system

Abstract

The system according to the embodiment aims to reproduce the speaking style and facial expressions of the participant and generate realistic video. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a video generation unit. The collection unit collects audio or video data of the participant. The analysis unit analyzes the data collected by the collection unit and reproduces the participant's speaking style or facial expression. The generation unit generates a model based on the data analyzed by the analysis unit. The video generation unit generates a video based on the model generated by the generation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has limited means for passing on the valuable experiences of survivors of wars, disasters, and other events to future generations, and there is a risk that these experiences will be lost forever.

[0005] The system according to the embodiment aims to reproduce the speaking style and facial expressions of the participant and generate realistic video. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a video generation unit. The collection unit collects audio or video data of the experiencer. The analysis unit analyzes the data collected by the collection unit and reproduces the speaking style or facial expression of the experiencer. The generation unit generates a model based on the data analyzed by the analysis unit. The video generation unit generates a video based on the model generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the speaking style and facial expressions of the participant and generate realistic video. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An experience reenactment system according to an embodiment of the present invention recreates the real experiences of survivors of events such as war, atomic bombing, leprosy, discrimination, earthquakes, and tsunamis, as if they were being spoken by a real person. This experience reenactment system aims to permanently preserve and pass on these experiences to future generations, given the current situation in which opportunities to hear directly from survivors are decreasing due to the aging population. Specifically, audio and video data from survivors is collected, and a generative AI analyzes this data to generate a model that reproduces the survivor's speaking style, facial expressions, nodding, blinking, and mouth movements. The generated model then generates a video in which the survivor speaks as if they were a real person, leaving a strong impression on viewers. For example, a video in which the survivor recounts a specific event is generated. This allows viewers to relive the survivor's real experience and gain a deeper understanding of history and social issues. This system is intended for use in educational institutions, museums, public facilities, and other settings, and serves as a powerful tool for passing on the valuable testimonies of survivors to future generations. This allows the experience reenactment system to recreate the survivor's real experience and pass it on to future generations.

[0029] An experience reproduction system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a video generation unit. The collection unit collects audio or video data of the experiencer. The audio or video data of the experiencer includes, but is not limited to, interview audio, everyday conversations, video messages, etc. The collection unit collects audio data of the experiencer in the form of an interview, for example. The collection unit can also record the experiencer's everyday conversations to collect natural speaking styles. The collection unit can also collect video data of the experiencer recording a video message. For example, the collection unit collects video messages in which the experiencer talks about a specific event. The analysis unit analyzes the data collected by the collection unit and reproduces the experiencer's speaking style or facial expression. The analysis is performed using, for example, voice recognition technology or facial expression recognition technology, but is not limited to, examples. For example, the analysis unit extracts characteristics of the experiencer's speaking style using voice recognition technology. The analysis unit can also analyze changes in the experiencer's facial expression using facial expression recognition technology. The analysis unit can also combine voice recognition technology and facial expression recognition technology to simultaneously analyze the experiencer's speaking style and facial expression. For example, the analysis unit analyzes the rhythm and intonation of the user's speech and reproduces it in combination with changes in facial expressions. The generation unit generates a model based on the data analyzed by the analysis unit. Model generation is performed using, for example, a machine learning algorithm or 3D modeling technology, but is not limited to these examples. For example, the generation unit generates a model that reproduces the user's speech and facial expressions using a machine learning algorithm. The generation unit can also generate a model that reproduces the user's facial movements using 3D modeling technology. Furthermore, the generation unit can combine a machine learning algorithm and 3D modeling technology to generate a model that simultaneously reproduces the user's speech and facial expressions. For example, the generation unit learns characteristics of the user's speech and reflects them in the 3D model. The video generation unit generates a video based on the model generated by the generation unit. Video generation is performed using, for example, rendering technology or animation technology, but is not limited to these examples. For example, the video generation unit generates a video that reproduces the user's speech and facial expressions using rendering technology.The video generation unit can also generate a video that reproduces the movements of the experiencer using animation technology. Furthermore, the video generation unit can combine rendering technology and animation technology to generate a video that simultaneously reproduces the speech and movements of the experiencer. For example, the video generation unit reproduces the rhythm and intonation of the experiencer's speech and generates a video that combines facial expressions and movements. In this way, the experience reproduction system according to the embodiment can reproduce the experiencer's realistic experience and pass it on to future generations.

[0030] The collection unit can analyze the experiencer's past interview history and select a collection method. For example, the collection unit prioritizes the selection of a collection method (audio, video, etc.) that the experiencer has previously preferred. The collection unit can also select the method that results in the most natural facial expressions and speaking style from the experiencer's past interview history. The collection unit can also analyze the experiencer's past interview history and customize the collection method to collect optimal data. For example, the collection unit stores the experiencer's past interview history in a database and uses it to select a collection method. The past interview history includes information such as the number of interviews, content, and duration. The collection unit analyzes this information and selects the optimal collection method for the experiencer. This allows the collection unit to select the optimal collection method based on the past interview history. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input past interview history data into a generation AI and have the generation AI select a collection method.

[0031] The collection unit may filter audio or video data based on the user's current health condition or environment when collecting the data. For example, if the user is not in good health, the collection unit may temporarily suspend collection and wait until the user's health improves. If the user's environment is noisy, the collection unit may postpone collection and resume collection in a quieter environment. Furthermore, the collection unit may filter the quality of collected data and select optimal data based on the user's health condition and environment. For example, the collection unit may evaluate the user's health condition using vital signs, self-reporting, medical data, etc. Vital signs are biometric data such as the user's heart rate, blood pressure, and body temperature, and are indicators for evaluating health status. Self-reporting is a method in which the user reports their own health condition and includes subjective assessments. Medical data is information such as the user's medical records and diagnosis results, providing an objective assessment. Environmental assessments include ambient noise levels, lighting conditions, location, etc. This allows the collection unit to collect optimal data according to the user's health condition and environment. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the experiencer's health status data and environmental data into the generation AI and have the generation AI perform filtering.

[0032] When collecting audio or video data, the collection unit can prioritize collecting highly relevant data based on the experiencer's geographical location information. For example, if the experiencer describes an experience in a specific location, the collection unit prioritizes collecting data related to that location. The collection unit can also collect related events and background information based on the experiencer's geographical location information. Furthermore, if the experiencer is moving, the collection unit can prioritize collecting data related to the experiencer's current location. For example, the collection unit acquires the experiencer's geographical location information using GPS data, address information, etc. GPS data is a technology for acquiring the experiencer's current location in real time and can accurately determine location information. Address information provides detailed information about a specific location when the experiencer is staying in that location. This allows the collection unit to collect optimal data based on the experiencer's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the experiencer's geographical location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0033] The collection unit can analyze the experiencer's social media activities and collect related data when collecting audio or video data. For example, the collection unit can analyze the experiencer's social media posts and collect related episodes. The collection unit can also collect data related to the experiencer's interests and concerns from the experiencer's social media activities. Furthermore, the collection unit can analyze the experiencer's social media followers and comments and collect related data. For example, the collection unit can collect the experiencer's social media activities as online activity data and use it for analysis. The online activity data includes the content of social media posts, the number of followers, engagement, etc. This allows the collection unit to collect optimal data based on the experiencer's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the experiencer's social media activity data into the generation AI and have the generation AI collect related data.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the experiencer's important episodes. For example, if the experiencer emphasizes a particular event in his or her recounting, the analysis unit performs a detailed analysis of that episode. Furthermore, if the experiencer recounts multiple episodes, the analysis unit can also adjust the level of detail of the analysis according to the importance of each episode. Furthermore, the analysis unit can add related background information and data to the analysis based on the experiencer's important episodes. For example, the analysis unit stores the experiencer's important episodes in a database and uses the data to adjust the level of detail of the analysis. Important episodes include events that the experiencer emphasizes or events that elicited strong emotional reactions. This allows the analysis unit to perform the analysis at an optimal level of detail according to the experiencer's important episodes. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the experiencer's important episode data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the experiencer. For example, in the case of a war experiencer, the analysis unit can apply a specific analysis algorithm related to war. In addition, in the case of an atomic bomb survivor, the analysis unit can apply a specific analysis algorithm related to atomic bombing. Furthermore, in the case of an earthquake or tsunami survivor, the analysis unit can apply a specific analysis algorithm related to natural disasters. For example, the analysis unit classifies the categories of experiencers based on criteria such as age, occupation, and interests, and applies an appropriate analysis algorithm to each category. This allows the analysis unit to apply the optimal analysis algorithm according to the category of experiencer. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category data of the experiencer into the generation AI and have the generation AI apply the analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time of the experiencer's interview. For example, if the experiencer's interview was conducted recently, the analysis unit prioritizes analysis of that data. Furthermore, if the experiencer's interview was conducted in the past, the analysis unit can also prioritize analysis based on the importance of the interview. Furthermore, the analysis unit can prioritize analysis of related data based on the time of the experiencer's interview. For example, the analysis unit stores the time of the experiencer's interview in a database and uses it to determine the analysis priority. The interview time includes information such as the frequency of interviews, the season, and whether the interview occurred before or after a specific event. This allows the analysis unit to perform analysis with optimal priority based on the time of the experiencer's interview. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the experiencer's interview time data into the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the experiencer. For example, if the relevance of the experiencer is high, the analysis unit prioritizes analysis of that data. Furthermore, if the relevance of the experiencer is low, the analysis unit can also adjust the order of analysis according to the importance. Furthermore, the analysis unit can prioritize analysis of related data based on the relevance of the experiencer. For example, the analysis unit stores the relevance of the experiencer in a database and uses it to adjust the order of analysis. The relevance includes information such as the influence of the experiencer and the uniqueness of the experience. This allows the analysis unit to perform analysis in an optimal order based on the relevance of the experiencer. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance data of the experiencer into the generation AI and have the generation AI adjust the order of analysis.

[0038] When generating a model, the generation unit can analyze the user's past behavioral patterns and select the optimal model generation method. For example, the generation unit can analyze the user's past behavioral patterns and generate a model that reproduces the most natural facial expressions and speaking style. The generation unit can also generate a model related to a specific episode from the user's past behavioral patterns. Furthermore, the generation unit can select the optimal model generation method based on the user's past behavioral patterns. For example, the generation unit stores the user's past behavioral patterns in a database and uses them to select model generation. The past behavioral patterns include information such as the frequency of behavior, the type of behavior, and the outcome of the behavior. This allows the generation unit to select the optimal model generation method based on the user's past behavioral patterns. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past behavioral pattern data into the generation AI and have the generation AI select a model generation method.

[0039] When generating a model, the generation unit can customize the means for generating the model based on the experiencer's current living situation. For example, the generation unit selects the optimal model generation means taking into account the experiencer's current living situation. The generation unit can also generate a model related to a specific episode based on the experiencer's current living situation. Furthermore, the generation unit can customize the means for generating the model based on the experiencer's current living situation. For example, the generation unit stores the experiencer's current living situation in a database and uses it to customize model generation. The current living situation includes information such as occupation, family environment, and health condition. This allows the generation unit to select the optimal model generation means based on the experiencer's current living situation. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data on the experiencer's current living situation into the generation AI and cause the generation AI to customize the model generation means.

[0040] When generating a model, the generation unit can select an optimal model generation method taking into account the geographical location information of the experiencer. For example, the generation unit generates a model that reproduces a related episode based on the geographical location information of the experiencer. The generation unit can also select an optimal model generation method taking into account the geographical location information of the experiencer. Furthermore, the generation unit can generate a model related to a specific episode based on the geographical location information of the experiencer. For example, the generation unit acquires the geographical location information of the experiencer using GPS data, address information, etc. GPS data is a technology for acquiring the experiencer's current location in real time and can accurately determine location information. Address information provides detailed information about a specific location when the experiencer is staying in that location. This allows the generation unit to select an optimal model generation method based on the experiencer's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the experiencer's geographical location information data to the generation AI and cause the generation AI to select a model generation method.

[0041] When generating a model, the generation unit can analyze the experiencer's social media activities and propose a means for generating the model. For example, the generation unit can analyze the experiencer's social media posts and generate a model that recreates related episodes. The generation unit can also generate a model related to a topic of interest or concern from the experiencer's social media activities. The generation unit can also analyze the experiencer's social media followers and comments and generate a related model. For example, the generation unit collects the experiencer's social media activities as online activity data and uses it for analysis. The online activity data includes the content of social media posts, the number of followers, engagement, etc. This allows the generation unit to propose an optimal model generation means based on the experiencer's social media activities. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the experiencer's social media activity data into the generation AI and have the generation AI execute a proposal for a means for generating a model.

[0042] When generating a video, the video generation unit can select the optimal video generation method by referring to the experiencer's past video data. For example, the video generation unit analyzes the experiencer's past video data and generates a video that reproduces the most natural facial expressions and speaking style. The video generation unit can also generate a video related to a specific episode from the experiencer's past video data. Furthermore, the video generation unit can select the optimal video generation method based on the experiencer's past video data. For example, the video generation unit stores the experiencer's past video data in a database and uses it to select a video generation method. The past video data includes information such as the type of video, the length of the video, and the content of the video. This allows the video generation unit to select the optimal video generation method based on the experiencer's past video data. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the experiencer's past video data into the generation AI and have the generation AI select a video generation method.

[0043] When generating a video, the video generation unit can customize the means for generating the video based on the experiencer's current living situation. For example, the video generation unit selects the optimal video generation means taking into account the experiencer's current living situation. The video generation unit can also generate a video related to a specific episode based on the experiencer's current living situation. Furthermore, the video generation unit can customize the means for generating the video based on the experiencer's current living situation. For example, the video generation unit stores the experiencer's current living situation in a database and uses it to customize video generation. The current living situation includes information such as occupation, family environment, and health condition. This allows the video generation unit to select the optimal video generation means based on the experiencer's current living situation. Some or all of the above-described processing in the video generation unit may be performed using, or without, a generation AI. For example, the video generation unit can input the experiencer's current living situation data into the generation AI and cause the generation AI to customize the video generation means.

[0044] When generating a video, the video generation unit can select an optimal video generation method by taking into account the geographical location information of the user. For example, the video generation unit generates a video that recreates a related episode based on the geographical location information of the user. The video generation unit can also select an optimal video generation method by taking into account the geographical location information of the user. Furthermore, the video generation unit can generate a video related to a specific episode based on the geographical location information of the user. For example, the video generation unit acquires the geographical location information of the user using GPS data, address information, etc. GPS data is a technology for acquiring the user's current location in real time and can accurately determine location information. Address information provides detailed information about a specific location when the user is staying in that location. This allows the video generation unit to select an optimal video generation method based on the user's geographical location information. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the user's geographical location information data to the generation AI and have the generation AI select a video generation method.

[0045] When generating a video, the video generation unit can analyze the experiencer's social media activities and suggest a means for generating the video. For example, the video generation unit can analyze the experiencer's social media posts and generate a video that recreates a related episode. The video generation unit can also generate videos related to topics of interest or concern based on the experiencer's social media activities. Furthermore, the video generation unit can analyze the experiencer's social media followers and comments and generate related videos. For example, the video generation unit collects the experiencer's social media activities as online activity data and uses it for analysis. The online activity data includes social media post content, number of followers, engagement, etc. This allows the video generation unit to suggest optimal video generation means based on the experiencer's social media activities. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the experiencer's social media activity data into the generation AI and have the generation AI execute a suggestion for a means for generating a video.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] When collecting audio or video data from participants, the collection unit can adjust the collection method taking into account the participants' cultural backgrounds and language differences. For example, if the participants speak a different language, the collection unit can provide an interpretation or translation function corresponding to that language. The collection unit can also select an appropriate interview format and questions based on the participants' cultural background. Furthermore, the collection unit can customize the interpretation and analysis methods of the collected data according to the participants' cultural backgrounds. This allows the collection unit to collect optimal data that corresponds to the participants' diverse cultural backgrounds and languages.

[0048] The collection unit can analyze the experiencer's past interview history and customize the content of the data to be collected. For example, additional questions can be prepared to supplement the details of episodes previously told by the experiencer. The collection unit can also create new questions about topics that the experiencer has not previously touched upon. Furthermore, the collection unit can prioritize the collection of data related to specific events from the experiencer's past interview history. This allows the collection unit to collect optimal data based on the experiencer's past interview history.

[0049] The collection unit can evaluate the experiencer's current psychological state when collecting audio or video data and adjust the collection method accordingly. For example, if the experiencer feels stressed, the collection unit can temporarily suspend collection and provide a relaxing environment. Alternatively, if the experiencer feels at ease, the collection unit can continue the interview and collect more detailed information. Furthermore, the collection unit can evaluate the quality of the collected data based on the experiencer's psychological state and select the most appropriate data. This allows the collection unit to collect the most appropriate data according to the experiencer's psychological state.

[0050] When collecting audio or video data, the collection unit can adjust the collection method based on the age or generation of the experiencer. For example, a collection method using a digital device can be provided for younger experiencers, while a traditional interview format can be used for older experiencers. Furthermore, the collection unit can customize the interpretation and analysis method of the collected data depending on the experiencer's generation. This allows the collection unit to collect optimal data corresponding to the experiencer's age and generation.

[0051] When collecting audio or video data, the collection unit can analyze the experiencer's social media activities and select the themes of the data to be collected. For example, the collection unit can prepare questions related to themes that the experiencer frequently mentions on social media. The collection unit can also identify themes that the experiencer is interested in from the experiencer's social media activities and customize the collected data based on those themes. Furthermore, the collection unit can analyze the experiencer's followers and comments on social media and collect related data. This allows the collection unit to collect optimal data based on the experiencer's social media activities.

[0052] During analysis, the analysis unit can adjust the visual representation of the analysis based on the experiencer's important episodes. For example, if the experiencer emphasizes a particular event in his or her recounting, the analysis unit can visually emphasize that episode. In addition, if the experiencer recounts multiple episodes, the analysis unit can adjust the level of detail in the visual representation according to the importance of each episode. Furthermore, the analysis unit can visually add related background information and data based on the experiencer's important episodes and perform the analysis. This allows the analysis unit to perform the analysis with the optimal visual representation according to the experiencer's important episodes.

[0053] During analysis, the analysis unit can select different methods of presenting the analysis results depending on the category of the experiencer. For example, for war veterans, the analysis results can be presented using specific visuals and graphs related to war. For atomic bomb survivors, the analysis results can be presented using specific visuals and graphs related to the atomic bombing. Furthermore, for earthquake and tsunami survivors, the analysis results can be presented using specific visuals and graphs related to natural disasters. This allows the analysis unit to select the optimal method of presenting the analysis results depending on the category of the experiencer.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects audio or video data of the experiencer. The audio or video data of the experiencer includes interview audio, everyday conversations, video messages, etc. For example, the collection unit can collect audio data of the experiencer in the form of an interview, record everyday conversations, and collect natural speaking patterns. It can also collect video messages in which the experiencer talks about a specific event. Step 2: The analysis unit analyzes the data collected by the collection unit and reproduces the user's speaking style or facial expressions. The analysis is performed using voice recognition technology and facial expression recognition technology. For example, the analysis unit can use voice recognition technology to extract characteristics of the user's speaking style and facial expression recognition technology to analyze changes in the user's facial expressions. Furthermore, the analysis unit can combine voice recognition technology and facial expression recognition technology to simultaneously analyze the user's speaking style and facial expressions. Step 3: The generation unit generates a model based on the data analyzed by the analysis unit. Model generation is performed using machine learning algorithms and 3D modeling technology. For example, the generation unit can use machine learning algorithms to generate a model that reproduces the user's speaking style and facial expressions, and 3D modeling technology to generate a model that reproduces the user's facial movements. Furthermore, the generation unit can combine machine learning algorithms and 3D modeling technology to generate a model that simultaneously reproduces the user's speaking style and facial expressions. Step 4: The video generation unit generates a video based on the model generated by the generation unit. The video is generated using rendering technology and animation technology. For example, the video generation unit can use rendering technology to generate a video that reproduces the user's way of speaking and facial expressions, and animation technology to generate a video that reproduces the user's movements. Furthermore, by combining rendering technology and animation technology, it is possible to generate a video that simultaneously reproduces the user's way of speaking and movements.

[0056] (Example 2) An experience reenactment system according to an embodiment of the present invention recreates the real experiences of survivors of events such as war, atomic bombing, leprosy, discrimination, earthquakes, and tsunamis, as if they were being spoken by a real person. This experience reenactment system aims to permanently preserve and pass on these experiences to future generations, given the current situation in which opportunities to hear directly from survivors are decreasing due to the aging population. Specifically, audio and video data from survivors is collected, and a generative AI analyzes this data to generate a model that reproduces the survivor's speaking style, facial expressions, nodding, blinking, and mouth movements. The generated model then generates a video in which the survivor speaks as if they were a real person, leaving a strong impression on viewers. For example, a video in which the survivor recounts a specific event is generated. This allows viewers to relive the survivor's real experience and gain a deeper understanding of history and social issues. This system is intended for use in educational institutions, museums, public facilities, and other settings, and serves as a powerful tool for passing on the valuable testimonies of survivors to future generations. This allows the experience reenactment system to recreate the survivor's real experience and pass it on to future generations.

[0057] An experience reproduction system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a video generation unit. The collection unit collects audio or video data of the experiencer. The audio or video data of the experiencer includes, but is not limited to, interview audio, everyday conversations, video messages, etc. The collection unit collects audio data of the experiencer in the form of an interview, for example. The collection unit can also record the experiencer's everyday conversations to collect natural speaking styles. The collection unit can also collect video data of the experiencer recording a video message. For example, the collection unit collects video messages in which the experiencer talks about a specific event. The analysis unit analyzes the data collected by the collection unit and reproduces the experiencer's speaking style or facial expression. The analysis is performed using, for example, voice recognition technology or facial expression recognition technology, but is not limited to, examples. For example, the analysis unit extracts characteristics of the experiencer's speaking style using voice recognition technology. The analysis unit can also analyze changes in the experiencer's facial expression using facial expression recognition technology. The analysis unit can also combine voice recognition technology and facial expression recognition technology to simultaneously analyze the experiencer's speaking style and facial expression. For example, the analysis unit analyzes the rhythm and intonation of the user's speech and reproduces it in combination with changes in facial expressions. The generation unit generates a model based on the data analyzed by the analysis unit. Model generation is performed using, for example, a machine learning algorithm or 3D modeling technology, but is not limited to these examples. For example, the generation unit generates a model that reproduces the user's speech and facial expressions using a machine learning algorithm. The generation unit can also generate a model that reproduces the user's facial movements using 3D modeling technology. Furthermore, the generation unit can combine a machine learning algorithm and 3D modeling technology to generate a model that simultaneously reproduces the user's speech and facial expressions. For example, the generation unit learns characteristics of the user's speech and reflects them in the 3D model. The video generation unit generates a video based on the model generated by the generation unit. Video generation is performed using, for example, rendering technology or animation technology, but is not limited to these examples. For example, the video generation unit generates a video that reproduces the user's speech and facial expressions using rendering technology.The video generation unit can also generate a video that reproduces the movements of the experiencer using animation technology. Furthermore, the video generation unit can combine rendering technology and animation technology to generate a video that simultaneously reproduces the speech and movements of the experiencer. For example, the video generation unit reproduces the rhythm and intonation of the experiencer's speech and generates a video that combines facial expressions and movements. In this way, the experience reproduction system according to the embodiment can reproduce the experiencer's realistic experience and pass it on to future generations.

[0058] The collection unit can estimate the emotions of the user and adjust the timing of collecting audio or video data based on the estimated emotions of the user. For example, if the user is emotionally aroused, the collection unit can delay the collection timing and wait until the user calms down. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to collect natural facial expressions and speaking patterns. Furthermore, if the user is tired, the collection unit can adjust the collection timing to collect data while taking breaks. For example, the collection unit estimates the user's emotions using technologies such as voice analysis, facial expression analysis, and biometrics. Voice analysis is a technology that analyzes changes in the tone, speed, and volume of the user's voice to estimate emotions. Facial expression analysis is a technology that analyzes changes in the user's facial expressions to estimate emotions. Biometrics is a technology that analyzes biometric data such as the user's heart rate and electrodermal activity to estimate emotions. This allows the collection unit to collect data at the optimal timing according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the emotion data of the user into the generation AI and cause the generation AI to adjust the collection timing.

[0059] The collection unit can analyze the experiencer's past interview history and select a collection method. For example, the collection unit prioritizes the selection of a collection method (audio, video, etc.) that the experiencer has previously preferred. The collection unit can also select the method that results in the most natural facial expressions and speaking style from the experiencer's past interview history. The collection unit can also analyze the experiencer's past interview history and customize the collection method to collect optimal data. For example, the collection unit stores the experiencer's past interview history in a database and uses it to select a collection method. The past interview history includes information such as the number of interviews, content, and duration. The collection unit analyzes this information and selects the optimal collection method for the experiencer. This allows the collection unit to select the optimal collection method based on the past interview history. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input past interview history data into a generation AI and have the generation AI select a collection method.

[0060] The collection unit may filter audio or video data based on the user's current health condition or environment when collecting the data. For example, if the user is not in good health, the collection unit may temporarily suspend collection and wait until the user's health improves. If the user's environment is noisy, the collection unit may postpone collection and resume collection in a quieter environment. Furthermore, the collection unit may filter the quality of collected data and select optimal data based on the user's health condition and environment. For example, the collection unit may evaluate the user's health condition using vital signs, self-reporting, medical data, etc. Vital signs are biometric data such as the user's heart rate, blood pressure, and body temperature, and are indicators for evaluating health status. Self-reporting is a method in which the user reports their own health condition and includes subjective assessments. Medical data is information such as the user's medical records and diagnosis results, providing an objective assessment. Environmental assessments include ambient noise levels, lighting conditions, location, etc. This allows the collection unit to collect optimal data according to the user's health condition and environment. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the experiencer's health status data and environmental data into the generation AI and have the generation AI perform filtering.

[0061] The collection unit can estimate the user's emotions and prioritize data collection based on the estimated user's emotions. For example, if the user is emotionally charged, the collection unit can prioritize collecting emotional episodes. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed descriptions and background information. Furthermore, if the user is tired, the collection unit can prioritize collecting short episodes and key points. For example, the collection unit can estimate the user's emotions using technologies such as voice analysis, facial expression analysis, and biometrics. Voice analysis is a technology that analyzes changes in the user's voice tone, speed, and volume to estimate emotions. Facial expression analysis is a technology that analyzes changes in the user's facial expressions to estimate emotions. Biometrics is a technology that analyzes biometric data such as the user's heart rate and electrodermal activity to estimate emotions. This allows the collection unit to prioritize collecting optimal data according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input emotional data of the experiencer into the generation AI and have the generation AI determine the priority of the data.

[0062] When collecting audio or video data, the collection unit can prioritize collecting highly relevant data based on the experiencer's geographical location information. For example, if the experiencer describes an experience in a specific location, the collection unit prioritizes collecting data related to that location. The collection unit can also collect related events and background information based on the experiencer's geographical location information. Furthermore, if the experiencer is moving, the collection unit can prioritize collecting data related to the experiencer's current location. For example, the collection unit acquires the experiencer's geographical location information using GPS data, address information, etc. GPS data is a technology for acquiring the experiencer's current location in real time and can accurately determine location information. Address information provides detailed information about a specific location when the experiencer is staying in that location. This allows the collection unit to collect optimal data based on the experiencer's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the experiencer's geographical location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0063] The collection unit can analyze the experiencer's social media activities and collect related data when collecting audio or video data. For example, the collection unit can analyze the experiencer's social media posts and collect related episodes. The collection unit can also collect data related to the experiencer's interests and concerns from the experiencer's social media activities. Furthermore, the collection unit can analyze the experiencer's social media followers and comments and collect related data. For example, the collection unit can collect the experiencer's social media activities as online activity data and use it for analysis. The online activity data includes the content of social media posts, the number of followers, engagement, etc. This allows the collection unit to collect optimal data based on the experiencer's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the experiencer's social media activity data into the generation AI and have the generation AI collect related data.

[0064] The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the estimated user's emotions. For example, if the user is emotionally excited, the analysis unit can emphasize emotional expressions in the analysis. Furthermore, if the user is relaxed, the analysis unit can provide an analysis that includes detailed explanations and background information. Furthermore, if the user is tired, the analysis unit can provide a concise, to-the-point analysis. For example, the analysis unit can estimate the user's emotions using technologies such as voice analysis, facial expression analysis, and biometrics. Voice analysis is a technology that analyzes changes in the user's voice tone, speed, and volume to estimate emotions. Facial expression analysis is a technology that analyzes changes in the user's facial expressions to estimate emotions. Biometrics is a technology that analyzes biometric data such as the user's heart rate and electrodermal activity to estimate emotions. This allows the analysis unit to perform analysis using the optimal expression method according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the emotional data of the user into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0065] During analysis, the analysis unit can adjust the level of detail of the analysis based on the experiencer's important episodes. For example, if the experiencer emphasizes a particular event in his or her recounting, the analysis unit performs a detailed analysis of that episode. Furthermore, if the experiencer recounts multiple episodes, the analysis unit can also adjust the level of detail of the analysis according to the importance of each episode. Furthermore, the analysis unit can add related background information and data to the analysis based on the experiencer's important episodes. For example, the analysis unit stores the experiencer's important episodes in a database and uses the data to adjust the level of detail of the analysis. Important episodes include events that the experiencer emphasizes or events that elicited strong emotional reactions. This allows the analysis unit to perform the analysis at an optimal level of detail according to the experiencer's important episodes. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the experiencer's important episode data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0066] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the experiencer. For example, in the case of a war experiencer, the analysis unit can apply a specific analysis algorithm related to war. In addition, in the case of an atomic bomb survivor, the analysis unit can apply a specific analysis algorithm related to atomic bombing. Furthermore, in the case of an earthquake or tsunami survivor, the analysis unit can apply a specific analysis algorithm related to natural disasters. For example, the analysis unit classifies the categories of experiencers based on criteria such as age, occupation, and interests, and applies an appropriate analysis algorithm to each category. This allows the analysis unit to apply the optimal analysis algorithm according to the category of experiencer. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category data of the experiencer into the generation AI and have the generation AI apply the analysis algorithm.

[0067] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is emotionally excited, the analysis unit can analyze the emotional parts in a longer period. Furthermore, if the user is relaxed, the analysis unit can perform a longer analysis including detailed explanations. Furthermore, if the user is tired, the analysis unit can perform a short analysis that is concise and to the point. For example, the analysis unit can estimate the user's emotions using technologies such as voice analysis, facial expression analysis, and biometrics. Voice analysis is a technology that analyzes changes in the user's voice tone, speed, and volume to estimate emotions. Facial expression analysis is a technology that analyzes changes in the user's facial expressions to estimate emotions. Biometrics is a technology that analyzes biometric data such as the user's heart rate and electrodermal activity to estimate emotions. This allows the analysis unit to perform the analysis at an optimal length depending on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the emotional data of the user into the generation AI and have the generation AI adjust the length of the analysis.

[0068] During analysis, the analysis unit can determine the priority of analysis based on the time of the experiencer's interview. For example, if the experiencer's interview was conducted recently, the analysis unit prioritizes analysis of that data. Furthermore, if the experiencer's interview was conducted in the past, the analysis unit can also prioritize analysis based on the importance of the interview. Furthermore, the analysis unit can prioritize analysis of related data based on the time of the experiencer's interview. For example, the analysis unit stores the time of the experiencer's interview in a database and uses it to determine the analysis priority. The interview time includes information such as the frequency of interviews, the season, and whether the interview occurred before or after a specific event. This allows the analysis unit to perform analysis with optimal priority based on the time of the experiencer's interview. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the experiencer's interview time data into the generation AI and have the generation AI determine the analysis priority.

[0069] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the experiencer. For example, if the relevance of the experiencer is high, the analysis unit prioritizes analysis of that data. Furthermore, if the relevance of the experiencer is low, the analysis unit can also adjust the order of analysis according to the importance. Furthermore, the analysis unit can prioritize analysis of related data based on the relevance of the experiencer. For example, the analysis unit stores the relevance of the experiencer in a database and uses it to adjust the order of analysis. The relevance includes information such as the influence of the experiencer and the uniqueness of the experience. This allows the analysis unit to perform analysis in an optimal order based on the relevance of the experiencer. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance data of the experiencer into the generation AI and have the generation AI adjust the order of analysis.

[0070] The generation unit can estimate the user's emotions and adjust the model generation method based on the estimated user's emotions. For example, if the user is emotionally excited, the generation unit generates a model that emphasizes emotional expressions. Furthermore, if the user is relaxed, the generation unit can generate a model that reproduces natural facial expressions and speaking styles. Furthermore, if the user is tired, the generation unit can generate a concise, to-the-point model. For example, the generation unit estimates the user's emotions using technologies such as voice analysis, facial expression analysis, and biometrics. Voice analysis is a technology that analyzes changes in the user's voice tone, speed, and volume to estimate emotions. Facial expression analysis is a technology that analyzes changes in the user's facial expressions to estimate emotions. Biometrics is a technology that analyzes biometric data such as the user's heart rate and electrodermal activity to estimate emotions. This allows the generation unit to generate a model in an optimal manner according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input emotional data of the user into the generation AI and cause the generation AI to adjust the model generation method.

[0071] When generating a model, the generation unit can analyze the user's past behavioral patterns and select the optimal model generation method. For example, the generation unit can analyze the user's past behavioral patterns and generate a model that reproduces the most natural facial expressions and speaking style. The generation unit can also generate a model related to a specific episode from the user's past behavioral patterns. Furthermore, the generation unit can select the optimal model generation method based on the user's past behavioral patterns. For example, the generation unit stores the user's past behavioral patterns in a database and uses them to select model generation. The past behavioral patterns include information such as the frequency of behavior, the type of behavior, and the outcome of the behavior. This allows the generation unit to select the optimal model generation method based on the user's past behavioral patterns. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past behavioral pattern data into the generation AI and have the generation AI select a model generation method.

[0072] When generating a model, the generation unit can customize the means for generating the model based on the experiencer's current living situation. For example, the generation unit selects the optimal model generation means taking into account the experiencer's current living situation. The generation unit can also generate a model related to a specific episode based on the experiencer's current living situation. Furthermore, the generation unit can customize the means for generating the model based on the experiencer's current living situation. For example, the generation unit stores the experiencer's current living situation in a database and uses it to customize model generation. The current living situation includes information such as occupation, family environment, and health condition. This allows the generation unit to select the optimal model generation means based on the experiencer's current living situation. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data on the experiencer's current living situation into the generation AI and cause the generation AI to customize the model generation means.

[0073] The generation unit can estimate the emotions of the user and determine the priority of model generation based on the estimated emotions of the user. For example, if the user is emotionally charged, the generation unit can prioritize generating models based on emotional episodes. Furthermore, if the user is relaxed, the generation unit can prioritize generating models that include detailed explanations and background information. Furthermore, if the user is tired, the generation unit can prioritize generating models that are concise and to the point. For example, the generation unit can estimate the user's emotions using technologies such as voice analysis, facial expression analysis, and biometrics. Voice analysis is a technology that analyzes changes in the tone, speed, and volume of the user's voice to estimate emotions. Facial expression analysis is a technology that analyzes changes in the user's facial expressions to estimate emotions. Biometrics is a technology that analyzes biological data such as the user's heart rate and electrodermal activity to estimate emotions. This allows the generation unit to generate models with optimal priorities according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input emotion data of the user to the generation AI and have the generation AI determine the priority of model generation.

[0074] When generating a model, the generation unit can select an optimal model generation method taking into account the geographical location information of the experiencer. For example, the generation unit generates a model that reproduces a related episode based on the geographical location information of the experiencer. The generation unit can also select an optimal model generation method taking into account the geographical location information of the experiencer. Furthermore, the generation unit can generate a model related to a specific episode based on the geographical location information of the experiencer. For example, the generation unit acquires the geographical location information of the experiencer using GPS data, address information, etc. GPS data is a technology for acquiring the experiencer's current location in real time and can accurately determine location information. Address information provides detailed information about a specific location when the experiencer is staying in that location. This allows the generation unit to select an optimal model generation method based on the experiencer's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the experiencer's geographical location information data to the generation AI and cause the generation AI to select a model generation method.

[0075] When generating a model, the generation unit can analyze the experiencer's social media activities and propose a means for generating the model. For example, the generation unit can analyze the experiencer's social media posts and generate a model that recreates related episodes. The generation unit can also generate a model related to a topic of interest or concern from the experiencer's social media activities. The generation unit can also analyze the experiencer's social media followers and comments and generate a related model. For example, the generation unit collects the experiencer's social media activities as online activity data and uses it for analysis. The online activity data includes the content of social media posts, the number of followers, engagement, etc. This allows the generation unit to propose an optimal model generation means based on the experiencer's social media activities. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the experiencer's social media activity data into the generation AI and have the generation AI execute a proposal for a means for generating a model.

[0076] The video generation unit can estimate the emotions of the user and adjust the video generation method based on the estimated emotions of the user. For example, if the user is emotionally excited, the video generation unit generates a video that emphasizes emotional expressions. Furthermore, if the user is relaxed, the video generation unit can generate a video that reproduces natural facial expressions and speaking style. Furthermore, if the user is tired, the video generation unit can generate a concise video that focuses on the main points. For example, the video generation unit estimates the user's emotions using technologies such as voice analysis, facial expression analysis, and biometrics. Voice analysis is a technology that analyzes changes in the tone, speed, and volume of the user's voice to estimate emotions. Facial expression analysis is a technology that analyzes changes in the user's facial expressions to estimate emotions. Biometrics is a technology that analyzes biometric data such as the user's heart rate and electrodermal activity to estimate emotions. This allows the video generation unit to generate a video in an optimal manner according to the user's emotions. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit may input emotional data of the user into the generation AI and cause the generation AI to adjust the video generation method.

[0077] When generating a video, the video generation unit can select the optimal video generation method by referring to the experiencer's past video data. For example, the video generation unit analyzes the experiencer's past video data and generates a video that reproduces the most natural facial expressions and speaking style. The video generation unit can also generate a video related to a specific episode from the experiencer's past video data. Furthermore, the video generation unit can select the optimal video generation method based on the experiencer's past video data. For example, the video generation unit stores the experiencer's past video data in a database and uses it to select a video generation method. The past video data includes information such as the type of video, the length of the video, and the content of the video. This allows the video generation unit to select the optimal video generation method based on the experiencer's past video data. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the experiencer's past video data into the generation AI and have the generation AI select a video generation method.

[0078] When generating a video, the video generation unit can customize the means for generating the video based on the experiencer's current living situation. For example, the video generation unit selects the optimal video generation means taking into account the experiencer's current living situation. The video generation unit can also generate a video related to a specific episode based on the experiencer's current living situation. Furthermore, the video generation unit can customize the means for generating the video based on the experiencer's current living situation. For example, the video generation unit stores the experiencer's current living situation in a database and uses it to customize video generation. The current living situation includes information such as occupation, family environment, and health condition. This allows the video generation unit to select the optimal video generation means based on the experiencer's current living situation. Some or all of the above-described processing in the video generation unit may be performed using, or without, a generation AI. For example, the video generation unit can input the experiencer's current living situation data into the generation AI and cause the generation AI to customize the video generation means.

[0079] The video generation unit can estimate the emotions of the user and determine the priority of video generation based on the estimated emotions of the user. For example, if the user is emotionally charged, the video generation unit can prioritize generating videos of emotional episodes. Furthermore, if the user is relaxed, the video generation unit can prioritize generating videos that include detailed explanations and background information. Furthermore, if the user is tired, the video generation unit can prioritize generating videos that are concise and to the point. For example, the video generation unit can estimate the emotions of the user using technologies such as voice analysis, facial expression analysis, and biometrics. Voice analysis is a technology that analyzes changes in the tone, speed, and volume of the user's voice to estimate emotions. Facial expression analysis is a technology that analyzes changes in the user's facial expressions to estimate emotions. Biometrics is a technology that analyzes biological data such as the user's heart rate and electrodermal activity to estimate emotions. This allows the video generation unit to generate videos with optimal priorities according to the user's emotions. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit may input emotion data of the user to the generation AI and have the generation AI determine the priority of video generation.

[0080] When generating a video, the video generation unit can select an optimal video generation method by taking into account the geographical location information of the user. For example, the video generation unit generates a video that recreates a related episode based on the geographical location information of the user. The video generation unit can also select an optimal video generation method by taking into account the geographical location information of the user. Furthermore, the video generation unit can generate a video related to a specific episode based on the geographical location information of the user. For example, the video generation unit acquires the geographical location information of the user using GPS data, address information, etc. GPS data is a technology for acquiring the user's current location in real time and can accurately determine location information. Address information provides detailed information about a specific location when the user is staying in that location. This allows the video generation unit to select an optimal video generation method based on the user's geographical location information. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the user's geographical location information data to the generation AI and have the generation AI select a video generation method.

[0081] When generating a video, the video generation unit can analyze the experiencer's social media activities and suggest a means for generating the video. For example, the video generation unit can analyze the experiencer's social media posts and generate a video that recreates a related episode. The video generation unit can also generate videos related to topics of interest or concern based on the experiencer's social media activities. Furthermore, the video generation unit can analyze the experiencer's social media followers and comments and generate related videos. For example, the video generation unit collects the experiencer's social media activities as online activity data and uses it for analysis. The online activity data includes social media post content, number of followers, engagement, etc. This allows the video generation unit to suggest optimal video generation means based on the experiencer's social media activities. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the experiencer's social media activity data into the generation AI and have the generation AI execute a suggestion for a means for generating a video. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and video generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects audio and video data of the experiencer using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to reproduce the experiencer's speaking style and facial expressions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a model based on the analyzed data. The video generation unit is realized, for example, by the control unit 46A of the smart device 14 and generates a video based on the generated model. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and video generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects audio and video data of the experiencer using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to reproduce the experiencer's speaking style and facial expressions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a model based on the analyzed data. The video generation unit is realized, for example, by the control unit 46A of the smart glasses 214, and generates a video based on the generated model. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and video generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects audio and video data of the experiencer using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to reproduce the speaking style and facial expressions of the experiencer. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a model based on the analyzed data. The video generation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and generates a video based on the generated model. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and video generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects audio and video data of the experiencer using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to reproduce the experiencer's speaking style and facial expressions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a model based on the analyzed data. The video generation unit is realized, for example, by the control unit 46A of the robot 414, and generates a video based on the generated model.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] When collecting audio or video data from participants, the collection unit can adjust the collection method taking into account the participants' cultural backgrounds and language differences. For example, if the participants speak a different language, the collection unit can provide an interpretation or translation function corresponding to that language. The collection unit can also select an appropriate interview format and questions based on the participants' cultural background. Furthermore, the collection unit can customize the interpretation and analysis methods of the collected data according to the participants' cultural backgrounds. This allows the collection unit to collect optimal data that corresponds to the participants' diverse cultural backgrounds and languages.

[0084] The collection unit can estimate the emotions of the participant and select the format of data to collect based on the estimated emotions of the participant. For example, if the participant is emotionally excited, audio data can be collected preferentially to capture the nuances of the emotion. If the participant is relaxed, video data can be collected preferentially to record natural facial expressions and movements. Furthermore, if the participant is tired, short audio messages or text data can be collected. This allows the collection unit to select the optimal data format according to the participant's emotions.

[0085] The collection unit can analyze the experiencer's past interview history and customize the content of the data to be collected. For example, additional questions can be prepared to supplement the details of episodes previously told by the experiencer. The collection unit can also create new questions about topics that the experiencer has not previously touched upon. Furthermore, the collection unit can prioritize the collection of data related to specific events from the experiencer's past interview history. This allows the collection unit to collect optimal data based on the experiencer's past interview history.

[0086] The collection unit can evaluate the experiencer's current psychological state when collecting audio or video data and adjust the collection method accordingly. For example, if the experiencer feels stressed, the collection unit can temporarily suspend collection and provide a relaxing environment. Alternatively, if the experiencer feels at ease, the collection unit can continue the interview and collect more detailed information. Furthermore, the collection unit can evaluate the quality of the collected data based on the experiencer's psychological state and select the most appropriate data. This allows the collection unit to collect the most appropriate data according to the experiencer's psychological state.

[0087] The collection unit can estimate the emotions of the experiencer and adjust the depth of data to be collected based on the estimated emotions of the experiencer. For example, if the experiencer is emotionally charged, it can dig deep and collect emotional episodes. If the experiencer is relaxed, it can collect detailed background information and explanations. Furthermore, if the experiencer is tired, it can collect concise, to-the-point data. This allows the collection unit to adjust the optimal depth of data according to the experiencer's emotions.

[0088] When collecting audio or video data, the collection unit can adjust the collection method based on the age or generation of the experiencer. For example, a collection method using a digital device can be provided for younger experiencers, while a traditional interview format can be used for older experiencers. Furthermore, the collection unit can customize the interpretation and analysis method of the collected data depending on the experiencer's generation. This allows the collection unit to collect optimal data corresponding to the experiencer's age and generation.

[0089] When collecting audio or video data, the collection unit can analyze the experiencer's social media activities and select the themes of the data to be collected. For example, the collection unit can prepare questions related to themes that the experiencer frequently mentions on social media. The collection unit can also identify themes that the experiencer is interested in from the experiencer's social media activities and customize the collected data based on those themes. Furthermore, the collection unit can analyze the experiencer's followers and comments on social media and collect related data. This allows the collection unit to collect optimal data based on the experiencer's social media activities.

[0090] The analysis unit can estimate the emotions of the user and adjust the visual representation of the analysis based on the estimated emotions of the user. For example, if the user is emotionally excited, graphics or animations that emphasize emotional expressions can be used. If the user is relaxed, visual representations that include detailed explanations and background information can be used. Furthermore, if the user is tired, visual representations that are concise and to the point can be used. This allows the analysis unit to perform analysis using optimal visual representations according to the user's emotions.

[0091] During analysis, the analysis unit can adjust the visual representation of the analysis based on the experiencer's important episodes. For example, if the experiencer emphasizes a particular event in his or her recounting, the analysis unit can visually emphasize that episode. In addition, if the experiencer recounts multiple episodes, the analysis unit can adjust the level of detail in the visual representation according to the importance of each episode. Furthermore, the analysis unit can visually add related background information and data based on the experiencer's important episodes and perform the analysis. This allows the analysis unit to perform the analysis with the optimal visual representation according to the experiencer's important episodes.

[0092] During analysis, the analysis unit can select different methods of presenting the analysis results depending on the category of the experiencer. For example, for war veterans, the analysis results can be presented using specific visuals and graphs related to war. For atomic bomb survivors, the analysis results can be presented using specific visuals and graphs related to the atomic bombing. Furthermore, for earthquake and tsunami survivors, the analysis results can be presented using specific visuals and graphs related to natural disasters. This allows the analysis unit to select the optimal method of presenting the analysis results depending on the category of the experiencer.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The collection unit collects audio or video data of the experiencer. The audio or video data of the experiencer includes interview audio, everyday conversations, video messages, etc. For example, the collection unit can collect audio data of the experiencer in the form of an interview, record everyday conversations, and collect natural speaking patterns. It can also collect video messages in which the experiencer talks about a specific event. Step 2: The analysis unit analyzes the data collected by the collection unit and reproduces the user's speaking style or facial expressions. The analysis is performed using voice recognition technology and facial expression recognition technology. For example, the analysis unit can use voice recognition technology to extract characteristics of the user's speaking style and facial expression recognition technology to analyze changes in the user's facial expressions. Furthermore, the analysis unit can combine voice recognition technology and facial expression recognition technology to simultaneously analyze the user's speaking style and facial expressions. Step 3: The generation unit generates a model based on the data analyzed by the analysis unit. Model generation is performed using machine learning algorithms and 3D modeling technology. For example, the generation unit can use machine learning algorithms to generate a model that reproduces the user's speaking style and facial expressions, and 3D modeling technology to generate a model that reproduces the user's facial movements. Furthermore, the generation unit can combine machine learning algorithms and 3D modeling technology to generate a model that simultaneously reproduces the user's speaking style and facial expressions. Step 4: The video generation unit generates a video based on the model generated by the generation unit. The video is generated using rendering technology and animation technology. For example, the video generation unit can use rendering technology to generate a video that reproduces the user's way of speaking and facial expressions, and animation technology to generate a video that reproduces the user's movements. Furthermore, by combining rendering technology and animation technology, it is possible to generate a video that simultaneously reproduces the user's way of speaking and movements.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that collects audio or video data of an experiencer; an analysis unit that analyzes the data collected by the collection unit and reproduces the experiencer's speaking style or facial expressions; a generation unit that generates a model based on the data analyzed by the analysis unit; and a video generation unit that generates a video based on the model generated by the generation unit.

2. The system according to claim 1 , wherein the collection unit estimates an emotion of the experiencer and adjusts the timing of collecting the audio or video data based on the estimated emotion of the experiencer.

3. 2. The system according to claim 1, wherein the collection unit analyzes the past interview history of the experiencer and selects a collection method.

4. The system according to claim 1 , wherein the collection unit performs filtering based on the current health condition or environment of the experiencer when collecting audio or video data.

5. The system according to claim 1 , wherein the collection unit estimates an emotion of the experiencer and determines a priority of data to be collected based on the estimated emotion of the experiencer.

6. The system according to claim 1 , wherein the collection unit, when collecting audio or video data, prioritizes collection of highly relevant data based on geographical location information of the user.

7. The system according to claim 1 , wherein the collection unit analyzes online activities of the user and collects related data when collecting audio or video data.

8. The system according to claim 1 , wherein the analysis unit estimates the emotion of the user and adjusts the method of expressing the analysis based on the estimated emotion of the user.

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