System

Abstract

To provide a system according to an embodiment which regenerates conversations with deceased parents or persons whom a user cares about and maintains emotional connections.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a reproduction unit. The collection unit collects data. The analysis unit analyzes data collected by the collection unit and regenerates words, the manners of speaking, and the expressions of deceased parents or persons whom a user cares about. The generation unit prepares the account of a messenger application on the basis of data analyzed by the analysis unit and a user can thus enjoy conversations. The regeneration unit regenerates a figure by using an AR technique on the basis of the data 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technology had the disadvantage that it was difficult to recreate conversations with deceased parents or loved ones, and there were limited ways to maintain emotional connections.

[0005] The system of the embodiment aims to recreate conversations with deceased parents or loved ones and maintain emotional connections. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a reproduction unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and reproduces the language, speech, and facial expressions of the deceased parents or important people. The generation unit creates a messenger app account based on the data analyzed by the analysis unit, allowing the user to enjoy conversations. The reproduction unit reproduces the appearance using AR technology based on the data generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment allows users to recreate conversations with their deceased parents or loved ones, allowing them to maintain emotional ties. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention reproduces memories with deceased parents or loved ones, and allows users to talk with them 24 hours a day when they feel lonely. With the consent of the family, this system uses a generation AI to learn the emails, photos, videos, voicemails, conversation logs and letters from SNS such as messenger apps that are lying dormant in the family home, and integrates them into one messenger app account. This allows users to enjoy conversations whenever they feel lonely. In addition, as an option, AR technology is used to reproduce the appearance through the glass, providing a more realistic conversation experience. For example, with the consent of the family, a user collects data such as emails, photos, videos, voicemails, conversation logs from messenger apps, and letters stored on the smartphone or computer at the family home. Next, these data are input into the generation AI and learned. The generation AI analyzes the collected data and reproduces the language, speaking style, and facial expressions of the deceased parents or loved ones. A messenger app account is created based on the data learned by the generation AI. Through this account, the user can enjoy conversations at any time. For example, if a user sends a message saying "I'm tired from work today," the AI ​​will reply in the parents' voice, saying "Thank you for your hard work, have a good rest." In addition, by using AR technology as an option, the appearance of the parents can be reproduced through the glasses. For example, when the user wears the AR glasses, the image of the parents appears in front of the user, and the user can experience as if they are actually talking to the parents. This allows the user to enjoy a more realistic conversation experience. This service provides a new way to cherish memories of deceased parents and loved ones and heal loneliness. Users can enjoy conversations with their loved ones anytime, anywhere, providing emotional support. This allows the system to allow users to enjoy conversations with their deceased parents and loved ones 24 hours a day.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a reproduction unit. The collection unit collects data. The data includes, for example, text data, voice data, image data, video data, and the like, but is not limited to such examples. For example, with the consent of the family, the collection unit collects data such as emails, photos, videos, voicemails, conversation logs of messenger apps, and letters stored in a smartphone or computer at the parent's home. For example, the collection unit collects emails and photos from a smartphone with the consent of the family. The collection unit can also collect videos and voicemails stored in a computer. Furthermore, the collection unit can also collect conversation logs and letters of messenger apps. The analysis unit analyzes the data collected by the collection unit and reproduces the language, speaking style, facial expressions, and the like of the deceased parents or important people. The analysis is performed by, for example, natural language processing, voice analysis, image analysis, and the like, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze the collected text data. The analysis unit can also use voice analysis technology to analyze the collected voice data. Furthermore, the analysis unit can also analyze the collected image data using image analysis technology. The generation unit creates a messenger app account based on the data analyzed by the analysis unit, so that the user can enjoy conversations at any time. The generation is performed, for example, based on an account creation procedure or a message generation algorithm, but is not limited to such examples. For example, the generation unit creates a messenger app account based on the analyzed data. The generation unit can also generate a reply to a message sent by the user using a message generation algorithm. The reproduction unit reproduces the appearance using AR technology based on the data generated by the generation unit. The reproduction is performed, for example, by a method such as voice synthesis, facial expression recognition, and motion reproduction, but is not limited to such examples. For example, the reproduction unit reproduces the voice of the deceased parents or a loved one using voice synthesis technology. The reproduction unit can also reproduce the facial expression of the deceased parents or a loved one using facial expression recognition technology. Furthermore, the reproduction unit can also reproduce the motion of the deceased parents or a loved one using motion reproduction technology.As a result, the system according to the embodiment allows the user to enjoy conversation with deceased parents or loved ones 24 hours a day. Some or all of the above-mentioned processing in the reproducing unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproducing unit can reproduce the appearance using an AI model that reproduces the appearance using the data generated by the generating unit as input.

[0030] The collection unit collects data. Examples of the data include, but are not limited to, text data, voice data, image data, and video data. For example, with the consent of the family, the collection unit collects data such as emails, photos, videos, voicemails, conversation logs of messenger apps, and letters stored in the smartphone or computer at the parent's home. For example, the collection unit collects emails and photos from the smartphone with the consent of the family. The collection unit can also collect videos and voicemails stored in the computer. Furthermore, the collection unit can collect conversation logs and letters of messenger apps. The collection unit can use dedicated software or hardware to efficiently collect these data. For example, the data can be automatically collected and uploaded to a cloud server through a dedicated application installed on the smartphone or computer. This allows the data collection work to be performed quickly and accurately. In addition, the collection unit can protect the data using encryption technology to ensure the privacy and security of the data. For example, the collected data is encrypted at the time of collection, and is stored in an encrypted state when it is saved in the cloud server. This can prevent data leakage and unauthorized access. Furthermore, the collection unit can build a database for eliminating data duplication and efficiently managing data. For example, to prevent the same email or photo from being collected multiple times, the collection unit can calculate the hash value of the data and automatically detect and eliminate duplicate data. This allows for efficient operation of the database.

[0031] The analysis unit analyzes the data collected by the collection unit and reproduces the language, speech, facial expressions, etc. of the deceased parents or important people. The analysis is performed by, for example, natural language processing, voice analysis, image analysis, etc., but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze the collected text data. The analysis unit can also use voice analysis technology to analyze the collected voice data. Furthermore, the analysis unit can also use image analysis technology to analyze the collected image data. The analysis unit combines these technologies to achieve more accurate reproduction. For example, natural language processing technology is used to extract specific language and speech patterns from the text data, and voice analysis technology is used to analyze specific voice characteristics and intonation from the voice data. Furthermore, image analysis technology is used to extract specific facial expressions and movement patterns from the image data. As a result, the analysis unit can comprehensively analyze the collected data and reproduce the language, speech, facial expressions, etc. of the deceased parents or important people with high accuracy. Furthermore, the analysis unit can continuously improve the accuracy of the analysis by using machine learning algorithms. For example, the collected data is used to train a machine learning model and improve the accuracy of the analysis. The analysis unit can also collect feedback from users and continuously improve the accuracy and reproducibility of the analysis results. This allows the analysis unit to always use the latest technology and data to achieve highly accurate analysis and improve the performance of the entire system.

[0032] The generation unit creates an account for the messenger app based on the data analyzed by the analysis unit, so that the user can enjoy a conversation at any time. The generation is performed, for example, based on the procedure for creating an account and an algorithm for generating a message, but is not limited to such examples. For example, the generation unit creates an account for the messenger app based on the analyzed data. The generation unit can also generate a reply to a message sent by the user using a message generation algorithm. The generation unit uses these algorithms to enable the user to enjoy a natural conversation. For example, the generation unit uses natural language generation technology to generate an appropriate reply to the user's message. The generation unit can also use voice synthesis technology to generate a reply in the voice of the deceased parents or a loved one based on the analyzed voice data. Furthermore, the generation unit can also use image generation technology to reproduce the facial expressions and movements of the deceased parents or a loved one based on the analyzed image data. This allows the generation unit to provide an experience as if the user were talking to the deceased parents or a loved one. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and naturalness of the generation algorithm. For example, the user can evaluate the generated reply and adjust the generation algorithm based on the evaluation. This enables the generation unit to always use the latest technology and data to provide a highly accurate, natural conversational experience and improve the performance of the entire system.

[0033] The reproduction unit reproduces the appearance using AR technology based on the data generated by the generation unit. The reproduction is performed by, for example, voice synthesis, facial expression recognition, motion reproduction, and the like, but is not limited to such examples. For example, the reproduction unit reproduces the voice of the deceased parents or a loved one using voice synthesis technology. The reproduction unit can also reproduce the facial expression of the deceased parents or a loved one using facial expression recognition technology. Furthermore, the reproduction unit can reproduce the motion of the deceased parents or a loved one using motion reproduction technology. The reproduction unit combines these technologies to achieve a more realistic reproduction. For example, the reproduction unit reproduces the voice of the deceased parents or a loved one based on the analyzed voice data using voice synthesis technology, and reproduces the facial expression of the deceased parents or a loved one based on the analyzed image data using facial expression recognition technology. Furthermore, the motion reproduction technology reproduces the motion of the deceased parents or a loved one based on the analyzed video data. This allows the reproduction unit to provide an experience as if the user were face-to-face with the deceased parents or a loved one. Furthermore, the reproduction unit can collect user feedback and continuously improve the accuracy and realism of the reproduction technology. For example, the user can evaluate the reproduced appearance and adjust the reproduction technology based on the evaluation. This allows the reproduction unit to always use the latest technology and data to provide highly accurate and realistic reproduction, improving the performance of the entire system. Some or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproduction unit can use the data generated by the generation unit as input and reproduce the appearance using an AI model that reproduces the appearance.

[0034] The collection unit can collect data of emails, photos, videos, voicemails, conversation logs of messenger apps, and letters stored in a smartphone or computer at the family home with the consent of the family. For example, the collection unit collects emails and photos from the smartphone with the consent of the family. For example, the collection unit collects emails from the smartphone with the consent of the family. The collection unit can also collect photos from the smartphone with the consent of the family. Furthermore, the collection unit can collect videos and voicemails stored in the computer with the consent of the family. For example, the collection unit collects videos stored in the computer with the consent of the family. The collection unit can also collect voicemails stored in the computer with the consent of the family. Furthermore, the collection unit can collect conversation logs and letters from messenger apps with the consent of the family. For example, the collection unit collects conversation logs from messenger apps with the consent of the family. The collection unit can also collect letters with the consent of the family. In this way, data can be collected while protecting privacy by collecting data with the consent of the family. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect emails and photos from smartphones with the consent of the family, input the collected data into AI, and have the AI ​​collect the data.

[0035] The analysis unit can analyze the collected data using the generation AI and reproduce the language, speech, and facial expressions of the deceased parents or important people. The analysis unit can analyze the collected text data using, for example, natural language processing technology. For example, the analysis unit can analyze the collected text data using natural language processing technology. The analysis unit can also analyze the collected voice data using voice analysis technology. For example, the analysis unit can analyze the collected voice data using voice analysis technology. Furthermore, the analysis unit can also analyze the collected image data using image analysis technology. For example, the analysis unit can analyze the collected image data using image analysis technology. As a result, the language, speech, and facial expressions of the deceased parents or important people can be reproduced with high accuracy by using the generation AI. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the analysis of the data.

[0036] The generation unit can create a messenger app account based on the analyzed data, so that the user can enjoy a conversation at any time. The generation unit, for example, creates a messenger app account based on the analyzed data. For example, the generation unit creates a messenger app account based on the analyzed data. The generation unit can also generate a reply to a message sent by the user using a message generation algorithm. For example, the generation unit generates a reply to a message sent by the user using a message generation algorithm. This allows the user to enjoy a conversation at any time. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the analyzed data to a generation AI and cause the generation AI to create a messenger app account and generate a message.

[0037] The reproduction unit can reproduce the appearance of the parents through the glass using AR technology. The reproduction unit reproduces the voice of the deceased parents or a loved one, for example, using voice synthesis technology. For example, the reproduction unit reproduces the voice of the deceased parents or a loved one, using voice synthesis technology. The reproduction unit can also reproduce the facial expression of the deceased parents or a loved one, using facial expression recognition technology. For example, the reproduction unit reproduces the facial expression of the deceased parents or a loved one, using facial expression recognition technology. The reproduction unit can also reproduce the motion of the deceased parents or a loved one, using motion reproduction technology. For example, the reproduction unit reproduces the motion of the deceased parents or a loved one, using motion reproduction technology. This allows a more realistic conversation experience to be provided by using AR technology. Part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproduction unit can input the data generated by the generation unit to the generation AI and cause the generation AI to reproduce the appearance.

[0038] The collection unit can optimize the type of data to be collected based on the user's past behavior history. For example, the collection unit preferentially collects data that the user frequently accessed in the past. For example, the collection unit preferentially collects data that the user frequently accessed in the past. The collection unit can also determine the priority of collection based on data accessed by the user in a specific time period. For example, the collection unit determines the priority of collection based on data accessed by the user in a specific time period. Furthermore, the collection unit can also preferentially collect data related to a specific event or anniversary by the user. For example, the collection unit preferentially collects data related to a specific event or anniversary by the user. As a result, more relevant data can be collected by optimizing data collection based on the user's past behavior history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavior history data to the generation AI and cause the generation AI to execute optimization of data collection.

[0039] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting data. For example, the collection unit preferentially collects data related to the user's current living situation. For example, the collection unit preferentially collects data related to the user's current living situation. The collection unit can also filter and collect data related to the user's field of interest. For example, the collection unit filters and collects data related to the user's field of interest. Furthermore, the collection unit can also exclude unnecessary data according to the user's current living situation. For example, the collection unit excludes unnecessary data according to the user's current living situation. As a result, more relevant data can be collected by filtering data based on the user's current living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation and field of interest data to the generation AI and cause the generation AI to filter the data.

[0040] The collection unit can collect highly relevant data preferentially based on the geographical location information of the user when collecting data. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. In addition, when the user is traveling, the collection unit can also preferentially collect data related to the travel destination. For example, when the user is traveling, the collection unit preferentially collects data related to the travel destination. In addition, when the user is at home, the collection unit can also preferentially collect data of memories related to the home. For example, when the user is at home, the collection unit preferentially collects data of memories related to the home. In this way, by collecting data based on the geographical location information of the user, more relevant data can be collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the determination of the priority order of data collection.

[0041] The collection unit can analyze the user's social media activities and collect related data at the time of data collection. The collection unit, for example, collects photos and videos shared by the user on social media. For example, the collection unit collects photos and videos shared by the user on social media. The collection unit can also collect conversation logs made by the user on social media. For example, the collection unit collects conversation logs made by the user on social media. Furthermore, the collection unit can also collect data related to accounts the user follows on social media. For example, the collection unit collects data related to accounts the user follows on social media. In this way, by collecting data based on the user's social media activities, more relevant data can be collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to perform optimization of data collection.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit, for example, performs a detailed analysis on important data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general data. For example, the analysis unit performs a simplified analysis on general data. Furthermore, the analysis unit can also perform a detailed analysis on data in which the user is highly interested. For example, the analysis unit performs a detailed analysis on data in which the user is highly interested. As a result, by adjusting the level of detail of the analysis based on the importance of the data, it is possible to perform a detailed analysis on more important data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that evaluates the importance of data and adjusts the level of detail of the analysis based on the evaluation result.

[0043] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an image analysis algorithm to photo data. For example, the analysis unit applies an image analysis algorithm to photo data. The analysis unit can also apply a natural language processing algorithm to text data. For example, the analysis unit applies a natural language processing algorithm to text data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a voice analysis algorithm to voice data. This allows for more accurate analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that identifies the category of data and applies an analysis algorithm according to the category.

[0044] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, preferentially analyzes the most recently collected data. For example, the analysis unit preferentially analyzes the most recently collected data. The analysis unit can also preferentially analyze data related to a specific event. For example, the analysis unit preferentially analyzes data related to a specific event. The analysis unit can also preferentially analyze data for a period specified by the user. For example, the analysis unit preferentially analyzes data for a period specified by the user. In this way, by determining the priority of the analysis based on the collection time of the data, it is possible to preferentially analyze data with higher relevance. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that evaluates the collection time of the data and determines the priority of the analysis based on the evaluation result.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also prioritize analysis of data in which the user is highly interested. For example, the analysis unit prioritizes analysis of data in which the user is highly interested. Furthermore, the analysis unit can also adjust the order of analysis based on the importance of data. For example, the analysis unit adjusts the order of analysis based on the importance of data. As a result, by adjusting the order of analysis based on the relevance of data, data with higher relevance can be analyzed with higher priority. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that evaluates the relevance of data and adjusts the order of analysis based on the evaluation result.

[0046] The generation unit can adjust the level of detail of the message to be generated based on the importance of the data at the time of generation. The generation unit generates, for example, a detailed message for important data. For example, the generation unit generates a detailed message for important data. The generation unit can also generate a simple message for general data. For example, the generation unit generates a simple message for general data. Furthermore, the generation unit can also generate a detailed message for data in which the user is highly interested. For example, the generation unit generates a detailed message for data in which the user is highly interested. As a result, by adjusting the level of detail of the message based on the importance of the data, a detailed message can be generated for more important data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a message using an AI model that evaluates the importance of data and adjusts the level of detail of the message based on the evaluation result.

[0047] The generation unit can apply different generation algorithms depending on the category of data at the time of generation. For example, the generation unit applies an image generation algorithm to photo data. For example, the generation unit applies an image generation algorithm to photo data. The generation unit can also apply a natural language generation algorithm to text data. For example, the generation unit applies a natural language generation algorithm to text data. Furthermore, the generation unit can also apply a voice generation algorithm to voice data. For example, the generation unit applies a voice generation algorithm to voice data. In this way, a more accurate message can be generated by applying an appropriate generation algorithm depending on the category of data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a message using an AI model that identifies the category of data and applies a generation algorithm according to the category.

[0048] The generation unit can determine the priority of the message to be generated based on the collection time of the data at the time of generation. The generation unit generates a message based on, for example, recently collected data. For example, the generation unit generates a message based on recently collected data. The generation unit can also generate a message based on data related to a specific event. For example, the generation unit generates a message based on data related to a specific event. Furthermore, the generation unit can also generate a message based on data for a period specified by the user. For example, the generation unit generates a message based on data for a period specified by the user. In this way, by determining the priority of the message based on the collection time of the data, a more relevant message can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a message using an AI model that evaluates the collection time of the data and determines the priority of the message based on the evaluation result.

[0049] The generation unit can adjust the order of messages to be generated based on the relevance of data at the time of generation. The generation unit generates a message based on, for example, highly relevant data. For example, the generation unit generates a message based on highly relevant data. The generation unit can also generate a message based on data in which the user is highly interested. For example, the generation unit generates a message based on data in which the user is highly interested. Furthermore, the generation unit can adjust the order of messages based on the importance of data. For example, the generation unit adjusts the order of messages based on the importance of data. As a result, a more highly relevant message can be generated by adjusting the order of messages based on the relevance of data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a message using an AI model that evaluates the relevance of data and adjusts the order of messages based on the evaluation result.

[0050] The reproduction unit can adjust the level of detail of the reproduced appearance based on the importance of the data at the time of reproduction. For example, the reproduction unit reproduces a detailed appearance for important data. For example, the reproduction unit reproduces a detailed appearance for important data. The reproduction unit can also reproduce a simplified appearance for general data. For example, the reproduction unit reproduces a simplified appearance for general data. Furthermore, the reproduction unit can also reproduce a detailed appearance for data in which the user is highly interested. For example, the reproduction unit reproduces a detailed appearance for data in which the user is highly interested. As a result, by adjusting the level of detail of the reproduced appearance based on the importance of the data, a detailed appearance can be reproduced for more important data. Part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI or may be performed without using AI. For example, the reproduction unit can reproduce the appearance using an AI model that evaluates the importance of data and adjusts the level of detail of the reproduced appearance based on the evaluation result.

[0051] The reproduction unit can apply different reproduction algorithms depending on the category of data during reproduction. For example, the reproduction unit applies an image reproduction algorithm to photo data. For example, the reproduction unit applies an image reproduction algorithm to photo data. The reproduction unit can also apply a natural language reproduction algorithm to text data. For example, the reproduction unit applies a natural language reproduction algorithm to text data. Furthermore, the reproduction unit can also apply a voice reproduction algorithm to voice data. For example, the reproduction unit applies a voice reproduction algorithm to voice data. This allows for more accurate reproduction of the appearance by applying an appropriate reproduction algorithm depending on the category of data. Some or all of the above-mentioned processing in the reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproduction unit can reproduce the appearance using an AI model that identifies the category of data and applies a reproduction algorithm according to the category.

[0052] At the time of reproduction, the reproduction unit can determine the priority of the appearance to be reproduced based on the collection time of data. The reproduction unit reproduces the appearance based on, for example, recently collected data. For example, the reproduction unit reproduces the appearance based on recently collected data. The reproduction unit can also reproduce the appearance based on data related to a specific event. For example, the reproduction unit reproduces the appearance based on data related to a specific event. Furthermore, the reproduction unit can also reproduce the appearance based on data for a period specified by the user. For example, the reproduction unit reproduces the appearance based on data for a period specified by the user. In this way, by determining the priority of the appearance to be reproduced based on the collection time of data, a more relevant appearance can be reproduced. Part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproduction unit can reproduce the appearance using an AI model that evaluates the collection time of data and determines the priority of the appearance to be reproduced based on the evaluation result.

[0053] The reproduction unit can adjust the order of the appearances to be reproduced based on the relevance of the data at the time of reproduction. The reproduction unit reproduces the appearance based on, for example, highly relevant data. For example, the reproduction unit reproduces the appearance based on highly relevant data. The reproduction unit can also reproduce the appearance based on data in which the user is highly interested. For example, the reproduction unit reproduces the appearance based on data in which the user is highly interested. Furthermore, the reproduction unit can adjust the order of the appearances based on the importance of the data. For example, the reproduction unit adjusts the order of the appearances based on the importance of the data. As a result, by adjusting the order of the appearances to be reproduced based on the relevance of the data, it is possible to reproduce an appearance with a higher relevance. Part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproduction unit can reproduce the appearance using an AI model that evaluates the relevance of the data and adjusts the order of the appearances to be reproduced based on the evaluation result.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can determine the priority of data collection based on the user's past behavior history. For example, the collection unit preferentially collects data that the user frequently accessed in the past. The collection unit can also determine the priority of collection based on data that the user accessed during a specific time period. Furthermore, the collection unit can also preferentially collect data related to a specific event or anniversary that the user is involved in. This allows more relevant data to be collected by optimizing data collection based on the user's past behavior history.

[0056] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data. Also, a simplified analysis can be performed on general data. Furthermore, a detailed analysis can be performed on data that the user is highly interested in. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on more important data.

[0057] The generation unit can apply different generation algorithms depending on the data category. For example, an image generation algorithm can be applied to photo data. A natural language generation algorithm can also be applied to text data. Furthermore, a voice generation algorithm can be applied to voice data. In this way, by applying an appropriate generation algorithm depending on the data category, a more accurate message can be generated.

[0058] The reproducing unit can determine the priority of the appearance to be reproduced based on the time of data collection. For example, the appearance can be reproduced based on the most recently collected data. The reproducing unit can also reproduce the appearance based on data related to a specific event. Furthermore, the reproducing unit can also reproduce the appearance based on data for a period specified by the user. In this way, by determining the priority of the appearance to be reproduced based on the time of data collection, it is possible to reproduce a more relevant appearance.

[0059] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user. For example, when the user is in a specific location, data related to that location is preferentially collected. Also, when the user is traveling, data related to the travel destination can be preferentially collected. Furthermore, when the user is at home, data of memories related to the home can be preferentially collected. In this way, by collecting data based on the geographical location information of the user, more relevant data can be collected.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The collection unit collects data. The data includes text data, audio data, image data, video data, etc. With the consent of the family, the collection unit collects data such as emails, photos, videos, voicemails, conversation logs of messenger apps, letters, etc. stored on the smartphone or computer at the family home. For example, emails and photos from the smartphone, videos and voicemails stored on the computer, conversation logs of messenger apps, and letters are collected. Step 2: The analysis unit analyzes the data collected by the collection unit and reproduces the words, speech patterns, and facial expressions of the deceased parents or loved ones. The analysis is carried out using methods such as natural language processing, voice analysis, and image analysis. For example, text data is analyzed using natural language processing technology, voice data is analyzed using voice analysis technology, and image data is analyzed using image analysis technology. Step 3: The generation unit creates a messenger app account based on the data analyzed by the analysis unit, allowing the user to enjoy conversations. The generation is performed based on an account creation procedure and a message generation algorithm. For example, a messenger app account is created based on the analyzed data, and a reply to a message sent by the user is generated using a message generation algorithm. Step 4: The reproduction unit reproduces the appearance using AR technology based on the data generated by the generation unit. Reproduction is performed using methods such as voice synthesis, facial expression recognition, and motion reproduction. For example, voice is reproduced using voice synthesis technology, facial expressions are reproduced using facial expression recognition technology, and motion is reproduced using motion reproduction technology. Some or all of the processing in the reproduction unit may be performed using AI.

[0062] (Example 2) The system according to the embodiment of the present invention reproduces memories with deceased parents or loved ones, and allows users to talk with them 24 hours a day when they feel lonely. With the consent of the family, this system uses a generation AI to learn the emails, photos, videos, voicemails, conversation logs and letters from SNS such as messenger apps that are lying dormant in the family home, and integrates them into one messenger app account. This allows users to enjoy conversations whenever they feel lonely. In addition, as an option, AR technology is used to reproduce the appearance through the glass, providing a more realistic conversation experience. For example, with the consent of the family, a user collects data such as emails, photos, videos, voicemails, conversation logs from messenger apps, and letters stored on the smartphone or computer at the family home. Next, these data are input into the generation AI and learned. The generation AI analyzes the collected data and reproduces the language, speaking style, and facial expressions of the deceased parents or loved ones. A messenger app account is created based on the data learned by the generation AI. Through this account, the user can enjoy conversations at any time. For example, if a user sends a message saying "I'm tired from work today," the AI ​​will reply in the parents' voice, saying "Thank you for your hard work, have a good rest." In addition, by using AR technology as an option, the appearance of the parents can be reproduced through the glasses. For example, when the user wears the AR glasses, the image of the parents appears in front of the user, and the user can experience as if they are actually talking to the parents. This allows the user to enjoy a more realistic conversation experience. This service provides a new way to cherish memories of deceased parents and loved ones and heal loneliness. Users can enjoy conversations with their loved ones anytime, anywhere, providing emotional support. This allows the system to allow users to enjoy conversations with their deceased parents and loved ones 24 hours a day.

[0063] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a reproduction unit. The collection unit collects data. The data includes, for example, text data, voice data, image data, video data, and the like, but is not limited to such examples. For example, with the consent of the family, the collection unit collects data such as emails, photos, videos, voicemails, conversation logs of messenger apps, and letters stored in a smartphone or computer at the parent's home. For example, the collection unit collects emails and photos from a smartphone with the consent of the family. The collection unit can also collect videos and voicemails stored in a computer. Furthermore, the collection unit can also collect conversation logs and letters of messenger apps. The analysis unit analyzes the data collected by the collection unit and reproduces the language, speaking style, facial expressions, and the like of the deceased parents or important people. The analysis is performed by, for example, natural language processing, voice analysis, image analysis, and the like, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze the collected text data. The analysis unit can also use voice analysis technology to analyze the collected voice data. Furthermore, the analysis unit can also analyze the collected image data using image analysis technology. The generation unit creates a messenger app account based on the data analyzed by the analysis unit, so that the user can enjoy conversations at any time. The generation is performed, for example, based on an account creation procedure or a message generation algorithm, but is not limited to such examples. For example, the generation unit creates a messenger app account based on the analyzed data. The generation unit can also generate a reply to a message sent by the user using a message generation algorithm. The reproduction unit reproduces the appearance using AR technology based on the data generated by the generation unit. The reproduction is performed, for example, by a method such as voice synthesis, facial expression recognition, and motion reproduction, but is not limited to such examples. For example, the reproduction unit reproduces the voice of the deceased parents or a loved one using voice synthesis technology. The reproduction unit can also reproduce the facial expression of the deceased parents or a loved one using facial expression recognition technology. Furthermore, the reproduction unit can also reproduce the motion of the deceased parents or a loved one using motion reproduction technology.As a result, the system according to the embodiment allows the user to enjoy conversation with deceased parents or loved ones 24 hours a day. Some or all of the above-mentioned processing in the reproducing unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproducing unit can reproduce the appearance using an AI model that reproduces the appearance using the data generated by the generating unit as input.

[0064] The collection unit collects data. Examples of the data include, but are not limited to, text data, voice data, image data, and video data. For example, with the consent of the family, the collection unit collects data such as emails, photos, videos, voicemails, conversation logs of messenger apps, and letters stored in the smartphone or computer at the parent's home. For example, the collection unit collects emails and photos from the smartphone with the consent of the family. The collection unit can also collect videos and voicemails stored in the computer. Furthermore, the collection unit can collect conversation logs and letters of messenger apps. The collection unit can use dedicated software or hardware to efficiently collect these data. For example, the data can be automatically collected and uploaded to a cloud server through a dedicated application installed on the smartphone or computer. This allows the data collection work to be performed quickly and accurately. In addition, the collection unit can protect the data using encryption technology to ensure the privacy and security of the data. For example, the collected data is encrypted at the time of collection, and is stored in an encrypted state when it is saved in the cloud server. This can prevent data leakage and unauthorized access. Furthermore, the collection unit can build a database for eliminating data duplication and efficiently managing data. For example, to prevent the same email or photo from being collected multiple times, the collection unit can calculate the hash value of the data and automatically detect and eliminate duplicate data. This allows for efficient operation of the database.

[0065] The analysis unit analyzes the data collected by the collection unit and reproduces the language, speech, facial expressions, etc. of the deceased parents or important people. The analysis is performed by, for example, natural language processing, voice analysis, image analysis, etc., but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze the collected text data. The analysis unit can also use voice analysis technology to analyze the collected voice data. Furthermore, the analysis unit can also use image analysis technology to analyze the collected image data. The analysis unit combines these technologies to achieve more accurate reproduction. For example, natural language processing technology is used to extract specific language and speech patterns from the text data, and voice analysis technology is used to analyze specific voice characteristics and intonation from the voice data. Furthermore, image analysis technology is used to extract specific facial expressions and movement patterns from the image data. As a result, the analysis unit can comprehensively analyze the collected data and reproduce the language, speech, facial expressions, etc. of the deceased parents or important people with high accuracy. Furthermore, the analysis unit can continuously improve the accuracy of the analysis by using machine learning algorithms. For example, the collected data is used to train a machine learning model and improve the accuracy of the analysis. The analysis unit can also collect feedback from users and continuously improve the accuracy and reproducibility of the analysis results. This allows the analysis unit to always use the latest technology and data to achieve highly accurate analysis and improve the performance of the entire system.

[0066] The generation unit creates an account for the messenger app based on the data analyzed by the analysis unit, so that the user can enjoy a conversation at any time. The generation is performed, for example, based on the procedure for creating an account and an algorithm for generating a message, but is not limited to such examples. For example, the generation unit creates an account for the messenger app based on the analyzed data. The generation unit can also generate a reply to a message sent by the user using a message generation algorithm. The generation unit uses these algorithms to enable the user to enjoy a natural conversation. For example, the generation unit uses natural language generation technology to generate an appropriate reply to the user's message. The generation unit can also use voice synthesis technology to generate a reply in the voice of the deceased parents or a loved one based on the analyzed voice data. Furthermore, the generation unit can also use image generation technology to reproduce the facial expressions and movements of the deceased parents or a loved one based on the analyzed image data. This allows the generation unit to provide an experience as if the user were talking to the deceased parents or a loved one. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and naturalness of the generation algorithm. For example, the user can evaluate the generated reply and adjust the generation algorithm based on the evaluation. This enables the generation unit to always use the latest technology and data to provide a highly accurate, natural conversational experience and improve the performance of the entire system.

[0067] The reproduction unit reproduces the appearance using AR technology based on the data generated by the generation unit. The reproduction is performed by, for example, voice synthesis, facial expression recognition, motion reproduction, and the like, but is not limited to such examples. For example, the reproduction unit reproduces the voice of the deceased parents or a loved one using voice synthesis technology. The reproduction unit can also reproduce the facial expression of the deceased parents or a loved one using facial expression recognition technology. Furthermore, the reproduction unit can reproduce the motion of the deceased parents or a loved one using motion reproduction technology. The reproduction unit combines these technologies to achieve a more realistic reproduction. For example, the reproduction unit reproduces the voice of the deceased parents or a loved one based on the analyzed voice data using voice synthesis technology, and reproduces the facial expression of the deceased parents or a loved one based on the analyzed image data using facial expression recognition technology. Furthermore, the motion reproduction technology reproduces the motion of the deceased parents or a loved one based on the analyzed video data. This allows the reproduction unit to provide an experience as if the user were face-to-face with the deceased parents or a loved one. Furthermore, the reproduction unit can collect user feedback and continuously improve the accuracy and realism of the reproduction technology. For example, the user can evaluate the reproduced appearance and adjust the reproduction technology based on the evaluation. This allows the reproduction unit to always use the latest technology and data to provide highly accurate and realistic reproduction, improving the performance of the entire system. Some or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproduction unit can use the data generated by the generation unit as input and reproduce the appearance using an AI model that reproduces the appearance.

[0068] The collection unit can collect data of emails, photos, videos, voicemails, conversation logs of messenger apps, and letters stored in a smartphone or computer at the family home with the consent of the family. For example, the collection unit collects emails and photos from the smartphone with the consent of the family. For example, the collection unit collects emails from the smartphone with the consent of the family. The collection unit can also collect photos from the smartphone with the consent of the family. Furthermore, the collection unit can collect videos and voicemails stored in the computer with the consent of the family. For example, the collection unit collects videos stored in the computer with the consent of the family. The collection unit can also collect voicemails stored in the computer with the consent of the family. Furthermore, the collection unit can collect conversation logs and letters from messenger apps with the consent of the family. For example, the collection unit collects conversation logs from messenger apps with the consent of the family. The collection unit can also collect letters with the consent of the family. In this way, data can be collected while protecting privacy by collecting data with the consent of the family. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may collect emails and photos from smartphones with the consent of the family, input the collected data into AI, and have the AI ​​collect the data.

[0069] The analysis unit can analyze the collected data using the generation AI and reproduce the language, speech, and facial expressions of the deceased parents or important people. The analysis unit can analyze the collected text data using, for example, natural language processing technology. For example, the analysis unit can analyze the collected text data using natural language processing technology. The analysis unit can also analyze the collected voice data using voice analysis technology. For example, the analysis unit can analyze the collected voice data using voice analysis technology. Furthermore, the analysis unit can also analyze the collected image data using image analysis technology. For example, the analysis unit can analyze the collected image data using image analysis technology. As a result, the language, speech, and facial expressions of the deceased parents or important people can be reproduced with high accuracy by using the generation AI. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the analysis of the data.

[0070] The generation unit can create a messenger app account based on the analyzed data, so that the user can enjoy a conversation at any time. The generation unit, for example, creates a messenger app account based on the analyzed data. For example, the generation unit creates a messenger app account based on the analyzed data. The generation unit can also generate a reply to a message sent by the user using a message generation algorithm. For example, the generation unit generates a reply to a message sent by the user using a message generation algorithm. This allows the user to enjoy a conversation at any time. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the analyzed data to a generation AI and cause the generation AI to create a messenger app account and generate a message.

[0071] The reproduction unit can reproduce the appearance of the parents through the glass using AR technology. The reproduction unit reproduces the voice of the deceased parents or a loved one, for example, using voice synthesis technology. For example, the reproduction unit reproduces the voice of the deceased parents or a loved one, using voice synthesis technology. The reproduction unit can also reproduce the facial expression of the deceased parents or a loved one, using facial expression recognition technology. For example, the reproduction unit reproduces the facial expression of the deceased parents or a loved one, using facial expression recognition technology. The reproduction unit can also reproduce the motion of the deceased parents or a loved one, using motion reproduction technology. For example, the reproduction unit reproduces the motion of the deceased parents or a loved one, using motion reproduction technology. This allows a more realistic conversation experience to be provided by using AR technology. Part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproduction unit can input the data generated by the generation unit to the generation AI and cause the generation AI to reproduce the appearance.

[0072] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user feels lonely, the collection unit immediately starts data collection. For example, when the user feels lonely, the collection unit immediately starts data collection. In addition, when the user is busy, the collection unit can postpone data collection and notify at an appropriate time. For example, when the user is busy, the collection unit postpones data collection and notifies at an appropriate time. Furthermore, when the user is relaxed, the collection unit can provide a guide for smoothly collecting data. For example, when the user is relaxed, the collection unit provides a guide for smoothly collecting data. This allows data to be collected at a more appropriate time by adjusting the timing of data collection according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0073] The collection unit can optimize the type of data to be collected based on the user's past behavior history. For example, the collection unit preferentially collects data that the user frequently accessed in the past. For example, the collection unit preferentially collects data that the user frequently accessed in the past. The collection unit can also determine the priority of collection based on data accessed by the user in a specific time period. For example, the collection unit determines the priority of collection based on data accessed by the user in a specific time period. Furthermore, the collection unit can also preferentially collect data related to a specific event or anniversary by the user. For example, the collection unit preferentially collects data related to a specific event or anniversary by the user. As a result, more relevant data can be collected by optimizing data collection based on the user's past behavior history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavior history data to the generation AI and cause the generation AI to execute optimization of data collection.

[0074] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting data. For example, the collection unit preferentially collects data related to the user's current living situation. For example, the collection unit preferentially collects data related to the user's current living situation. The collection unit can also filter and collect data related to the user's field of interest. For example, the collection unit filters and collects data related to the user's field of interest. Furthermore, the collection unit can also exclude unnecessary data according to the user's current living situation. For example, the collection unit excludes unnecessary data according to the user's current living situation. As a result, more relevant data can be collected by filtering data based on the user's current living situation and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's current living situation and field of interest data to the generation AI and cause the generation AI to filter the data.

[0075] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling sad, the collection unit preferentially collects data related to emotion. For example, when the user is feeling sad, the collection unit preferentially collects data related to emotion. In addition, when the user is feeling happy, the collection unit can also preferentially collect data related to positive memories. For example, when the user is feeling happy, the collection unit preferentially collects data related to positive memories. In addition, when the user is feeling anxious, the collection unit can also preferentially collect data that gives a sense of security. For example, when the user is feeling anxious, the collection unit preferentially collects data that gives a sense of security. In this way, more appropriate data can be collected by determining the priority of data according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0076] The collection unit can collect highly relevant data preferentially based on the geographical location information of the user when collecting data. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. In addition, when the user is traveling, the collection unit can also preferentially collect data related to the travel destination. For example, when the user is traveling, the collection unit preferentially collects data related to the travel destination. In addition, when the user is at home, the collection unit can also preferentially collect data of memories related to the home. For example, when the user is at home, the collection unit preferentially collects data of memories related to the home. In this way, by collecting data based on the geographical location information of the user, more relevant data can be collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute the determination of the priority order of data collection.

[0077] The collection unit can analyze the user's social media activities and collect related data at the time of data collection. The collection unit, for example, collects photos and videos shared by the user on social media. For example, the collection unit collects photos and videos shared by the user on social media. The collection unit can also collect conversation logs made by the user on social media. For example, the collection unit collects conversation logs made by the user on social media. Furthermore, the collection unit can also collect data related to accounts the user follows on social media. For example, the collection unit collects data related to accounts the user follows on social media. In this way, by collecting data based on the user's social media activities, more relevant data can be collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to perform optimization of data collection.

[0078] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user feels sad, the analysis unit expresses the analysis result in gentle language. For example, when the user feels sad, the analysis unit expresses the analysis result in gentle language. Furthermore, when the user feels happy, the analysis unit can also express the analysis result in cheerful language. For example, when the user feels happy, the analysis unit expresses the analysis result in cheerful language. Furthermore, when the user feels anxious, the analysis unit can also express the analysis result in language that gives a sense of security. For example, when the user feels anxious, the analysis unit expresses the analysis result in language that gives a sense of security. In this way, by adjusting the expression method of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit, for example, performs a detailed analysis on important data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on general data. For example, the analysis unit performs a simplified analysis on general data. Furthermore, the analysis unit can also perform a detailed analysis on data in which the user is highly interested. For example, the analysis unit performs a detailed analysis on data in which the user is highly interested. As a result, by adjusting the level of detail of the analysis based on the importance of the data, it is possible to perform a detailed analysis on more important data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that evaluates the importance of data and adjusts the level of detail of the analysis based on the evaluation result.

[0080] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an image analysis algorithm to photo data. For example, the analysis unit applies an image analysis algorithm to photo data. The analysis unit can also apply a natural language processing algorithm to text data. For example, the analysis unit applies a natural language processing algorithm to text data. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a voice analysis algorithm to voice data. This allows for more accurate analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that identifies the category of data and applies an analysis algorithm according to the category.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit performs a short and to the point analysis. For example, when the user is in a hurry, the analysis unit performs a short and to the point analysis. In addition, the analysis unit can also perform a detailed analysis when the user is relaxed. For example, when the user is relaxed, the analysis unit performs a detailed analysis. In addition, the analysis unit can also perform a visually stimulating analysis when the user is excited. For example, when the user is excited, the analysis unit performs a visually stimulating analysis. This allows the length of the analysis to be adjusted according to the user's emotion, thereby providing a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotion data to the generation AI and have the generation AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, preferentially analyzes the most recently collected data. For example, the analysis unit preferentially analyzes the most recently collected data. The analysis unit can also preferentially analyze data related to a specific event. For example, the analysis unit preferentially analyzes data related to a specific event. The analysis unit can also preferentially analyze data for a period specified by the user. For example, the analysis unit preferentially analyzes data for a period specified by the user. In this way, by determining the priority of the analysis based on the collection time of the data, it is possible to preferentially analyze data with higher relevance. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that evaluates the collection time of the data and determines the priority of the analysis based on the evaluation result.

[0083] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also prioritize analysis of data in which the user is highly interested. For example, the analysis unit prioritizes analysis of data in which the user is highly interested. Furthermore, the analysis unit can also adjust the order of analysis based on the importance of data. For example, the analysis unit adjusts the order of analysis based on the importance of data. As a result, by adjusting the order of analysis based on the relevance of data, data with higher relevance can be analyzed with higher priority. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that evaluates the relevance of data and adjusts the order of analysis based on the evaluation result.

[0084] The generation unit can estimate the user's emotion and adjust the expression method of the generated message based on the estimated user's emotion. For example, when the user feels sad, the generation unit generates a message in gentle language. For example, when the user feels sad, the generation unit generates a message in gentle language. In addition, when the user feels happy, the generation unit can also generate a message in cheerful language. For example, when the user feels happy, the generation unit generates a message in cheerful language. In addition, when the user feels anxious, the generation unit can also generate a message in language that gives a sense of security. For example, when the user feels anxious, the generation unit generates a message in language that gives a sense of security. In this way, by adjusting the expression method of the message according to the user's emotion, a more appropriate message can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the message is expressed.

[0085] The generation unit can adjust the level of detail of the message to be generated based on the importance of the data at the time of generation. The generation unit generates, for example, a detailed message for important data. For example, the generation unit generates a detailed message for important data. The generation unit can also generate a simple message for general data. For example, the generation unit generates a simple message for general data. Furthermore, the generation unit can also generate a detailed message for data in which the user is highly interested. For example, the generation unit generates a detailed message for data in which the user is highly interested. As a result, by adjusting the level of detail of the message based on the importance of the data, a detailed message can be generated for more important data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a message using an AI model that evaluates the importance of data and adjusts the level of detail of the message based on the evaluation result.

[0086] The generation unit can apply different generation algorithms depending on the category of data at the time of generation. For example, the generation unit applies an image generation algorithm to photo data. For example, the generation unit applies an image generation algorithm to photo data. The generation unit can also apply a natural language generation algorithm to text data. For example, the generation unit applies a natural language generation algorithm to text data. Furthermore, the generation unit can also apply a voice generation algorithm to voice data. For example, the generation unit applies a voice generation algorithm to voice data. In this way, a more accurate message can be generated by applying an appropriate generation algorithm depending on the category of data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a message using an AI model that identifies the category of data and applies a generation algorithm according to the category.

[0087] The generation unit can estimate the user's emotion and adjust the length of the message to be generated based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short message that is to the point. For example, when the user is in a hurry, the generation unit generates a short message that is to the point. In addition, the generation unit can also generate a longer message including a detailed explanation when the user is relaxed. For example, when the user is relaxed, the generation unit generates a longer message including a detailed explanation. In addition, the generation unit can also generate a visually stimulating message when the user is excited. For example, when the user is excited, the generation unit generates a visually stimulating message. This allows a more appropriate message to be generated by adjusting the length of the message according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and have the generation AI adjust the length of the message.

[0088] The generation unit can determine the priority of the message to be generated based on the collection time of the data at the time of generation. The generation unit generates a message based on, for example, recently collected data. For example, the generation unit generates a message based on recently collected data. The generation unit can also generate a message based on data related to a specific event. For example, the generation unit generates a message based on data related to a specific event. Furthermore, the generation unit can also generate a message based on data for a period specified by the user. For example, the generation unit generates a message based on data for a period specified by the user. In this way, by determining the priority of the message based on the collection time of the data, a more relevant message can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a message using an AI model that evaluates the collection time of the data and determines the priority of the message based on the evaluation result.

[0089] The generation unit can adjust the order of messages to be generated based on the relevance of data at the time of generation. The generation unit generates a message based on, for example, highly relevant data. For example, the generation unit generates a message based on highly relevant data. The generation unit can also generate a message based on data in which the user is highly interested. For example, the generation unit generates a message based on data in which the user is highly interested. Furthermore, the generation unit can adjust the order of messages based on the importance of data. For example, the generation unit adjusts the order of messages based on the importance of data. As a result, a more highly relevant message can be generated by adjusting the order of messages based on the relevance of data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a message using an AI model that evaluates the relevance of data and adjusts the order of messages based on the evaluation result.

[0090] The reproducing unit can estimate the user's emotion and adjust the expression method of the reproduced appearance based on the estimated user's emotion. For example, when the user feels sad, the reproducing unit reproduces the user with a gentle expression. For example, when the user feels sad, the reproducing unit reproduces the user with a gentle expression. In addition, when the user feels happy, the reproducing unit can also reproduce the user with a bright expression. For example, when the user feels happy, the reproducing unit reproduces the user with a bright expression. In addition, when the user feels anxious, the reproducing unit can also reproduce the user with an expression that gives a sense of security. For example, when the user feels anxious, the reproducing unit reproduces the user with an expression that gives a sense of security. In this way, by adjusting the expression method of the reproduced appearance according to the user's emotion, a more appropriate appearance can be reproduced. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reproducing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproduction unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the reproduced appearance is expressed.

[0091] The reproduction unit can adjust the level of detail of the reproduced appearance based on the importance of the data at the time of reproduction. For example, the reproduction unit reproduces a detailed appearance for important data. For example, the reproduction unit reproduces a detailed appearance for important data. The reproduction unit can also reproduce a simplified appearance for general data. For example, the reproduction unit reproduces a simplified appearance for general data. Furthermore, the reproduction unit can also reproduce a detailed appearance for data in which the user is highly interested. For example, the reproduction unit reproduces a detailed appearance for data in which the user is highly interested. As a result, by adjusting the level of detail of the reproduced appearance based on the importance of the data, a detailed appearance can be reproduced for more important data. Part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI or may be performed without using AI. For example, the reproduction unit can reproduce the appearance using an AI model that evaluates the importance of data and adjusts the level of detail of the reproduced appearance based on the evaluation result.

[0092] The reproduction unit can apply different reproduction algorithms depending on the category of data during reproduction. For example, the reproduction unit applies an image reproduction algorithm to photo data. For example, the reproduction unit applies an image reproduction algorithm to photo data. The reproduction unit can also apply a natural language reproduction algorithm to text data. For example, the reproduction unit applies a natural language reproduction algorithm to text data. Furthermore, the reproduction unit can also apply a voice reproduction algorithm to voice data. For example, the reproduction unit applies a voice reproduction algorithm to voice data. This allows for more accurate reproduction of the appearance by applying an appropriate reproduction algorithm depending on the category of data. Some or all of the above-mentioned processing in the reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproduction unit can reproduce the appearance using an AI model that identifies the category of data and applies a reproduction algorithm according to the category.

[0093] The reproducing unit can estimate the user's emotion and adjust the length of the appearance to be reproduced based on the estimated user's emotion. For example, when the user is in a hurry, the reproducing unit reproduces a short appearance that is to the point. For example, when the user is in a hurry, the reproducing unit reproduces a short appearance that is to the point. In addition, the reproducing unit can reproduce a longer appearance including detailed explanations when the user is relaxed. For example, when the user is relaxed, the reproducing unit reproduces a longer appearance including detailed explanations. In addition, the reproducing unit can reproduce a visually stimulating appearance when the user is excited. For example, when the user is excited, the reproducing unit reproduces a visually stimulating appearance. In this way, by adjusting the length of the appearance to be reproduced according to the user's emotion, a more appropriate appearance can be reproduced. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reproducing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproduction unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the reproduced appearance.

[0094] At the time of reproduction, the reproduction unit can determine the priority of the appearance to be reproduced based on the collection time of data. The reproduction unit reproduces the appearance based on, for example, recently collected data. For example, the reproduction unit reproduces the appearance based on recently collected data. The reproduction unit can also reproduce the appearance based on data related to a specific event. For example, the reproduction unit reproduces the appearance based on data related to a specific event. Furthermore, the reproduction unit can also reproduce the appearance based on data for a period specified by the user. For example, the reproduction unit reproduces the appearance based on data for a period specified by the user. In this way, by determining the priority of the appearance to be reproduced based on the collection time of data, a more relevant appearance can be reproduced. Part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproduction unit can reproduce the appearance using an AI model that evaluates the collection time of data and determines the priority of the appearance to be reproduced based on the evaluation result.

[0095] The reproduction unit can adjust the order of the appearances to be reproduced based on the relevance of the data at the time of reproduction. The reproduction unit reproduces the appearance based on, for example, highly relevant data. For example, the reproduction unit reproduces the appearance based on highly relevant data. The reproduction unit can also reproduce the appearance based on data in which the user is highly interested. For example, the reproduction unit reproduces the appearance based on data in which the user is highly interested. Furthermore, the reproduction unit can adjust the order of the appearances based on the importance of the data. For example, the reproduction unit adjusts the order of the appearances based on the importance of the data. As a result, by adjusting the order of the appearances to be reproduced based on the relevance of the data, it is possible to reproduce an appearance with a higher relevance. Part or all of the above-mentioned processing in the reproduction unit may be performed, for example, using AI, or may be performed without using AI. For example, the reproduction unit can reproduce the appearance using an AI model that evaluates the relevance of the data and adjusts the order of the appearances to be reproduced based on the evaluation result.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The collection unit can determine the priority of data collection based on the user's past behavior history. For example, the collection unit preferentially collects data that the user frequently accessed in the past. The collection unit can also determine the priority of collection based on data that the user accessed during a specific time period. Furthermore, the collection unit can also preferentially collect data related to a specific event or anniversary that the user is involved in. This allows more relevant data to be collected by optimizing data collection based on the user's past behavior history.

[0098] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on important data. Also, a simplified analysis can be performed on general data. Furthermore, a detailed analysis can be performed on data that the user is highly interested in. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on more important data.

[0099] The generation unit can apply different generation algorithms depending on the data category. For example, an image generation algorithm can be applied to photo data. A natural language generation algorithm can also be applied to text data. Furthermore, a voice generation algorithm can be applied to voice data. In this way, by applying an appropriate generation algorithm depending on the data category, a more accurate message can be generated.

[0100] The reproducing unit can determine the priority of the appearance to be reproduced based on the time of data collection. For example, the appearance can be reproduced based on the most recently collected data. The reproducing unit can also reproduce the appearance based on data related to a specific event. Furthermore, the reproducing unit can also reproduce the appearance based on data for a period specified by the user. In this way, by determining the priority of the appearance to be reproduced based on the time of data collection, it is possible to reproduce a more relevant appearance.

[0101] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user. For example, when the user is in a specific location, data related to that location is preferentially collected. Also, when the user is traveling, data related to the travel destination can be preferentially collected. Furthermore, when the user is at home, data of memories related to the home can be preferentially collected. In this way, by collecting data based on the geographical location information of the user, more relevant data can be collected.

[0102] 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 feeling sad, the analysis results can be presented in kind language. If the user is feeling happy, the analysis results can be presented in cheerful language. Furthermore, if the user is feeling anxious, the analysis results can be presented in language that gives a sense of security. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0103] The generation unit can estimate the user's emotion and adjust the expression method of the generated message based on the estimated user's emotion. For example, if the user is feeling sad, the generation unit can generate a message using kind language. If the user is feeling happy, the generation unit can generate a message using cheerful language. Furthermore, if the user is feeling anxious, the generation unit can generate a message using language that gives a sense of security. In this way, a more appropriate message can be generated by adjusting the expression method of the message according to the user's emotion.

[0104] The reproducing unit can estimate the user's emotion and adjust the expression method of the reproduced appearance based on the estimated user's emotion. For example, if the user feels sad, the expression can be reproduced with a gentle expression. If the user feels happy, the expression can be reproduced with a bright expression. Furthermore, if the user feels anxious, the expression can be reproduced with a feeling of security. In this way, a more appropriate appearance can be reproduced by adjusting the expression method of the reproduced appearance according to the user's emotion.

[0105] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, if the user feels sad, data related to the emotion can be collected preferentially. Also, if the user feels happy, data related to positive memories can be collected preferentially. Furthermore, if the user feels anxious, data that gives a sense of security can be collected preferentially. In this way, by determining the priority of data according to the user's emotion, more appropriate data can be collected.

[0106] The reproducing unit can estimate the user's emotion and adjust the length of the appearance to be reproduced based on the estimated user's emotion. For example, if the user is in a hurry, a short appearance that focuses on the main points can be reproduced. If the user is relaxed, a longer appearance including detailed explanations can be reproduced. Furthermore, if the user is excited, a visually stimulating appearance can be reproduced. In this way, a more appropriate appearance can be reproduced by adjusting the length of the appearance to be reproduced according to the user's emotion.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The collection unit collects data. The data includes text data, audio data, image data, video data, etc. With the consent of the family, the collection unit collects data such as emails, photos, videos, voicemails, conversation logs of messenger apps, letters, etc. stored on the smartphone or computer at the family home. For example, emails and photos from the smartphone, videos and voicemails stored on the computer, conversation logs of messenger apps, and letters are collected. Step 2: The analysis unit analyzes the data collected by the collection unit and reproduces the words, speech patterns, and facial expressions of the deceased parents or loved ones. The analysis is carried out using methods such as natural language processing, voice analysis, and image analysis. For example, text data is analyzed using natural language processing technology, voice data is analyzed using voice analysis technology, and image data is analyzed using image analysis technology. Step 3: The generation unit creates a messenger app account based on the data analyzed by the analysis unit, allowing the user to enjoy conversations. The generation is performed based on an account creation procedure and a message generation algorithm. For example, a messenger app account is created based on the analyzed data, and a reply to a message sent by the user is generated using a message generation algorithm. Step 4: The reproduction unit reproduces the appearance using AR technology based on the data generated by the generation unit. Reproduction is performed using methods such as voice synthesis, facial expression recognition, and motion reproduction. For example, voice is reproduced using voice synthesis technology, facial expressions are reproduced using facial expression recognition technology, and motion is reproduced using motion reproduction technology. Some or all of the processing in the reproduction unit may be performed using AI.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and reproduction 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 data using the camera 42 and microphone 38B of the smart device 14, and transmits the data collected by the control unit 46A to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a messenger app account based on the analyzed data. The reproduction unit is realized, for example, by the control unit 46A of the smart device 14, and reproduces an appearance using AR technology based on the generated data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and reproduction unit 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 data using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data collected by the control unit 46A to the data processing device 12. The analysis unit is realized, for example, by a specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by a specific processing unit 290 of the data processing device 12, and creates a messenger app account based on the analyzed data. The reproduction unit is realized, for example, by the control unit 46A of the smart glasses 214, and reproduces an appearance using AR technology based on the generated data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and reproduction unit 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 data using the camera 42 and microphone 238 of the headset type terminal 314, and transmits the data collected by the control unit 46A to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates an account for a messenger app based on the analyzed data. The reproduction unit is realized, for example, by the control unit 46A of the headset type terminal 314, and reproduces an appearance using AR technology based on the generated data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and reproduction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414, and transmits the data collected by the control unit 46A to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a messenger app account based on the analyzed data. The reproduction unit is realized, for example, by the control unit 46A of the robot 414, and reproduces the appearance using AR technology based on the generated data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A collection unit that collects data; An analysis unit that analyzes the data collected by the collection unit and reproduces the language, speaking style, and facial expressions of the deceased parents or loved ones; A generation unit that creates a messenger app account based on the data analyzed by the analysis unit, allowing the user to enjoy conversations; A reproduction unit that reproduces an appearance using AR technology based on the data generated by the generation unit. A system characterized by: (Appendix 2) The collecting unit includes: With the consent of the family, data such as emails, photos, videos, voicemails, conversation logs from messenger apps, and letters stored on smartphones and computers at the family home will be collected. 2. The system of claim 1, (Appendix 3) The analysis unit is The collected data is analyzed using generative AI to recreate the words, speech patterns, and facial expressions of deceased parents and loved ones. 2. The system of claim 1, (Appendix 4) The generation unit is Create a messenger app account based on the analyzed data, allowing users to enjoy conversations at any time 2. The system of claim 1, (Appendix 5) The reproducing portion comprises: Using AR technology, you can see your parents through the glass 2. The system of claim 1, (Appendix 6) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The collecting unit includes: Optimize the type of data you collect based on users’ past behavior 2. The system of claim 1, (Appendix 8) The collecting unit includes: Filter data collection based on the user's current life situation and interests 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, prioritize collection of data that is more relevant based on the user's geographic location 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 18) The generation unit is Estimate the user's emotions and adjust the way messages are expressed based on the estimated user emotions. 2. The system of claim 1, (Appendix 19) The generation unit is At generation time, adjust the verbosity of generated messages based on the importance of the data 2. The system of claim 1, (Appendix 20) The generation unit is During generation, different generation algorithms are applied depending on the category of data 2. The system of claim 1, (Appendix 21) The generation unit is Estimate the user's emotions and adjust the length of the generated message based on the estimated user emotions. 2. The system of claim 1, (Appendix 22) The generation unit is At generation time, prioritizes the messages to generate based on when the data was collected 2. The system of claim 1, (Appendix 23) The generation unit is At generation time, order generated messages based on data relevance 2. The system of claim 1, (Appendix 24) The reproducing portion comprises: Estimate the user's emotions and adjust the way the person's appearance is expressed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 25) The reproducing portion comprises: During reconstruction, the level of detail of the reconstruction is adjusted based on the importance of the data. 2. The system of claim 1, (Appendix 26) The reproducing portion comprises: During reconstruction, different reconstruction algorithms are applied depending on the category of data. 2. The system of claim 1, (Appendix 27) The reproducing portion comprises: The user's emotion is estimated, and the length of the image to be reproduced is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 28) The reproducing portion comprises: During reconstruction, prioritize which aspects to reconstruct based on when the data was collected. 2. The system of claim 1, (Appendix 29) The reproducing portion comprises: During reconstruction, the order of reconstructions is adjusted based on data relevance 2. The system of claim 1, [Explanation of symbols]

[0181] 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 unit that collects data; An analysis unit that analyzes the data collected by the collection unit and reproduces the language, speech patterns, and facial expressions of the deceased parents or loved ones; A generation unit that creates a messenger app account based on the data analyzed by the analysis unit, allowing the user to enjoy conversations; A reproduction unit that reproduces an appearance using AR technology based on the data generated by the generation unit. A system characterized by:

2. The collecting unit includes: With the consent of the family, data such as emails, photos, videos, voicemails, conversation logs from messenger apps, and letters stored on smartphones and computers at the family home will be collected.

2. The system of claim 1 .

3. The analysis unit is The collected data is analyzed using generative AI to recreate the words, speech patterns, and facial expressions of deceased parents and loved ones.

2. The system of claim 1 .

4. The generation unit is Create a messenger app account based on the analyzed data, allowing users to enjoy conversations at any time 2. The system of claim 1 .

5. The reproducing portion comprises: Using AR technology, you can see your parents through the glass 2. The system of claim 1 .

6. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

7. The collecting unit includes: Optimize the type of data you collect based on users’ past behavior 2. The system of claim 1 .

8. The collecting unit includes: Filter data collection based on the user's current life situation and interests 2. The system of claim 1 .

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