide simple publishing options. Furthermore, if the user is excited, the generating AI can provide many publishing options. This allows the publishing method to be adjusted according to the user's emotions.

[0101] The publishing function can analyze a user's past publishing history to select the optimal publishing method at the time of publication. For example, the publishing function's generating AI can select the optimal publishing method based on the scope of videos the user has previously published. The publishing function can also adjust the publishing method based on the response to videos the user has previously published. Furthermore, the publishing function can select the publishing method based on the content of videos the user has previously published. This allows for the selection of the optimal publishing method based on the user's past publishing history.

[0102] The publishing function can customize the publishing method based on the user's current situation and areas of interest at the time of publication. For example, if the publishing function is interested in the user's current occupation, it can provide publishing methods related to that occupation. It can also provide publishing methods related to the user's recent travels. Furthermore, if the user has started a new hobby, it can provide publishing methods related to that hobby. This allows for the customization of publishing methods according to the user's current situation and areas of interest.

[0103] The publishing unit can estimate the user's emotions and prioritize which videos to publish based on those emotions. For example, if the user is relaxed, the generating AI will prioritize publishing detailed videos. If the user is in a hurry, the generating AI can also prioritize publishing important videos. Furthermore, if the user is excited, the generating AI can also prioritize publishing many videos. This allows for the prioritization of videos according to the user's emotions.

[0104] The publishing function can select the optimal publishing method based on the user's geographical location information at the time of publication. For example, the publishing function can select the optimal publishing method considering the user's geographical location information at the time of publication. For example, if the publishing function lives in a specific region, it can provide a publishing method relevant to that region. It can also provide a publishing method relevant to the user's current location if the user is traveling. Furthermore, if the publishing function is considering moving, it can provide a publishing method relevant to the new region. This allows for the selection of the optimal publishing method based on the user's geographical location information.

[0105] The publishing department can provide relevant publishing methods based on the user's social media activity at the time of publication. For example, the publishing department can analyze the user's social media activity at the time of publication and provide relevant publishing methods. For example, the publishing department can provide publishing methods based on events the user has recently shared on social media. The publishing department can also provide publishing methods related to topics and hashtags the user follows. Furthermore, the publishing department can provide publishing methods based on events and news that the user has shown interest in on social media. This allows the publishing department to provide publishing methods based on the user's social media activity.

[0106] The multiverse generation unit can estimate the user's emotions and adjust the multiverse generation method based on those emotions. For example, if the user is relaxed, the generating AI will generate a multiverse that progresses at a leisurely pace. If the user is in a hurry, the generating AI can also generate a multiverse that emphasizes the shortest route. Furthermore, if the user is excited, the generating AI can also generate a multiverse with visually stimulating effects. This allows the multiverse generation method to be adjusted according to the user's emotions.

[0107] The multiverse generation unit can analyze the user's past selection history to select the optimal generation method when generating a multiverse. For example, the multiverse generation unit uses the user's past selection history to generate an optimal multiverse using its generation AI. Furthermore, the multiverse generation unit can also use its generation AI to suggest routes that avoid congestion based on the user's past selection history. In addition, the multiverse generation unit can analyze the user's past selection history and have its generation AI suggest the most efficient route. This allows for the selection of the optimal generation method based on the user's past selection history.

[0108] The multiverse generation unit can customize the generation method based on the user's current situation when generating a multiverse. For example, if the user is interested in their current occupation, the multiverse generation unit can generate a multiverse related to that occupation. Furthermore, if the user has recently traveled, the multiverse generation unit can generate a multiverse related to that trip. Additionally, if the user has started a new hobby, the multiverse generation unit can generate a multiverse related to that hobby. This allows for customization of the generation method according to the user's current situation.

[0109] The multiverse generation unit can estimate the user's emotions and determine the priority of multiverses based on those emotions. For example, if the user is relaxed, the generating AI will prioritize generating detailed multiverses. If the user is in a hurry, the generating AI can also prioritize generating important multiverses. Furthermore, if the user is excited, the generating AI can also prioritize generating many multiverses. This allows for the determination of multiverse priorities in accordance with the user's emotions.

[0110] The multiverse generation unit can select the optimal generation method based on the user's geographical location information when generating a multiverse. For example, the multiverse generation unit selects the optimal generation method considering the user's geographical location information when generating a multiverse. For example, if the user lives in a specific region, the multiverse generation unit will generate a multiverse related to that region. Also, if the user is traveling, the multiverse generation unit can generate a multiverse related to their current location. Furthermore, if the user is considering moving, the multiverse generation unit can generate a multiverse related to the new region. This allows the unit to select the optimal generation method based on the user's geographical location information.

[0111] The multiverse generation unit can propose generation methods based on the user's social media activity when generating a multiverse. For example, the multiverse generation unit can analyze the user's social media activity and propose generation methods when generating a multiverse. For example, the multiverse generation unit can generate a multiverse based on events the user has recently shared on social media. The multiverse generation unit can also generate multiverses related to topics and hashtags the user follows. Furthermore, the multiverse generation unit can generate a multiverse based on events and news that the user has shown interest in on social media. This allows the unit to propose generation methods based on the user's social media activity.

[0112] The simulation unit can estimate the user's emotions and adjust the simulation method based on those emotions. For example, if the user is relaxed, the generating AI can provide a simulation that proceeds at a leisurely pace. If the user is in a hurry, the generating AI can provide a simulation that proceeds quickly. Furthermore, if the user is excited, the generating AI can provide a simulation with visually stimulating effects. This allows the simulation method to be adjusted according to the user's emotions.

[0113] The simulation unit can analyze the user's past behavior history during simulation to select the optimal simulation method. For example, the simulation unit can use the user's past behavior history to select the optimal simulation method. For example, the simulation unit can use the user's past actions to have the generating AI provide the optimal simulation. The simulation unit can also use the user's past behavior history to have the generating AI provide the most efficient simulation. Furthermore, the simulation unit can analyze the user's past behavior history to have the generating AI provide the most effective simulation. This allows for the selection of the optimal simulation method based on the user's past behavior history.

[0114] The simulation unit can customize the simulation methods based on the user's current situation during the simulation. For example, if the user is interested in their current occupation, the simulation unit can provide a simulation related to that occupation. It can also provide a simulation related to a recent trip if the user has traveled. Furthermore, if the user has started a new hobby, the simulation unit can provide a simulation related to that hobby. This allows the simulation methods to be customized according to the user's current situation.

[0115] The simulation unit can estimate the user's emotions and determine the priority of simulations based on those emotions. For example, if the user is relaxed, the generation AI will prioritize providing detailed simulations. If the user is in a hurry, the generation AI can also prioritize providing important simulations. Furthermore, if the user is excited, the generation AI can also prioritize providing many simulations. This allows the simulation unit to determine the priority of simulations according to the user's emotions.

[0116] The simulation unit can select the optimal simulation method based on the user's geographical location information during the simulation. For example, the simulation unit selects the optimal simulation method considering the user's geographical location information during the simulation. For instance, if the user lives in a specific region, the simulation unit provides a simulation related to that region. Furthermore, if the user is traveling, the simulation unit can provide a simulation related to their current location. Additionally, if the user is considering moving, the simulation unit can provide a simulation related to the new region. This allows the system to select the optimal simulation method based on the user's geographical location information.

[0117] The simulation unit can propose simulation methods based on the user's social media activity during the simulation. For example, the simulation unit can analyze the user's social media activity during the simulation and propose simulation methods. For instance, the simulation unit can provide a simulation based on events the user has recently shared on social media. The simulation unit can also provide simulations related to topics and hashtags the user follows. Furthermore, the simulation unit can provide simulations based on events and news that the user has shown interest in on social media. This allows the system to propose simulation methods based on the user's social media activity.

[0118] The VR generation unit can estimate the user's emotions and adjust the VR generation method based on those emotions. For example, if the user is relaxed, the generation AI will generate a VR experience that progresses at a leisurely pace. If the user is in a hurry, the generation AI can also generate a fast-paced VR experience. Furthermore, if the user is excited, the generation AI can also generate a VR experience with visually stimulating effects. This allows the VR generation method to be adjusted according to the user's emotions.

[0119] The VR generation unit can analyze the user's past behavior history to select the optimal generation method when generating VR. For example, the VR generation unit uses the user's past actions to generate the optimal VR using its generation AI. The VR generation unit can also use the user's past behavior history to generate the most efficient VR using its generation AI. Furthermore, the VR generation unit can analyze the user's past behavior history to generate the most effective VR using its generation AI. This allows the system to select the optimal generation method based on the user's past behavior history.

[0120] The VR generation unit can customize the generation method based on the user's current situation when generating VR content. For example, if the user is interested in their current occupation, the VR generation unit can generate VR content related to that occupation. It can also generate VR content related to a recent trip if the user has traveled. Furthermore, if the user has started a new hobby, the VR generation unit can generate VR content related to that hobby. This allows for customization of the generation method according to the user's current situation.

[0121] The VR generation unit can estimate the user's emotions and determine the priority of VR content based on those emotions. For example, if the user is relaxed, the generation AI will prioritize generating detailed VR content. If the user is in a hurry, the generation AI can also prioritize generating important VR content. Furthermore, if the user is excited, the generation AI can also prioritize generating many VR content. This allows the system to determine VR priorities according to the user's emotions.

[0122] The VR generation unit can select the optimal generation method based on the user's geographical location information when generating VR. For example, the VR generation unit selects the optimal generation method by considering the user's geographical location information when generating VR. For example, if the user lives in a specific region, the VR generation unit will generate VR related to that region. Also, if the user is traveling, the VR generation unit can generate VR related to their current location. Furthermore, if the user is considering moving, the VR generation unit can generate VR related to the new region. This allows the system to select the optimal generation method based on the user's geographical location information.

[0123] The VR generation unit can suggest generation methods based on the user's social media activity during VR generation. For example, the VR generation unit analyzes the user's social media activity during VR generation and suggests generation methods. For instance, the VR generation unit generates VR based on events the user has recently shared on social media. It can also generate VR related to topics and hashtags the user follows. Furthermore, the VR generation unit can generate VR based on events and news the user has shown interest in on social media. This allows the VR generation unit to suggest generation methods based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the interview unit, material collection unit, generation unit, publication unit, multiverse generation unit, simulation unit, and VR generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the interview unit is implemented by the control unit 46A of the smart device 14 and collects user information in interview format. The material collection unit is implemented by the control unit 46A of the smart device 14 and collects materials such as photos, audio, and music provided by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a video based on the collected information. The publication unit is implemented by the specific processing unit 290 of the data processing unit 12 and publishes the generated video. The multiverse generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a life video incorporating variables into past events and choices. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs life prediction by combining AI personality diagnosis with the lives of great people or other users. The VR generation unit is implemented by the control unit 46A of the smart device 14, which transforms the created video into virtual reality, allowing users to virtually experience the past in VR. === Hard Collateral 1-2 === Each of the multiple elements described above, including the interview unit, material collection unit, generation unit, publication unit, multiverse generation unit, simulation unit, and VR generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the interview unit is implemented by the control unit 46A of the smart glasses 214 and collects user information in interview format. The material collection unit is implemented by the control unit 46A of the smart glasses 214 and collects materials such as photos, audio, and music provided by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a video based on the collected information. The publication unit is implemented by the specific processing unit 290 of the data processing unit 12 and publishes the generated video. The multiverse generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a life video incorporating variables into past events and choices. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs life prediction by combining AI personality diagnosis with the lives of great people or other users. The VR generation unit is implemented by the control unit 46A of the smart glasses 214, which virtualizes the created video, allowing users to virtually experience the past in VR. === Hard Collateral 1-3 === Each of the multiple elements described above, including the interview unit, material collection unit, generation unit, publication unit, multiverse generation unit, simulation unit, and VR generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the interview unit is implemented by the control unit 46A of the headset terminal 314 and collects user information in interview format. The material collection unit is implemented by the control unit 46A of the headset terminal 314 and collects materials such as photos, audio, and music provided by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a video based on the collected information. The publication unit is implemented by the specific processing unit 290 of the data processing unit 12 and publishes the generated video. The multiverse generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a life video incorporating variables into past events and choices. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs life prediction by combining AI personality diagnosis with the lives of great people or other users. The VR generation unit is implemented by the control unit 46A of the headset terminal 314, which virtualizes the created video and allows users to virtually experience the past in VR. === Hard Collateral 1-4 === Each of the multiple elements described above, including the interview unit, material collection unit, generation unit, publication unit, multiverse generation unit, simulation unit, and VR generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the interview unit is implemented by the control unit 46A of the robot 414 and collects user information in interview format. The material collection unit is implemented by the control unit 46A of the robot 414 and collects materials such as photos, audio, and music provided by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a video based on the collected information. The publication unit is implemented by the specific processing unit 290 of the data processing unit 12 and publishes the generated video. The multiverse generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a life video incorporating variables into past events and choices. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs life prediction by combining AI personality diagnosis with the lives of great people or other users. The VR generation unit is implemented by the control unit 46A of the robot 414, which transforms the created video into virtual reality, allowing users to virtually experience the past in VR.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] Social media platforms can also estimate a user's emotions and adjust the video editing style based on that estimation. For example, if a user is sad, the generative AI might edit the video using calming music and subdued colors. If a user is happy, the generative AI might edit the video using bright colors and lively music. Furthermore, if a user is stressed, the generative AI might edit the video using natural scenery and calming music to help them relax. This allows for the provision of video editing styles that respond to the user's emotions.

[0126] Social media platforms can also analyze a user's past behavior history and select the optimal video generation algorithm. For example, the generation AI can select the best algorithm based on the style and theme of videos the user has previously selected. It can also analyze the characteristics of videos that the user has previously rated highly and generate videos with similar characteristics. Furthermore, the generation AI can adjust to avoid video styles that the user has previously avoided. This allows for optimal video generation based on the user's past behavior history.

[0127] Social networking platforms can also incorporate relevant events and culture into videos based on the user's current geographical location. For example, if a user lives in a particular region, the video can include local festivals and events. If the user is traveling, the video can include tourist attractions and landmarks in their current location. Furthermore, if the user is considering moving, the video can include information and advice about the new area. This allows for the creation of videos that are tailored to the user's geographical location.

[0128] Social networking platforms can analyze users' social media activity and incorporate relevant topics and hashtags into videos. For example, they can generate videos based on events users have recently shared. They can also incorporate content related to topics and hashtags users follow. Furthermore, they can generate videos based on events and news that users have shown interest in on social media. This allows for the creation of videos that are tailored to users' social media activity.

[0129] Social media platforms can also estimate a user's emotions and adjust the timing of video releases based on those estimates. For example, if a user is relaxed, the generative AI will release the video at the optimal time. If a user is in a hurry, the generative AI can release the video quickly. Furthermore, if a user is excited, the generative AI can release the video during times when there are many viewers. This allows for adjusting video release timing according to the user's emotions.

[0130] Social media platforms can also estimate a user's emotions and adjust the video length based on that estimation. For example, if a user is relaxed, the generative AI can generate a longer video with detailed explanations. If a user is in a hurry, the generative AI can generate a short, to-the-point video. Furthermore, if a user is excited, the generative AI can generate a video with visually stimulating effects. This allows for adjusting the video length according to the user's emotions.

[0131] Social media platforms can also estimate a user's emotions and adjust the video's visibility based on those estimates. For example, if a user is relaxed, the generative AI might offer detailed visibility options. If a user is in a hurry, the generative AI might offer simpler options. Furthermore, if a user is excited, the generative AI might offer many more options. This allows for adjusting visibility according to the user's emotions.

[0132] Social media platforms can also estimate a user's emotions and select video themes based on those estimates. For example, if a user is sad, the generative AI will select an emotionally uplifting theme. If a user is happy, the generative AI can select a fun theme. Furthermore, if a user is stressed, the generative AI can select a relaxing theme. This allows for the provision of video themes that match the user's emotions.

[0133] Social media platforms can also analyze a user's past video viewing history and select the optimal video generation algorithm. For example, the generation AI can select the best algorithm based on the style and themes of videos the user has watched in the past. It can also analyze the characteristics of videos that the user has previously rated highly and generate videos with similar characteristics. Furthermore, the generation AI can adjust to avoid video styles that the user has previously avoided. This allows for optimal video generation based on the user's past video viewing history.

[0134] Social networking platforms can also incorporate relevant topics and themes into videos based on the user's current interests. For example, if a user is interested in their current profession, the video can include content related to that profession. Similarly, if a user has recently traveled, the video can include content related to that trip. Furthermore, if a user has started a new hobby, the video can include content related to that hobby. This allows for the creation of videos tailored to the user's current interests.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The interview department collects user information in an interview format. For example, information is collected by having users answer questions generated by a generative AI. The generative AI uses natural language generation AI or conversational AI to ask questions to the user and collect their answers. Step 2: The material collection unit collects materials provided by the user. For example, it collects materials such as photos, audio, and music provided by the user. The material collection unit collects photos in JPEG format, audio in MP3 format, music in WAV format, etc. Step 3: The generation unit generates a video based on the information collected by the interview unit and the material collection unit. For example, it generates a video based on the output format selected by the user. The generation unit generates a video based on output formats such as MP4, AVI, and GIF. Using a generation AI, it analyzes the user's input information and materials, and generates a video using generation algorithms such as deep learning and rule-based generation. Step 4: The publishing unit publishes the generated video. For example, it publishes the video based on the user's set privacy settings. The publishing unit publishes the video based on privacy settings such as private, public, or restricted.

[0137] 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.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0139] 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.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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.

[0172] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] 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.

[0189] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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. The interview department collects user information through interviews, The material collection department collects materials provided by users, A generation unit that generates a video based on the information collected by the interview unit and the material collection unit, The public section publishes the generated video, Equipped with A system characterized by the following features.

2. The aforementioned interview section, Information is collected by having users answer questions generated by AI. The system according to feature 1.

3. The aforementioned material collection unit is Collect at least one of the following materials provided by the user: photos, audio, or music. The system according to feature 1.

4. The generating unit is Generates a video based on the output format selected by the user. The system according to feature 1.

5. The aforementioned public section is, Publish the video based on the privacy settings set by the user. The system according to feature 1.

6. It features a multiverse generation unit that incorporates variables into past events or choices to generate a life video that differs from the present. The system according to feature 1.

7. It features a simulation section that combines AI personality assessment with the lives of great figures or other users to predict your future life. The system according to feature 1.

8. It features a VR generation unit that transforms created videos into virtual reality, allowing users to virtually experience the past in VR. The system according to feature 1.

9. The aforementioned interview section, The system estimates the user's emotions and adjusts the content or order of questions based on those emotions. The system according to feature 1.

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