System

Abstract

The system according to the embodiment aims to effectively visualize a family history or a specific event.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and an imaging unit. The collection unit collects a family tree, a chronology, a photograph, and questionnaire data. The analyzer analyzes the data collected by the collector to identify family history and specific events. The generation unit generates a storyboard based on the information identified by the analysis unit. The visualization unit visualizes the storyboard generated by the generation unit.SELECTED DRAWING: Figure 1

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 had the challenge of making it difficult to effectively visualize family history or specific events.

[0005] The system according to the embodiment aims to effectively visualize family history and specific events. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a visualization unit. The collection unit collects family trees, timelines, photographs, and questionnaire data. The analysis unit analyzes the data collected by the collection unit to identify family history and specific events. The generation unit generates a storyboard based on the information identified by the analysis unit. The visualization unit visualizes the storyboard generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively visualize family history and specific events. [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) A system according to an embodiment of the present invention visualizes the life stories of individuals and families based on family trees, timelines, photographs, and survey data. This system collects family trees, timelines, photographs, and survey data, analyzes them using AI to identify family history and important events, and generates and visualizes a storyboard. The system also provides a simple service using a messaging app, generating an interviewer AI with which users can easily converse. For example, the system collects family trees, timelines, photographs, and survey data, analyzes them using AI to identify family history and important events. Next, the AI ​​generates and visualizes a storyboard. Furthermore, the system provides a service that allows users to easily check family history and important events using a messaging app. Finally, the system generates an interviewer AI with which users can easily converse, providing a friendly conversation. This allows the system to visualize the life stories of individuals and families based on family trees, timelines, photographs, and survey data. For example, family history and important events can be conveyed visually and clearly. Furthermore, family history and important events can be easily checked through a messaging app. Furthermore, the system generates an interviewer AI with which users can easily converse, providing a friendly conversation.

[0029] A storyboard generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a visualization unit. The collection unit collects family trees, timelines, photographs, and questionnaire data. For example, the collection unit inputs the family trees in digital format. The collection unit can also manually input the timelines. The collection unit can also scan photographs and convert them into digital data. The collection unit can also collect questionnaire data using an online form. The analysis unit analyzes the data collected by the collection unit to identify family history and important events. For example, the analysis unit identifies family structure and relationships from the family tree. The analysis unit can also identify dates and times of important events from the timeline. The analysis unit can also analyze family appearances and scenery from photographs. The analysis unit can also extract family memories and episodes from the questionnaire data. The generation unit generates a storyboard based on the information identified by the analysis unit. For example, the generation unit generates a diagram showing family structure and relationships. The generation unit can also generate a reenactment video of important events. The generation unit can also incorporate photos of family members and scenery into the storyboard. The generation unit can also generate narration of memories and episodes. The visualization unit visualizes the storyboard generated by the generation unit. For example, the visualization unit visualizes a diagram showing family structure and relationships. The visualization unit can also visualize a reenactment of an important event. The visualization unit can also visualize photos of family members and scenery. The visualization unit can also visualize narration of memories and episodes. In this way, the storyboard generation system according to the embodiment can visualize the life story of an individual or family based on a family tree, a chronology, photos, and questionnaire data.

[0030] The storyboard generation system includes a providing unit that provides a simple service using a messaging app. The providing unit provides the simple service using a messaging app. For example, the providing unit notifies the user of family history or important events through the messaging app. The providing unit can also display the family structure and relationships through the messaging app. The providing unit can also display the dates and times of important events through the messaging app. The providing unit can also display photos of family members and scenery through the messaging app. The providing unit can also display memories and episodes through the messaging app. This allows the user to easily check family history and important events through the messaging app.

[0031] The storyboard generation system includes a generation unit that generates an interviewer AI with which the user can easily converse. The generation unit generates an interviewer AI with which the user can easily converse. For example, the generation unit generates an AI avatar that reads data on the user's grandchild. The generation unit can also generate an AI that recognizes the user's voice and engages in natural conversation. The generation unit can also generate an AI that recognizes the user's facial expressions and engages in conversation according to their emotions. This allows the generation of an interviewer AI with which the user can converse easily, providing a friendly conversation.

[0032] The collection unit can automatically detect past events and important happenings of the family and determine the priority of data to be collected. For example, the collection unit prioritizes collection of important events such as family birthdays and wedding anniversaries. The collection unit can also detect past trips and special events of the family and prioritize collection of them. The collection unit can also automatically detect historical events and important turning points of the family and set them to high priority. This allows important information to be collected efficiently by prioritizing collection of important family events.

[0033] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. The collection unit can also collect highly relevant data based on the user's living situation (work, hobbies, family structure, etc.). The collection unit can also filter data based on the user's current areas of interest (sports, music, travel, etc.). In this way, highly relevant data can be collected by filtering data based on the user's interests.

[0034] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collection of voice data. Furthermore, when the user uses text input, the collection unit can also prioritize collection of text data. Furthermore, when the user uses image input, the collection unit can also prioritize collection of image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method.

[0035] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also prioritize collecting data related to the user's past residences. The collection unit can also prioritize collecting data related to places the user frequently visits. In this way, highly relevant data can be efficiently collected by taking into account the user's geographical location information.

[0036] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects photos and posts shared by the user on social media. The collection unit can also analyze the activities of the user's friends on social media and collect related data. The collection unit can also collect the user's check-in information on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0037] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the user in the past. The collection unit can also customize the collection method (audio, text, image, etc.) based on the user's past feedback. The collection unit can also adjust the priority of the data to be collected by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0038] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between important family events during the analysis. For example, the analysis unit analyzes the interrelationships between important family events such as marriages and births. The analysis unit can also analyze the interrelationships between events such as family migrations and job changes. The analysis unit can also analyze the interrelationships between events such as illnesses and accidents of family members. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between important family events.

[0039] The analysis unit can perform the analysis while taking into consideration the family structure and relationships. For example, the analysis unit performs the analysis while taking into consideration the family structure (parents, children, siblings, etc.). The analysis unit can also perform the analysis while taking into consideration the family relationships (parent-child relationships, sibling relationships, etc.). The analysis unit can also analyze the impact of important events based on the family structure and relationships. By taking into consideration the family structure and relationships, more accurate analysis is possible.

[0040] During analysis, the analysis unit can weight the analysis based on the frequency of past family events. For example, the analysis unit can weight events that occur frequently more highly. The analysis unit can also weight events that occur less frequently. The analysis unit can also adjust the importance of the analysis results based on the frequency of the events. Thus, by weighting the analysis based on the frequency of past family events, the importance of the analysis results can be adjusted.

[0041] The analysis unit can perform the analysis taking into account the geographical distribution of family members. For example, the analysis unit performs the analysis taking into account the geographical distribution of areas where family members live. The analysis unit can also perform the analysis taking into account the geographical distribution of areas to which family members have moved. The analysis unit can also perform the analysis taking into account the geographical distribution of areas that family members frequently visit. In this way, by taking into account the geographical distribution of family members, more accurate analysis is possible.

[0042] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the family. For example, the analysis unit performs the analysis by referring to literature related to the family's history. The analysis unit can also perform the analysis by referring to literature related to important events in the family. The analysis unit can also perform the analysis by referring to literature related to the structure and relationships of the family. In this way, by referring to literature related to the family, the accuracy of the analysis is improved.

[0043] The analysis unit can perform the analysis while taking into account the social background of the family. For example, the analysis unit can perform the analysis while taking into account the social status and occupation of the family. The analysis unit can also perform the analysis while taking into account the educational background and academic history of the family. The analysis unit can also analyze the impact of important events based on the social background of the family. This allows for more accurate analysis by taking into account the social background of the family.

[0044] The generation unit can adjust the level of detail of important family events when generating a storyboard. For example, the generation unit sets a high level of detail for important events such as a family member's marriage or birth. The generation unit can also set a medium level of detail for events such as a family member's migration or job change. The generation unit can also set a low level of detail for events such as a family member's illness or accident. In this way, by adjusting the level of detail of important family events, a more appropriate storyboard can be generated.

[0045] When generating a storyboard, the generation unit can apply different generation algorithms depending on the family category. For example, the generation unit can apply a generation algorithm that emphasizes historical events to a category related to family history. The generation unit can also apply a generation algorithm that emphasizes emotional episodes to a category related to family memories. The generation unit can also apply a visually appealing generation algorithm to a category related to family photos. In this way, by applying different generation algorithms depending on the family category, a more appropriate storyboard can be generated.

[0046] When generating a storyboard, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit analyzes the user's past generation results and applies similar patterns to improve the accuracy of generation. The generation unit can also adjust the generation algorithm by referring to the user's past feedback. The generation unit can also learn the optimal generation method from the user's past generation results and improve accuracy. In this way, the generation accuracy is improved by referring to the user's past generation results.

[0047] When generating a storyboard, the generation unit can determine a generation priority based on the timing of family events. For example, the generation unit prioritizes incorporating important events such as a family member's marriage or birth into the storyboard. The generation unit can also incorporate events such as a family member's migration or job change into the storyboard with a medium priority. The generation unit can also incorporate events such as a family member's illness or accident into the storyboard with a low priority. In this way, by determining a generation priority based on the timing of family events, more important events can be prioritized and incorporated into the storyboard.

[0048] When generating a storyboard, the generation unit can adjust the order of generation based on the relevance of the family. For example, the generation unit adjusts the order of important events based on the family structure and relationships. The generation unit can also adjust the order of historical events of the family in chronological order. The generation unit can also adjust the order of family memories and episodes in accordance with the emotional flow. In this way, by adjusting the order of generation based on the relevance of the family, a storyboard with a more natural flow can be generated.

[0049] The generator may adjust the use of technical terminology in generating a storyboard according to the user's level of expertise. For example, if the user has technical expertise, the generator may generate a storyboard that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the generator may generate a storyboard that explains things in simple terms. The generator may also adjust the use of appropriate technical terminology according to the user's level of expertise. This allows the generation of a storyboard that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise.

[0050] The visualization unit can adjust the level of detail of important family events during visualization. For example, the visualization unit sets a high level of detail for important events such as a family member's marriage or birth. The visualization unit can also set a medium level of detail for events such as a family member's migration or job change. The visualization unit can also set a low level of detail for events such as a family member's illness or accident. In this way, by adjusting the level of detail of important family events, more appropriate images can be generated.

[0051] The visualization unit can apply different visualization algorithms depending on the family category during visualization. For example, the visualization unit can apply a visualization algorithm that emphasizes historical events to a category related to family history. The visualization unit can also apply a visualization algorithm that emphasizes emotional episodes to a category related to family memories. The visualization unit can also apply a visually appealing visualization algorithm to a category related to family photos. In this way, by applying different visualization algorithms depending on the family category, more appropriate images can be generated.

[0052] During visualization, the visualization unit can improve the accuracy of the image by referring to the user's past visualization results. For example, the visualization unit analyzes the user's past visualization results and applies similar patterns to improve the accuracy of the image. The visualization unit can also adjust the visualization algorithm by referring to the user's past feedback. The visualization unit can also learn the optimal visualization method from the user's past visualization results and improve accuracy. In this way, the accuracy of the image is improved by referring to the user's past visualization results.

[0053] The visualization unit can determine the priority of videos based on the timing of family events during visualization. For example, the visualization unit prioritizes incorporating important events such as a family member's marriage or birth into the video. The visualization unit can also incorporate events such as a family member's relocation or job change into the video with a medium priority. The visualization unit can also incorporate events such as a family member's illness or accident into the video with a low priority. In this way, by determining the priority of videos based on the timing of family events, more important events can be prioritized and incorporated into the video.

[0054] The visualization unit can adjust the order of the videos based on the relevance of the family members when visualizing. For example, the visualization unit adjusts the order of important events based on the family structure and relationships. The visualization unit can also adjust the order of historical events of the family in chronological order. The visualization unit can also adjust the order of family memories and episodes in accordance with the emotional flow. In this way, by adjusting the order of the videos based on the relevance of the family members, it is possible to generate videos with a more natural flow.

[0055] The visualization unit can adjust the use of technical terms in the video during visualization according to the user's level of expertise. For example, if the user has technical expertise, the visualization unit generates a video that uses a lot of technical terms. Also, if the user does not have technical expertise, the visualization unit can generate a video that explains things in simple terms. Also, the visualization unit can adjust the use of appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to generate a video that is easier to understand.

[0056] The providing unit can adjust the level of detail of important family events when providing the information. For example, the providing unit sets a high level of detail for important events such as a family member's marriage or birth. The providing unit can also set a medium level of detail for events such as a family member's migration or job change. The providing unit can also set a low level of detail for events such as a family member's illness or accident. In this way, by adjusting the level of detail of important family events, more appropriate information can be provided.

[0057] The providing unit can apply different providing algorithms depending on the family category when providing information. For example, the providing unit can apply a providing algorithm that emphasizes historical events to a category related to family history. The providing unit can also apply a providing algorithm that emphasizes emotional episodes to a category related to family memories. The providing unit can also apply a visually appealing providing algorithm to a category related to family photos. In this way, by applying different providing algorithms depending on the family category, more appropriate information can be provided.

[0058] The providing unit can determine the priority of provision based on the timing of family events when providing information. For example, the providing unit provides important events such as a family member's marriage or birth as information with priority. The providing unit can also provide events such as a family member's relocation or job change as information with medium priority. The providing unit can also provide events such as a family member's illness or accident as information with low priority. In this way, by determining the priority of provision based on the timing of family events, more important information can be provided preferentially.

[0059] The providing unit can adjust the order of provision based on the relevance of the family when providing the information. The providing unit adjusts the order of important events based on, for example, the family structure and relationships. The providing unit can also adjust the order of historical events of the family in chronological order. The providing unit can also adjust the order of family memories and episodes in accordance with the emotional flow. In this way, by adjusting the order of provision based on the relevance of the family, information can be provided in a more natural flow.

[0060] The interviewer AI generation unit can adjust the level of detail for important family events when generating the interviewer AI. For example, the interviewer AI generation unit sets a high level of detail for important events such as family marriages and births. The interviewer AI generation unit can also set a medium level of detail for events such as family migrations and job changes. The interviewer AI generation unit can also set a low level of detail for events such as family illnesses and accidents. This allows for the generation of a more appropriate interviewer AI by adjusting the level of detail for important family events.

[0061] When generating an interviewer AI, the interviewer AI generation unit can apply different generation algorithms depending on the family category. For example, the interviewer AI generation unit can apply a generation algorithm that emphasizes historical events to a category related to family history. The interviewer AI generation unit can also apply a generation algorithm that emphasizes emotional episodes to a category related to family memories. The interviewer AI generation unit can also apply a visually appealing generation algorithm to a category related to family photos. In this way, by applying different generation algorithms depending on the family category, a more appropriate interviewer AI can be generated.

[0062] When generating an interviewer AI, the interviewer AI generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the interviewer AI generation unit analyzes the user's past generation results and applies similar patterns to improve the accuracy of generation. The interviewer AI generation unit can also adjust the generation algorithm by referring to the user's past feedback. The interviewer AI generation unit can also learn the optimal generation method from the user's past generation results and improve accuracy. In this way, the accuracy of generation is improved by referring to the user's past generation results.

[0063] When generating an interviewer AI, the interviewer AI generation unit can determine generation priorities based on the timing of family events. For example, the interviewer AI generation unit prioritizes incorporating important events such as family marriages and births into the interviewer AI. The interviewer AI generation unit can also incorporate events such as family migration or job changes into the interviewer AI with medium priority. The interviewer AI generation unit can also incorporate events such as family illnesses or accidents into the interviewer AI with low priority. In this way, by determining generation priorities based on the timing of family events, more important events can be prioritized and incorporated into the interviewer AI.

[0064] When generating an interviewer AI, the interviewer AI generation unit can adjust the order of generation based on family relevance. For example, the interviewer AI generation unit adjusts the order of important events based on family structure and relationships. The interviewer AI generation unit can also adjust the order of historical family events in chronological order. The interviewer AI generation unit can also adjust the order of family memories and episodes in accordance with the emotional flow. In this way, by adjusting the generation order based on family relevance, an interviewer AI with a more natural flow can be generated.

[0065] When generating an interviewer AI, the interviewer AI generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the interviewer AI generation unit generates an interviewer AI that uses a lot of technical terminology. In addition, if the user does not have technical expertise, the interviewer AI generation unit can generate an interviewer AI that explains things in simple terms. The interviewer AI generation unit can also adjust the use of appropriate technical terminology according to the user's level of expertise. This allows for the generation of an interviewer AI that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The collection unit can analyze the user's past behavior history and determine the priority of data to be collected. For example, data that the user frequently accessed in the past can be collected with priority. Data that the user has given a high rating in the past can also be collected with priority. Data that the user has shared in the past can also be collected with priority. In this way, highly relevant data can be collected efficiently by analyzing the user's past behavior history.

[0068] The analysis unit can analyze the family's health data and adjust the content of the storyboard based on the family's health status. For example, it can identify important health events from the family's health data and incorporate them into the storyboard. It can also provide health advice from the family's health data. It can also suggest health preventive measures from the family's health data. In this way, by analyzing the family's health data, it is possible to generate a storyboard containing health-related information.

[0069] The providing unit can provide relevant information taking into account the user's geographical location information. For example, the providing unit can provide historical events related to the user's current location. The providing unit can also provide information related to the user's past residences. The providing unit can also provide information related to places the user frequently visits. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0070] The generation unit can analyze the user's social media activity and incorporate relevant data into the storyboard. For example, photos and posts shared by the user on social media can be incorporated into the storyboard. The generation unit can also analyze the activity of the user's friends on social media and incorporate relevant data into the storyboard. The generation unit can also incorporate the user's social media check-in information into the storyboard. In this way, highly relevant data can be incorporated into the storyboard by analyzing the user's social media activity.

[0071] The visualization unit can customize the way the video is presented by reflecting the user's past feedback. For example, the visualization unit can adjust the style of the video based on the user's past feedback. The visualization unit can also customize the audio presentation of the video based on the user's past feedback. The visualization unit can also adjust the length of the video by referring to the user's past feedback. In this way, the way the video is presented can be optimized by reflecting the user's past feedback.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection department collects the family tree, timeline, photographs, and questionnaire data. For example, the family tree is entered digitally, the timeline is entered manually, photographs are scanned and converted into digital data, and questionnaire data is collected through an online form. Step 2: The analysis unit analyzes the data collected by the collection unit to identify family history and important events. For example, it identifies family structure and relationships from family trees, identifies dates and times of important events from timelines, analyzes family appearances and landscapes from photographs, and extracts family memories and episodes from questionnaire data. Step 3: The generator generates a storyboard based on the information identified by the analyzer, for example, by generating a diagram showing family structure and relationships, recreating important events, incorporating photos of family members and scenery into the storyboard, and generating narration of memories and episodes. Step 4: The visualization unit visualizes the storyboard generated by the generation unit. For example, it visualizes diagrams showing family structure and relationships, reenactments of important events, photos of family members and landscapes, and narrations of memories and episodes.

[0074] (Example 2) A system according to an embodiment of the present invention visualizes the life stories of individuals and families based on family trees, timelines, photographs, and survey data. This system collects family trees, timelines, photographs, and survey data, analyzes them using AI to identify family history and important events, and generates and visualizes a storyboard. The system also provides a simple service using a messaging app, generating an interviewer AI with which users can easily converse. For example, the system collects family trees, timelines, photographs, and survey data, analyzes them using AI to identify family history and important events. Next, the AI ​​generates and visualizes a storyboard. Furthermore, the system provides a service that allows users to easily check family history and important events using a messaging app. Finally, the system generates an interviewer AI with which users can easily converse, providing a friendly conversation. This allows the system to visualize the life stories of individuals and families based on family trees, timelines, photographs, and survey data. For example, family history and important events can be conveyed visually and clearly. Furthermore, family history and important events can be easily checked through a messaging app. Furthermore, the system generates an interviewer AI with which users can easily converse, providing a friendly conversation.

[0075] A storyboard generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a visualization unit. The collection unit collects family trees, timelines, photographs, and questionnaire data. For example, the collection unit inputs the family trees in digital format. The collection unit can also manually input the timelines. The collection unit can also scan photographs and convert them into digital data. The collection unit can also collect questionnaire data using an online form. The analysis unit analyzes the data collected by the collection unit to identify family history and important events. For example, the analysis unit identifies family structure and relationships from the family tree. The analysis unit can also identify dates and times of important events from the timeline. The analysis unit can also analyze family appearances and scenery from photographs. The analysis unit can also extract family memories and episodes from the questionnaire data. The generation unit generates a storyboard based on the information identified by the analysis unit. For example, the generation unit generates a diagram showing family structure and relationships. The generation unit can also generate a reenactment video of important events. The generation unit can also incorporate photos of family members and scenery into the storyboard. The generation unit can also generate narration of memories and episodes. The visualization unit visualizes the storyboard generated by the generation unit. For example, the visualization unit visualizes a diagram showing family structure and relationships. The visualization unit can also visualize a reenactment of an important event. The visualization unit can also visualize photos of family members and scenery. The visualization unit can also visualize narration of memories and episodes. In this way, the storyboard generation system according to the embodiment can visualize the life story of an individual or family based on a family tree, a chronology, photos, and questionnaire data.

[0076] The storyboard generation system includes a providing unit that provides a simple service using a messaging app. The providing unit provides the simple service using a messaging app. For example, the providing unit notifies the user of family history or important events through the messaging app. The providing unit can also display the family structure and relationships through the messaging app. The providing unit can also display the dates and times of important events through the messaging app. The providing unit can also display photos of family members and scenery through the messaging app. The providing unit can also display memories and episodes through the messaging app. This allows the user to easily check family history and important events through the messaging app.

[0077] The storyboard generation system includes a generation unit that generates an interviewer AI with which the user can easily converse. The generation unit generates an interviewer AI with which the user can easily converse. For example, the generation unit generates an AI avatar that reads data on the user's grandchild. The generation unit can also generate an AI that recognizes the user's voice and engages in natural conversation. The generation unit can also generate an AI that recognizes the user's facial expressions and engages in conversation according to their emotions. This allows the generation of an interviewer AI with which the user can converse easily, providing a friendly conversation.

[0078] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit can immediately start data collection. Furthermore, if the user is feeling stressed, the collection unit can postpone data collection and start collection when the user is calm. Furthermore, if the user is busy, the collection unit can adjust data collection to suit the user's schedule. In this way, by adjusting the timing of data collection according to the user's emotions, data can be collected at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] The collection unit can automatically detect past events and important happenings of the family and determine the priority of data to be collected. For example, the collection unit prioritizes collection of important events such as family birthdays and wedding anniversaries. The collection unit can also detect past trips and special events of the family and prioritize collection of them. The collection unit can also automatically detect historical events and important turning points of the family and set them to high priority. This allows important information to be collected efficiently by prioritizing collection of important family events.

[0080] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. The collection unit can also collect highly relevant data based on the user's living situation (work, hobbies, family structure, etc.). The collection unit can also filter data based on the user's current areas of interest (sports, music, travel, etc.). In this way, highly relevant data can be collected by filtering data based on the user's interests.

[0081] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collection of voice data. Furthermore, when the user uses text input, the collection unit can also prioritize collection of text data. Furthermore, when the user uses image input, the collection unit can also prioritize collection of image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method.

[0082] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is emotional, the collection unit can prioritize collecting data related to emotional events. Furthermore, if the user is sad, the collection unit can prioritize collecting data that provides comfort. Furthermore, if the user is excited, the collection unit can prioritize collecting data that allows the user to share their excitement. Thus, by prioritizing data according to the user's emotions, more appropriate data can be collected. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also prioritize collecting data related to the user's past residences. The collection unit can also prioritize collecting data related to places the user frequently visits. In this way, highly relevant data can be efficiently collected by taking into account the user's geographical location information.

[0084] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects photos and posts shared by the user on social media. The collection unit can also analyze the activities of the user's friends on social media and collect related data. The collection unit can also collect the user's check-in information on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities.

[0085] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the user in the past. The collection unit can also customize the collection method (audio, text, image, etc.) based on the user's past feedback. The collection unit can also adjust the priority of the data to be collected by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0086] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is nervous, the analysis unit can also provide concise and to-the-point analysis results. If the user is excited, the analysis unit can also provide visually appealing analysis results. This allows for adjusting the way the analysis is presented according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between important family events during the analysis. For example, the analysis unit analyzes the interrelationships between important family events such as marriages and births. The analysis unit can also analyze the interrelationships between events such as family migrations and job changes. The analysis unit can also analyze the interrelationships between events such as illnesses and accidents of family members. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between important family events.

[0088] The analysis unit can perform the analysis while taking into consideration the family structure and relationships. For example, the analysis unit performs the analysis while taking into consideration the family structure (parents, children, siblings, etc.). The analysis unit can also perform the analysis while taking into consideration the family relationships (parent-child relationships, sibling relationships, etc.). The analysis unit can also analyze the impact of important events based on the family structure and relationships. By taking into consideration the family structure and relationships, more accurate analysis is possible.

[0089] During analysis, the analysis unit can weight the analysis based on the frequency of past family events. For example, the analysis unit can weight events that occur frequently more highly. The analysis unit can also weight events that occur less frequently. The analysis unit can also adjust the importance of the analysis results based on the frequency of the events. Thus, by weighting the analysis based on the frequency of past family events, the importance of the analysis results can be adjusted.

[0090] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results first. Also, if the user is nervous, the analysis unit can display concise analysis results first. Also, if the user is excited, the analysis unit can display visually appealing analysis results first. In this way, by adjusting the display order of the analysis results according to the user's emotions, the results can be displayed in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The analysis unit can perform the analysis taking into account the geographical distribution of family members. For example, the analysis unit performs the analysis taking into account the geographical distribution of areas where family members live. The analysis unit can also perform the analysis taking into account the geographical distribution of areas to which family members have moved. The analysis unit can also perform the analysis taking into account the geographical distribution of areas that family members frequently visit. In this way, by taking into account the geographical distribution of family members, more accurate analysis is possible.

[0092] During the analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the family. For example, the analysis unit performs the analysis by referring to literature related to the family's history. The analysis unit can also perform the analysis by referring to literature related to important events in the family. The analysis unit can also perform the analysis by referring to literature related to the structure and relationships of the family. In this way, by referring to literature related to the family, the accuracy of the analysis is improved.

[0093] The analysis unit can perform the analysis while taking into account the social background of the family. For example, the analysis unit can perform the analysis while taking into account the social status and occupation of the family. The analysis unit can also perform the analysis while taking into account the educational background and academic history of the family. The analysis unit can also analyze the impact of important events based on the social background of the family. This allows for more accurate analysis by taking into account the social background of the family.

[0094] The generation unit can estimate the user's emotions and adjust the way the storyboard is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a detailed storyboard. If the user is nervous, the generation unit can also generate a concise and to-the-point storyboard. If the user is excited, the generation unit can also generate a visually appealing storyboard. This allows a more appropriate storyboard to be generated by adjusting the way the storyboard is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] The generation unit can adjust the level of detail of important family events when generating a storyboard. For example, the generation unit sets a high level of detail for important events such as a family member's marriage or birth. The generation unit can also set a medium level of detail for events such as a family member's migration or job change. The generation unit can also set a low level of detail for events such as a family member's illness or accident. In this way, by adjusting the level of detail of important family events, a more appropriate storyboard can be generated.

[0096] When generating a storyboard, the generation unit can apply different generation algorithms depending on the family category. For example, the generation unit can apply a generation algorithm that emphasizes historical events to a category related to family history. The generation unit can also apply a generation algorithm that emphasizes emotional episodes to a category related to family memories. The generation unit can also apply a visually appealing generation algorithm to a category related to family photos. In this way, by applying different generation algorithms depending on the family category, a more appropriate storyboard can be generated.

[0097] When generating a storyboard, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit analyzes the user's past generation results and applies similar patterns to improve the accuracy of generation. The generation unit can also adjust the generation algorithm by referring to the user's past feedback. The generation unit can also learn the optimal generation method from the user's past generation results and improve accuracy. In this way, the generation accuracy is improved by referring to the user's past generation results.

[0098] The generation unit can estimate the user's emotions and adjust the length of the storyboard based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a longer storyboard with detailed explanations. If the user is in a hurry, the generation unit can also generate a short, to-the-point storyboard. If the user is excited, the generation unit can also generate a storyboard with visually stimulating effects. By adjusting the length of the storyboard according to the user's emotions, a storyboard of a more appropriate length can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] When generating a storyboard, the generation unit can determine a generation priority based on the timing of family events. For example, the generation unit prioritizes incorporating important events such as a family member's marriage or birth into the storyboard. The generation unit can also incorporate events such as a family member's migration or job change into the storyboard with a medium priority. The generation unit can also incorporate events such as a family member's illness or accident into the storyboard with a low priority. In this way, by determining a generation priority based on the timing of family events, more important events can be prioritized and incorporated into the storyboard.

[0100] When generating a storyboard, the generation unit can adjust the order of generation based on the relevance of the family. For example, the generation unit adjusts the order of important events based on the family structure and relationships. The generation unit can also adjust the order of historical events of the family in chronological order. The generation unit can also adjust the order of family memories and episodes in accordance with the emotional flow. In this way, by adjusting the order of generation based on the relevance of the family, a storyboard with a more natural flow can be generated.

[0101] The generator may adjust the use of technical terminology in generating a storyboard according to the user's level of expertise. For example, if the user has technical expertise, the generator may generate a storyboard that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the generator may generate a storyboard that explains things in simple terms. The generator may also adjust the use of appropriate technical terminology according to the user's level of expertise. This allows the generation of a storyboard that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise.

[0102] The visualization unit can estimate the user's emotion and adjust the way the video is presented based on the estimated user's emotion. For example, if the user is relaxed, the visualization unit can use a calming video presentation. If the user is excited, the visualization unit can also use a visually stimulating video presentation. If the user is sad, the visualization unit can also use a comforting video presentation. This allows for adjusting the way the video is presented according to the user's emotion, thereby generating a more appropriate video. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] The visualization unit can adjust the level of detail of important family events during visualization. For example, the visualization unit sets a high level of detail for important events such as a family member's marriage or birth. The visualization unit can also set a medium level of detail for events such as a family member's migration or job change. The visualization unit can also set a low level of detail for events such as a family member's illness or accident. In this way, by adjusting the level of detail of important family events, more appropriate images can be generated.

[0104] The visualization unit can apply different visualization algorithms depending on the family category during visualization. For example, the visualization unit can apply a visualization algorithm that emphasizes historical events to a category related to family history. The visualization unit can also apply a visualization algorithm that emphasizes emotional episodes to a category related to family memories. The visualization unit can also apply a visually appealing visualization algorithm to a category related to family photos. In this way, by applying different visualization algorithms depending on the family category, more appropriate images can be generated.

[0105] During visualization, the visualization unit can improve the accuracy of the image by referring to the user's past visualization results. For example, the visualization unit analyzes the user's past visualization results and applies similar patterns to improve the accuracy of the image. The visualization unit can also adjust the visualization algorithm by referring to the user's past feedback. The visualization unit can also learn the optimal visualization method from the user's past visualization results and improve accuracy. In this way, the accuracy of the image is improved by referring to the user's past visualization results.

[0106] The visualization unit can estimate the user's emotions and adjust the length of the video based on the estimated user's emotions. For example, if the user is relaxed, the visualization unit can generate a longer video with detailed explanations. If the user is in a hurry, the visualization unit can also generate a short video that focuses on the main points. If the user is excited, the visualization unit can also generate a video with visually stimulating effects. In this way, by adjusting the length of the video according to the user's emotions, a video of a more appropriate length can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The visualization unit can determine the priority of videos based on the timing of family events during visualization. For example, the visualization unit prioritizes incorporating important events such as a family member's marriage or birth into the video. The visualization unit can also incorporate events such as a family member's relocation or job change into the video with a medium priority. The visualization unit can also incorporate events such as a family member's illness or accident into the video with a low priority. In this way, by determining the priority of videos based on the timing of family events, more important events can be prioritized and incorporated into the video.

[0108] The visualization unit can adjust the order of the videos based on the relevance of the family members when visualizing. For example, the visualization unit adjusts the order of important events based on the family structure and relationships. The visualization unit can also adjust the order of historical events of the family in chronological order. The visualization unit can also adjust the order of family memories and episodes in accordance with the emotional flow. In this way, by adjusting the order of the videos based on the relevance of the family members, it is possible to generate videos with a more natural flow.

[0109] The visualization unit can adjust the use of technical terms in the video during visualization according to the user's level of expertise. For example, if the user has technical expertise, the visualization unit generates a video that uses a lot of technical terms. Also, if the user does not have technical expertise, the visualization unit can generate a video that explains things in simple terms. Also, the visualization unit can adjust the use of appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to generate a video that is easier to understand.

[0110] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can provide detailed information. Furthermore, when the user is nervous, the providing unit can provide concise and to-the-point information. Furthermore, when the user is excited, the providing unit can provide visually appealing information. In this way, by adjusting the way information is presented according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] The providing unit can adjust the level of detail of important family events when providing the information. For example, the providing unit sets a high level of detail for important events such as a family member's marriage or birth. The providing unit can also set a medium level of detail for events such as a family member's migration or job change. The providing unit can also set a low level of detail for events such as a family member's illness or accident. In this way, by adjusting the level of detail of important family events, more appropriate information can be provided.

[0112] The providing unit can apply different providing algorithms depending on the family category when providing information. For example, the providing unit can apply a providing algorithm that emphasizes historical events to a category related to family history. The providing unit can also apply a providing algorithm that emphasizes emotional episodes to a category related to family memories. The providing unit can also apply a visually appealing providing algorithm to a category related to family photos. In this way, by applying different providing algorithms depending on the family category, more appropriate information can be provided.

[0113] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide longer information including detailed explanations. If the user is in a hurry, the providing unit can also provide short, to-the-point information. If the user is excited, the providing unit can also provide information with visually stimulating effects. This allows for more appropriate information to be provided by adjusting the length of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The providing unit can determine the priority of provision based on the timing of family events when providing information. For example, the providing unit provides important events such as a family member's marriage or birth as information with priority. The providing unit can also provide events such as a family member's relocation or job change as information with medium priority. The providing unit can also provide events such as a family member's illness or accident as information with low priority. In this way, by determining the priority of provision based on the timing of family events, more important information can be provided preferentially.

[0115] The providing unit can adjust the order of provision based on the relevance of the family when providing the information. The providing unit adjusts the order of important events based on, for example, the family structure and relationships. The providing unit can also adjust the order of historical events of the family in chronological order. The providing unit can also adjust the order of family memories and episodes in accordance with the emotional flow. In this way, by adjusting the order of provision based on the relevance of the family, information can be provided in a more natural flow.

[0116] The interviewer AI generation unit can estimate the user's emotions and adjust the interviewer AI's expression style based on the estimated user emotions. For example, if the user is relaxed, the interviewer AI generation unit can use a calm expression style. If the user is nervous, the interviewer AI generation unit can also use a concise and to-the-point expression style. If the user is excited, the interviewer AI generation unit can also use a visually stimulating expression style. This allows the interviewer AI's expression style to be adjusted according to the user's emotions, thereby providing a more friendly conversation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] The interviewer AI generation unit can adjust the level of detail for important family events when generating the interviewer AI. For example, the interviewer AI generation unit sets a high level of detail for important events such as family marriages and births. The interviewer AI generation unit can also set a medium level of detail for events such as family migrations and job changes. The interviewer AI generation unit can also set a low level of detail for events such as family illnesses and accidents. This allows for the generation of a more appropriate interviewer AI by adjusting the level of detail for important family events.

[0118] When generating an interviewer AI, the interviewer AI generation unit can apply different generation algorithms depending on the family category. For example, the interviewer AI generation unit can apply a generation algorithm that emphasizes historical events to a category related to family history. The interviewer AI generation unit can also apply a generation algorithm that emphasizes emotional episodes to a category related to family memories. The interviewer AI generation unit can also apply a visually appealing generation algorithm to a category related to family photos. In this way, by applying different generation algorithms depending on the family category, a more appropriate interviewer AI can be generated.

[0119] When generating an interviewer AI, the interviewer AI generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the interviewer AI generation unit analyzes the user's past generation results and applies similar patterns to improve the accuracy of generation. The interviewer AI generation unit can also adjust the generation algorithm by referring to the user's past feedback. The interviewer AI generation unit can also learn the optimal generation method from the user's past generation results and improve accuracy. In this way, the accuracy of generation is improved by referring to the user's past generation results.

[0120] The interviewer AI generation unit can estimate the user's emotions and adjust the length of the interviewer AI based on the estimated user emotions. For example, if the user is relaxed, the interviewer AI generation unit can generate a longer interviewer AI that includes detailed explanations. If the user is in a hurry, the interviewer AI generation unit can also generate a short, to-the-point interviewer AI. If the user is excited, the interviewer AI generation unit can also generate an interviewer AI that adds visually stimulating effects. This allows the length of the interviewer AI to be adjusted according to the user's emotions, thereby generating an interviewer AI of a more appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] When generating an interviewer AI, the interviewer AI generation unit can determine generation priorities based on the timing of family events. For example, the interviewer AI generation unit prioritizes incorporating important events such as family marriages and births into the interviewer AI. The interviewer AI generation unit can also incorporate events such as family migration or job changes into the interviewer AI with medium priority. The interviewer AI generation unit can also incorporate events such as family illnesses or accidents into the interviewer AI with low priority. In this way, by determining generation priorities based on the timing of family events, more important events can be prioritized and incorporated into the interviewer AI.

[0122] When generating an interviewer AI, the interviewer AI generation unit can adjust the order of generation based on family relevance. For example, the interviewer AI generation unit adjusts the order of important events based on family structure and relationships. The interviewer AI generation unit can also adjust the order of historical family events in chronological order. The interviewer AI generation unit can also adjust the order of family memories and episodes in accordance with the emotional flow. In this way, by adjusting the generation order based on family relevance, an interviewer AI with a more natural flow can be generated.

[0123] When generating an interviewer AI, the interviewer AI generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, if the user has technical expertise, the interviewer AI generation unit generates an interviewer AI that uses a lot of technical terminology. In addition, if the user does not have technical expertise, the interviewer AI generation unit can generate an interviewer AI that explains things in simple terms. The interviewer AI generation unit can also adjust the use of appropriate technical terminology according to the user's level of expertise. This allows for the generation of an interviewer AI that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, visualization unit, and provision unit, 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 family trees, timelines, photos, and questionnaire data using the camera 42 and reception device 38 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 identify family history and important events. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a storyboard based on the analysis results. The visualization unit visualizes the generated storyboard using, for example, the output device 40 of the smart device 14. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the user of family history and important events via a messaging app. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, visualization unit, and provision 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 family tree, chronology, photographs, and questionnaire data 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 identify family history and important events. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a storyboard based on the analysis results. The visualization unit visualizes the generated storyboard using, for example, the speaker 240 of the smart glasses 214. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user of family history and important events through a messaging app. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, visualization unit, and provision 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 family trees, timelines, photographs, and questionnaire data 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 identify family history and important events. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a storyboard based on the analysis results. The visualization unit visualizes the generated storyboard using, for example, the display 343 of the headset-type terminal 314. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and notifies the user of family history and important events via a messaging app. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, visualization unit, and provision 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 family trees, timelines, photographs, and questionnaire data 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 identify family history and important events. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a storyboard based on the analysis results. The visualization unit visualizes the generated storyboard using, for example, the control object 443 of the robot 414. The provision unit is realized, for example, by the control unit 46A of the robot 414 and notifies the robot 414 of family history and important events via a messaging app.

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

[0125] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is emotional, data related to emotional events can be analyzed preferentially. If the user is sad, data that provides comfort can be analyzed preferentially. If the user is excited, data that allows the user to share the excitement can be analyzed preferentially. In this way, by determining the analysis priority according to the user's emotions, more appropriate data can be analyzed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The collection unit can estimate the user's emotions and adjust the format of the data to be collected based on the estimated user emotions. For example, if the user is relaxed, detailed text data can be collected. If the user is nervous, concise text data can be collected. If the user is excited, visually appealing image data can be collected. This allows more appropriate data to be collected by adjusting the format of the data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0127] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user emotions. For example, if the user is relaxed, the information can be provided immediately. If the user is nervous, the information can be provided later when the user is calm. If the user is busy, the information can be provided according to the user's schedule. In this way, by adjusting the timing of information provision according to the user's emotions, the information can be provided at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0128] The generation unit can estimate the user's emotions and adjust the content of the storyboard based on the estimated user's emotions. For example, if the user is relaxed, a storyboard including detailed episodes can be generated. If the user is nervous, a concise storyboard that focuses on the main points can be generated. If the user is excited, a storyboard that adds visually stimulating effects can be generated. By adjusting the content of the storyboard according to the user's emotions, a more appropriate storyboard can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0129] The visualization unit can estimate the user's emotions and adjust the audio expression of the video based on the estimated user's emotions. For example, if the user is relaxed, a calm audio expression can be used. If the user is excited, a visually stimulating audio expression can be used. If the user is sad, a comforting audio expression can be used. This allows for adjusting the audio expression of the video according to the user's emotions to generate more appropriate videos. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0130] The collection unit can analyze the user's past behavior history and determine the priority of data to be collected. For example, data that the user frequently accessed in the past can be collected with priority. Data that the user has given a high rating in the past can also be collected with priority. Data that the user has shared in the past can also be collected with priority. In this way, highly relevant data can be collected efficiently by analyzing the user's past behavior history.

[0131] The analysis unit can analyze the family's health data and adjust the content of the storyboard based on the family's health status. For example, it can identify important health events from the family's health data and incorporate them into the storyboard. It can also provide health advice from the family's health data. It can also suggest health preventive measures from the family's health data. In this way, by analyzing the family's health data, it is possible to generate a storyboard containing health-related information.

[0132] The providing unit can provide relevant information taking into account the user's geographical location information. For example, the providing unit can provide historical events related to the user's current location. The providing unit can also provide information related to the user's past residences. The providing unit can also provide information related to places the user frequently visits. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0133] The generation unit can analyze the user's social media activity and incorporate relevant data into the storyboard. For example, photos and posts shared by the user on social media can be incorporated into the storyboard. The generation unit can also analyze the activity of the user's friends on social media and incorporate relevant data into the storyboard. The generation unit can also incorporate the user's social media check-in information into the storyboard. In this way, highly relevant data can be incorporated into the storyboard by analyzing the user's social media activity.

[0134] The visualization unit can customize the way the video is presented by reflecting the user's past feedback. For example, the visualization unit can adjust the style of the video based on the user's past feedback. The visualization unit can also customize the audio presentation of the video based on the user's past feedback. The visualization unit can also adjust the length of the video by referring to the user's past feedback. In this way, the way the video is presented can be optimized by reflecting the user's past feedback.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection department collects the family tree, timeline, photographs, and questionnaire data. For example, the family tree is entered digitally, the timeline is entered manually, photographs are scanned and converted into digital data, and questionnaire data is collected through an online form. Step 2: The analysis unit analyzes the data collected by the collection unit to identify family history and important events. For example, it identifies family structure and relationships from family trees, identifies dates and times of important events from timelines, analyzes family appearances and landscapes from photographs, and extracts family memories and episodes from questionnaire data. Step 3: The generator generates a storyboard based on the information identified by the analyzer, for example, by generating a diagram showing family structure and relationships, recreating important events, incorporating photos of family members and scenery into the storyboard, and generating narration of memories and episodes. Step 4: The visualization unit visualizes the storyboard generated by the generation unit. For example, it visualizes diagrams showing family structure and relationships, reenactments of important events, photos of family members and landscapes, and narrations of memories and episodes.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0138] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

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

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0154] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

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

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

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

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

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

[0174] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0187] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

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

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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, to avoid confusion and 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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 collection department that collects family trees, timelines, photographs, and survey data; an analysis unit that analyzes the data collected by the collection unit to identify family history and specific events; a generator that generates a storyboard based on the information identified by the analyzer; a visualization unit that visualizes the storyboard generated by the generation unit. A system characterized by:

2. Equipped with a service provider that provides simple services using messaging apps 2. The system of claim 1.

3. A generation unit is provided that generates an interviewer AI with which the user can easily converse.

2. The system of claim 1.

4. The collecting unit Estimate the user's emotions and adjust the timing of data collection according to the estimated user emotions.

2. The system of claim 1.

5. The collecting unit Automatically detects past family events and important happenings and prioritizes data collection 2. The system of claim 1.

6. The collecting unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

7. The collecting unit When collecting data, select the optimal collection method depending on the user's input method 2. The system of claim 1.

8. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

9. The collecting unit When collecting data, consider the user's geographic location to prioritize collecting the most relevant data.

2. The system of claim 1.

10. The collecting unit When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1.

Images