system

Abstract

The system according to the embodiment aims to utilize the life log of the deceased to enable communication even after death. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a model creation unit, and a management unit. The collection unit collects life logs. The analysis unit analyzes the life logs collected by the collection unit. The model creation unit creates a model based on the data analyzed by the analysis unit. The management unit manages the model created by the model creation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not providing sufficient systems that effectively utilize the life logs of the deceased and allow communication to continue even after death.

[0005] The system according to the embodiment aims to utilize the life log of the deceased to enable communication even after death. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a model creation unit, and a management unit. The collection unit collects life logs. The analysis unit analyzes the life logs collected by the collection unit. The model creation unit creates a model based on the data analyzed by the analysis unit. The management unit manages the model created by the model creation unit. [Effects of the Invention]

[0007] The system according to the embodiment utilizes the life log of the deceased person, allowing communication even after death. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is "Memoria Talk," a service that enables reunions with the deceased. This Memoria Talk system collects the deceased's life log (e.g., photos, videos, and message history) and trains a generation AI to create a model that allows communication after death. For example, photos, videos, and messages sent and received by the deceased are collected and input into the generation AI. The generation AI analyzes the collected data to learn the deceased's characteristics and behavioral patterns. For example, it learns the deceased's language, facial expressions, and behavior. Based on the data learned by the generation AI, a model capable of communicating with the deceased is created. Using this model, users can relive real memories with the deceased. For example, the model can reproduce the conversations and facial expressions of the deceased during their lifetime and interact with the user. This service enables the centralized management of digital legacy. By collecting the deceased's life log and training the generation AI, digital legacy can be managed centrally. For example, the deceased's photos, videos, message history, and other information can be centrally stored and accessed as needed. This service can also be used for mental care. By reminiscing on real memories with the deceased, users can feel a reunion with the deceased and find emotional healing. For example, by recreating the conversations and expressions of the deceased while they were alive and interacting with them, users can receive emotional care. Furthermore, using the service while still alive can also facilitate digital end-of-life planning. By collecting their own lifelog and having the AI ​​learn from it, digital end-of-life planning can be carried out efficiently. For example, by collecting their own photos, videos, message history, etc. and having the AI ​​learn from them, it can be easier to organize digital belongings. This allows the Memoria Talk system to enable reunions with the deceased, centrally manage digital belongings, and provide mental care for users. Using the service while still alive can also facilitate digital end-of-life planning.

[0029] The Memoria Talk system according to the embodiment includes a collection unit, an analysis unit, a model creation unit, and a management unit. The collection unit collects a life log of the deceased. The life log includes, but is not limited to, photos, videos, and message history. For example, the collection unit collects photos and videos taken by the deceased and messages sent and received by the deceased. The collection unit can also collect the deceased's location information and social media activity history. For example, the collection unit collects location information of places visited by the deceased and saves it as a life log. The analysis unit analyzes the life log collected by the collection unit. The analysis unit analyzes the collected data using, for example, a generative AI to learn the characteristics and behavioral patterns of the deceased. For example, the analysis unit learns the deceased's language, facial expressions, and behavior. The analysis unit can also analyze the deceased's life log to identify the deceased's hobbies and interests. For example, the analysis unit can analyze the content of photos frequently taken by the deceased to identify the deceased's hobbies. The model creation unit creates a model capable of communicating with the deceased based on the data analyzed by the analysis unit. The model creation unit, for example, uses generative AI to create a model that reproduces the characteristics and behavioral patterns of the deceased. For example, the model creation unit creates a model that can converse with the user by recreating the content and facial expressions that the deceased spoke while alive. The model creation unit can also customize the model to make communication with the deceased more realistic based on the deceased's hobbies and interests. For example, the model creation unit creates a model that incorporates topics related to the deceased's favorite music and movies. The management unit centrally manages the models created by the model creation unit and makes them accessible as needed. The management unit efficiently manages the created models, for example, using generative AI. For example, the management unit stores the created models in a database so that users can access them as needed. The management unit can also periodically update the created models to reflect the latest information. For example, when new life logs of the deceased are collected, the management unit reflects the data in the model.As a result, the Memoria Talk system according to the embodiment can centrally collect, analyze, model, and manage the life logs of the deceased.

[0030] The collection unit can collect a life log including photos, videos, and message history of the deceased. For example, the collection unit collects photos taken by the deceased before they died. For example, the collection unit acquires photo data from the deceased's smartphone or camera and stores it as a life log. The collection unit can also collect videos taken by the deceased before they died. For example, the collection unit acquires video data from the deceased's video camera or smartphone and stores it as a life log. The collection unit can also collect a message history sent and received by the deceased before they died. For example, the collection unit acquires message data from the deceased's smartphone or computer and stores it as a life log. This allows the deceased's life log to be collected efficiently. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input photo data acquired from the deceased's smartphone into the generation AI and have the generation AI classify and organize the photo data.

[0031] The analysis unit can analyze the collected life log and learn the characteristics and behavioral patterns of the deceased. The analysis unit can, for example, analyze collected photo data and learn the characteristics of the deceased. For example, the analysis unit can identify the subjects and locations that the deceased frequently photographed and learn the deceased's hobbies and interests. The analysis unit can also analyze collected video data and learn the behavioral patterns of the deceased. For example, the analysis unit can analyze the behavioral patterns of the deceased in the situations in which the deceased was present. The analysis unit can also analyze the collected message history and learn the language and communication style of the deceased. For example, the analysis unit can analyze the language used by the deceased and the topics discussed in the conversation to learn the communication style of the deceased. This allows the characteristics and behavioral patterns of the deceased to be accurately learned. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input collected photo data into the generation AI and have the generation AI extract the characteristics of the deceased.

[0032] The model creation unit can create a model capable of communicating with the deceased based on the analyzed data. The model creation unit creates a model that reproduces the characteristics of the deceased based on, for example, analyzed photo data. For example, the model creation unit reflects the subjects and locations frequently photographed by the deceased in the model. The model creation unit can also create a model that reproduces the behavioral patterns of the deceased based on analyzed video data. For example, the model creation unit reflects the behavior of the deceased in the situations in which they were photographed in the model. The model creation unit can also create a model that reproduces the language and communication style of the deceased based on the analyzed message history. For example, the model creation unit reflects the language used by the deceased and the topics they discussed in the model. This enables realistic communication with the deceased. Some or all of the above-described processing in the model creation unit may be performed using, or without, a generation AI. For example, the model creation unit can input analyzed data into the generation AI and cause the generation AI to create a model capable of communicating with the deceased.

[0033] The management unit can centrally manage the created model and make it accessible as needed. For example, the management unit can store the created model in a database so that users can access it as needed. For example, the management unit can store the created model in cloud storage and make it accessible via the Internet. The management unit can also periodically update the created model to reflect the latest information. For example, when new life logs of a deceased person are collected, the management unit can update the model with that data. The management unit can also use the created model for mental care of users. For example, the management unit can provide users with a model that can communicate with the deceased person, allowing them to feel reunited with the deceased. This allows the created model to be efficiently managed and accessed as needed. Some or all of the above-mentioned processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the created model into a generation AI and have the generation AI manage and update the model.

[0034] The management unit can be utilized for mental care of the user. For example, the management unit provides the user with a model capable of communicating with the deceased, allowing the user to feel as if they are reunited with the deceased. For example, the management unit provides a model that can converse with the user by recreating the content and facial expressions of the deceased when they were alive. The management unit can also provide a function that allows the user to reminisce about memories with the deceased to provide mental care for the user. For example, the management unit can display photos and videos of the deceased, allowing the user to reminisce about memories with the deceased. This is useful for mental care of the user. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input a model capable of communicating with the deceased into the generation AI and cause the generation AI to execute a dialogue to provide mental care for the user.

[0035] The management unit can support digital end-of-life planning based on life logs collected during the deceased's lifetime. For example, the management unit organizes the life logs collected by the deceased during their lifetime and centrally manages them as digital legacies. For example, the management unit stores the deceased's photos, videos, message history, etc. as digital legacies and makes them accessible as needed. The management unit can also provide a function to support digital end-of-life planning based on the life logs collected by the deceased during their lifetime. For example, the management unit allows the deceased to collect their own life logs during their lifetime and have a generation AI learn from them, making it easier to organize their digital legacies. This allows for efficient digital end-of-life planning. Some or all of the above-described processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the deceased's life logs into the generation AI and have the generation AI organize the digital legacies.

[0036] The collection unit can analyze the deceased's past lifelog submission history and select the optimal collection method. For example, the collection unit prioritizes collecting lifelogs from devices frequently used by the deceased. For example, the collection unit acquires lifelog data from the deceased's smartphone or computer and selects the optimal collection method. Furthermore, if the deceased submitted many lifelogs during a specific time period, the collection unit can concentrate collection on that time period. For example, the collection unit analyzes the deceased's lifelog submission history and selects the optimal collection timing. Furthermore, if the deceased submitted many lifelogs at a specific event or location, the collection unit can prioritize collecting data related to that event or location. For example, the collection unit collects lifelogs from events the deceased participated in and places the deceased visited and selects the optimal collection method. This allows the optimal collection method to be selected. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the deceased's lifelog submission history into the generation AI and have the generation AI select the optimal collection method.

[0037] When collecting life logs, the collection unit can filter them based on the deceased's living situation and areas of interest. For example, the collection unit prioritizes collecting life logs related to the deceased's hobbies. For example, the collection unit collects and filters photos and videos related to the deceased's hobbies taken by the deceased. Furthermore, if the deceased spent a lot of time in a particular place, the collection unit can prioritize collecting life logs related to that place. For example, the collection unit collects and filters location information and photos of places frequently visited by the deceased. Furthermore, if the deceased frequently interacted with certain people, the collection unit can prioritize collecting life logs related to interactions with those people. For example, the collection unit collects and filters the message history and social media activity history of the deceased. This allows filtering based on the deceased's living situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the deceased's life logs into a generation AI and have the generation AI perform filtering.

[0038] When collecting life logs, the collection unit can prioritize collecting highly relevant logs by taking into account the geographical location information of the deceased. For example, the collection unit prioritizes collecting life logs related to places frequently visited by the deceased. For example, the collection unit acquires the location information of the deceased and collects photos and videos related to the frequently visited places. The collection unit can also prioritize collecting life logs related to areas where the deceased lived. For example, the collection unit collects location information and photos of areas where the deceased lived and prioritizes collecting highly relevant logs. The collection unit can also prioritize collecting life logs related to places where the deceased traveled. For example, the collection unit collects location information and photos of places where the deceased traveled and prioritizes collecting highly relevant logs. This makes it possible to prioritize collecting highly relevant logs by taking into account the geographical location information of the deceased. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the location information of the deceased to the generation AI and cause the generation AI to collect highly relevant logs.

[0039] The collection unit can analyze the deceased's social media activities and collect related logs when collecting life logs. For example, the collection unit prioritizes collection of social media data to which the deceased frequently posted. For example, the collection unit obtains posting data from the deceased's social media accounts and collects related logs. If the deceased used a specific hashtag, the collection unit can also collect data related to that hashtag. For example, the collection unit analyzes the hashtags used by the deceased and collects related posting data. If the deceased participated in a specific group or community, the collection unit can also collect data related to that activity. For example, the collection unit collects posting data from groups and communities in which the deceased participated and collects related logs. This makes it possible to analyze the deceased's social media activities and collect related logs. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the deceased's social media data into the generation AI and cause the generation AI to collect related logs.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the life log. For example, the analysis unit performs a detailed analysis on life logs with high importance. For example, the analysis unit may perform a detailed analysis of photos and videos frequently taken by the deceased to identify the deceased's hobbies and interests. The analysis unit can also perform a simplified analysis on life logs with low importance. For example, the analysis unit may perform a simplified analysis of photos and videos taken by the deceased only once. The analysis unit can also perform an analysis with a moderate level of detail on life logs with medium importance. For example, the analysis unit may analyze photos and videos taken by the deceased several times with a moderate level of detail. This allows the level of detail of the analysis to be adjusted based on the importance of the life log. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the importance of the life log to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the life log. For example, the analysis unit applies an image analysis algorithm to photo data. For example, the analysis unit may analyze photos taken by the deceased using an image analysis algorithm to identify the deceased's characteristics and hobbies. The analysis unit can also apply a video analysis algorithm to video data. For example, the analysis unit may analyze videos taken by the deceased using a video analysis algorithm to identify the deceased's behavioral patterns. The analysis unit can also apply a text analysis algorithm to message history. For example, the analysis unit may analyze messages sent and received by the deceased using a text analysis algorithm to identify the deceased's language and communication style. This allows an appropriate analysis algorithm to be applied depending on the category of the life log. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input the category of the life log into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the life log. For example, the analysis unit prioritizes analysis of recently submitted life logs. For example, the analysis unit prioritizes analysis of photos and videos recently taken by the deceased to reflect the most recent information. The analysis unit can also prioritize analysis of life logs related to a specific event. For example, the analysis unit prioritizes analysis of photos and videos of events attended by the deceased to identify details of the event. The analysis unit can also prioritize analysis of life logs within a period specified by the user. For example, the analysis unit prioritizes analysis of photos and videos taken within a period specified by the user to identify information from the specified period. This allows the analysis priority to be determined based on the time of submission of the life log. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the time of submission of the life log to the generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the life logs. For example, the analysis unit prioritizes analysis of highly relevant life logs. For example, the analysis unit prioritizes analysis of photos and videos frequently taken by the deceased to identify highly relevant information. The analysis unit can also postpone analysis of less relevant life logs. For example, the analysis unit may analyze photos and videos taken only once by the deceased later. The analysis unit can also analyze life logs with a moderate degree of relevance in an appropriate order. For example, the analysis unit may analyze photos and videos taken by the deceased several times in an appropriate order. This makes it possible to adjust the order of analysis based on the relevance of the life logs. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the life logs into the generation AI and have the generation AI adjust the order of analysis.

[0044] When creating a model, the model creation unit can analyze the deceased's past behavioral patterns and select the optimal model creation method. The model creation unit, for example, reflects the deceased's frequently used language in the model. For example, the model creation unit analyzes the deceased's message history and reflects the deceased's frequently used language in the model. The model creation unit can also reflect specific behavioral patterns, if the deceased had them, in the model. For example, the model creation unit analyzes video data of the deceased and reflects the deceased's behavioral patterns in specific situations in the model. The model creation unit can also reflect specific facial expressions or gestures, if the deceased frequently used them, in the model. For example, the model creation unit analyzes photos and videos of the deceased and reflects the deceased's frequently used facial expressions and gestures in the model. This allows the optimal model creation method to be selected based on the deceased's past behavioral patterns. Some or all of the above-described processing in the model creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the model creation unit can input the behavioral patterns of the deceased into the generation AI and have the generation AI select the optimal model creation method.

[0045] When creating a model, the model creation unit can customize the means for creating the model based on the living conditions of the deceased. For example, if the deceased had a specific occupation, the model creation unit reflects elements related to that occupation in the model. For example, the model creation unit analyzes photos and videos related to the deceased's occupation and reflects elements related to the occupation in the model. Furthermore, if the deceased had a specific hobby, the model creation unit can also reflect elements related to that hobby in the model. For example, the model creation unit analyzes photos and videos related to the deceased's hobby and reflects elements related to the hobby in the model. Furthermore, if the deceased lived in a specific area, the model creation unit can also reflect elements related to that area in the model. For example, the model creation unit analyzes photos and videos of the area where the deceased lived and reflects elements related to the area in the model. This makes it possible to customize the means for creating the model based on the living conditions of the deceased. Some or all of the above-mentioned processing in the model creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the model creation unit can input the living conditions of the deceased person into the generation AI and have the generation AI customize the means for creating the model.

[0046] When creating a model, the model creation unit can select an optimal model creation method by taking into account the geographical location information of the deceased. The model creation unit, for example, reflects elements related to places frequently visited by the deceased in the model. For example, the model creation unit acquires the location information of the deceased, analyzes photos and videos related to the frequently visited places, and reflects them in the model. The model creation unit can also reflect elements related to the area where the deceased lived in the model. For example, the model creation unit analyzes location information and photos of the area where the deceased lived and reflects them in the model. The model creation unit can also reflect elements related to places where the deceased traveled in the model. For example, the model creation unit analyzes location information and photos of places where the deceased traveled and reflects them in the model. This makes it possible to select an optimal model creation method by taking into account the geographical location information of the deceased. Some or all of the above-described processing in the model creation unit may be performed using, or without, a generation AI. For example, the model creation unit can input the geographical location information of the deceased into the generation AI and cause the generation AI to select an optimal model creation method.

[0047] When creating a model, the model creation unit can analyze the social media activity of the deceased and suggest methods for creating the model. The model creation unit, for example, incorporates social media data to which the deceased frequently posted into the model. For example, the model creation unit obtains posting data from the deceased's social media accounts and incorporates it into the model. Furthermore, if the deceased used a specific hashtag, the model creation unit can also incorporate elements related to that hashtag into the model. For example, the model creation unit analyzes the hashtags used by the deceased and incorporates them into the model. Furthermore, if the deceased participated in a specific group or community, the model creation unit can also incorporate elements related to that activity into the model. For example, the model creation unit analyzes posting data from the groups or communities the deceased participated in and incorporates it into the model. This makes it possible to analyze the deceased's social media activity and propose methods for creating the model. Some or all of the above-described processing in the model creation unit may be performed using, or without, a generation AI. For example, the model creation unit may input the deceased's social media data into the generation AI and have the generation AI execute a proposal for methods for creating the model.

[0048] During data management, the management unit can select the optimal management method by referring to the deceased's past data management history. The management unit selects the management method based on, for example, the devices and applications frequently used by the deceased. For example, the management unit analyzes the deceased's device usage history and selects the optimal management method. Furthermore, if the deceased managed data during a specific time period, the management unit can provide a management method tailored to that time period. For example, the management unit analyzes the deceased's data management history and selects the optimal management time period. Furthermore, if the deceased managed data at a specific event or location, the management unit can provide a management method related to that event or location. For example, the management unit analyzes the data management history of events the deceased attended and places visited and selects the optimal management method. This allows the optimal management method to be selected by referring to the deceased's past data management history. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the deceased's data management history into the generation AI and have the generation AI select the optimal management method.

[0049] During management, the management unit can customize the management means based on the living conditions of the deceased. For example, if the deceased had a particular occupation, the management unit provides data management means related to that occupation. For example, the management unit analyzes data related to the deceased's occupation and provides management means related to the occupation. Furthermore, if the deceased had a particular hobby, the management unit can also provide data management means related to the hobby. For example, the management unit analyzes data related to the deceased's hobby and provides management means related to the hobby. Furthermore, if the deceased lived in a particular area, the management unit can also provide data management means related to the area. For example, the management unit analyzes data on the area where the deceased lived and provides management means related to the area. This allows the management means to be customized based on the living conditions of the deceased. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the living conditions of the deceased into the generation AI and have the generation AI customize the management means.

[0050] During management, the management unit can select the optimal management method by taking into account the geographical location information of the deceased. For example, the management unit prioritizes management of data related to places frequently visited by the deceased. For example, the management unit acquires the location information of the deceased, analyzes photos and videos related to the frequently visited places, and selects a management method. The management unit can also prioritize management of data related to areas where the deceased lived. For example, the management unit analyzes location information and photos of areas where the deceased lived and selects a management method. The management unit can also prioritize management of data related to places where the deceased traveled. For example, the management unit analyzes location information and photos of places where the deceased traveled and selects a management method. This allows the optimal management method to be selected by taking into account the geographical location information of the deceased. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the geographical location information of the deceased into the generation AI and have the generation AI select the optimal management method.

[0051] During management, the management unit can analyze the deceased's social media activity and suggest management measures. For example, the management unit prioritizes management of social media data to which the deceased frequently posted. For example, the management unit obtains posting data from the deceased's social media accounts and suggests management measures. Furthermore, if the deceased used a specific hashtag, the management unit can prioritize management of data related to that hashtag. For example, the management unit can analyze the hashtags used by the deceased and suggest management measures. Furthermore, if the deceased participated in a specific group or community, the management unit can prioritize management of data related to that activity. For example, the management unit can analyze posting data from the groups or communities the deceased participated in and suggest management measures. In this way, the deceased's social media activity can be analyzed and management measures can be suggested. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the deceased's social media data into a generation AI and have the generation AI execute the suggested management measures.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When collecting the deceased's life log, the collection unit can also collect the deceased's health data. For example, the collection unit may acquire data such as heart rate, step count, and sleep patterns from a wearable device used by the deceased and store the data as a life log. The collection unit may also collect medical records and health checkup results from the deceased's life. For example, the collection unit may acquire data from the deceased's electronic medical records at the hospital and store the data as a life log. This allows for a detailed understanding of the deceased's health condition and lifestyle habits. Some or all of the above-described processing by the collection unit may be performed using or without the generation AI. For example, the collection unit may input the deceased's health data into the generation AI and have the generation AI classify and organize the data.

[0054] The model creation unit can create a model specialized in the deceased's hobbies and interests based on the deceased's life log. For example, if the deceased's hobby was music, a model including music-related topics and anecdotes can be created. If the deceased's hobby was traveling, a model based on photos and videos of the deceased's travel destinations can be created. Furthermore, if the deceased's hobby was cooking, a model including cooking recipes and cooking methods can be created. This enables realistic communication specialized in the deceased's hobbies and interests. Some or all of the above-mentioned processing in the model creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the model creation unit can input data related to the deceased's hobbies and interests into the generation AI and have the generation AI create a specialized model.

[0055] When centrally managing the created models, the management unit can determine management priorities based on the importance of the deceased's life log. For example, photos and videos frequently taken by the deceased can be managed with priority. Photos and videos taken by the deceased at important events can also be managed with priority. Furthermore, if the deceased frequently interacted with certain people, data related to interactions with those people can be managed with priority. This allows for efficient management of life logs with high importance. Some or all of the above-described processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the importance of the deceased's life log into the generation AI and have the generation AI determine the management priorities.

[0056] When collecting the deceased's life log, the collection unit can analyze the deceased's social media activities and prioritize the collection of related data. For example, it can collect data on social media posts that the deceased frequently posted on. If the deceased used a specific hashtag, it can also collect data related to that hashtag. Furthermore, if the deceased participated in a specific group or community, it can also collect data related to that activity. This allows for a detailed understanding of the deceased's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the deceased's social media data into the generation AI and have the generation AI collect related data.

[0057] When analyzing the collected life log, the analysis unit can analyze the deceased's health data and identify the deceased's health condition and lifestyle habits. For example, the analysis unit can analyze data such as the deceased's heart rate, number of steps, and sleep patterns to identify the deceased's health condition. The analysis unit can also analyze the deceased's medical records and health checkup results to identify the deceased's medical history and health risks. Furthermore, the analysis unit can analyze the deceased's diet and exercise records to identify the deceased's lifestyle habits. This allows for a detailed understanding of the deceased's health condition and lifestyle habits. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the deceased's health data into the generation AI and have the generation AI identify the deceased's health condition and lifestyle habits.

[0058] When centrally managing the created models, the management unit can determine management priorities based on the time of submission of the deceased person's lifelogs. For example, it can prioritize management of recently submitted lifelogs. It can also prioritize management of lifelogs related to a specific event. It can also prioritize management of lifelogs within a period specified by the user. This allows for efficient management based on the time of submission. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit can input the time of submission of the lifelogs into the generation AI and have the generation AI determine the management priorities.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects the deceased person's life log. The life log includes photos, videos, message history, location information, social media activity history, etc. For example, it collects photos and videos taken by the deceased person during their lifetime, messages sent and received, and location information of places visited. Step 2: The analysis unit analyzes the life logs collected by the collection unit. Using generative AI, the analysis unit learns the characteristics, behavioral patterns, hobbies, and interests of the deceased. For example, it analyzes the deceased's speech, facial expressions, behavior, and the contents of photos they frequently took. Step 3: The model creation unit creates a model capable of communicating with the deceased based on the data analyzed by the analysis unit. Using generative AI, it recreates the characteristics and behavioral patterns of the deceased and creates a model that can converse with the user. For example, it creates a model that incorporates topics related to the music and movies that the deceased liked. Step 4: The Management Department centrally manages the models created by the Model Creation Department and makes them accessible as needed. Using generative AI, the created models are stored in a database so that users can access them as needed. The created models are also regularly updated to reflect the latest information.

[0061] (Example 2) A system according to an embodiment of the present invention is "Memoria Talk," a service that enables reunions with the deceased. This Memoria Talk system collects the deceased's life log (e.g., photos, videos, and message history) and trains a generation AI to create a model that allows communication after death. For example, photos, videos, and messages sent and received by the deceased are collected and input into the generation AI. The generation AI analyzes the collected data to learn the deceased's characteristics and behavioral patterns. For example, it learns the deceased's language, facial expressions, and behavior. Based on the data learned by the generation AI, a model capable of communicating with the deceased is created. Using this model, users can relive real memories with the deceased. For example, the model can reproduce the conversations and facial expressions of the deceased during their lifetime and interact with the user. This service enables the centralized management of digital legacy. By collecting the deceased's life log and training the generation AI, digital legacy can be managed centrally. For example, the deceased's photos, videos, message history, and other information can be centrally stored and accessed as needed. This service can also be used for mental care. By reminiscing on real memories with the deceased, users can feel a reunion with the deceased and find emotional healing. For example, by recreating the conversations and expressions of the deceased while they were alive and interacting with them, users can receive emotional care. Furthermore, using the service while still alive can also facilitate digital end-of-life planning. By collecting their own lifelog and having the AI ​​learn from it, digital end-of-life planning can be carried out efficiently. For example, by collecting their own photos, videos, message history, etc. and having the AI ​​learn from them, it can be easier to organize digital belongings. This allows the Memoria Talk system to enable reunions with the deceased, centrally manage digital belongings, and provide mental care for users. Using the service while still alive can also facilitate digital end-of-life planning.

[0062] The Memoria Talk system according to the embodiment includes a collection unit, an analysis unit, a model creation unit, and a management unit. The collection unit collects a life log of the deceased. The life log includes, but is not limited to, photos, videos, and message history. For example, the collection unit collects photos and videos taken by the deceased and messages sent and received by the deceased. The collection unit can also collect the deceased's location information and social media activity history. For example, the collection unit collects location information of places visited by the deceased and saves it as a life log. The analysis unit analyzes the life log collected by the collection unit. The analysis unit analyzes the collected data using, for example, a generative AI to learn the characteristics and behavioral patterns of the deceased. For example, the analysis unit learns the deceased's language, facial expressions, and behavior. The analysis unit can also analyze the deceased's life log to identify the deceased's hobbies and interests. For example, the analysis unit can analyze the content of photos frequently taken by the deceased to identify the deceased's hobbies. The model creation unit creates a model capable of communicating with the deceased based on the data analyzed by the analysis unit. The model creation unit, for example, uses generative AI to create a model that reproduces the characteristics and behavioral patterns of the deceased. For example, the model creation unit creates a model that can converse with the user by recreating the content and facial expressions that the deceased spoke while alive. The model creation unit can also customize the model to make communication with the deceased more realistic based on the deceased's hobbies and interests. For example, the model creation unit creates a model that incorporates topics related to the deceased's favorite music and movies. The management unit centrally manages the models created by the model creation unit and makes them accessible as needed. The management unit efficiently manages the created models, for example, using generative AI. For example, the management unit stores the created models in a database so that users can access them as needed. The management unit can also periodically update the created models to reflect the latest information. For example, when new life logs of the deceased are collected, the management unit reflects the data in the model.As a result, the Memoria Talk system according to the embodiment can centrally collect, analyze, model, and manage the life logs of the deceased.

[0063] The collection unit can collect a life log including photos, videos, and message history of the deceased. For example, the collection unit collects photos taken by the deceased before they died. For example, the collection unit acquires photo data from the deceased's smartphone or camera and stores it as a life log. The collection unit can also collect videos taken by the deceased before they died. For example, the collection unit acquires video data from the deceased's video camera or smartphone and stores it as a life log. The collection unit can also collect a message history sent and received by the deceased before they died. For example, the collection unit acquires message data from the deceased's smartphone or computer and stores it as a life log. This allows the deceased's life log to be collected efficiently. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input photo data acquired from the deceased's smartphone into the generation AI and have the generation AI classify and organize the photo data.

[0064] The analysis unit can analyze the collected life log and learn the characteristics and behavioral patterns of the deceased. The analysis unit can, for example, analyze collected photo data and learn the characteristics of the deceased. For example, the analysis unit can identify the subjects and locations that the deceased frequently photographed and learn the deceased's hobbies and interests. The analysis unit can also analyze collected video data and learn the behavioral patterns of the deceased. For example, the analysis unit can analyze the behavioral patterns of the deceased in the situations in which the deceased was present. The analysis unit can also analyze the collected message history and learn the language and communication style of the deceased. For example, the analysis unit can analyze the language used by the deceased and the topics discussed in the conversation to learn the communication style of the deceased. This allows the characteristics and behavioral patterns of the deceased to be accurately learned. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input collected photo data into the generation AI and have the generation AI extract the characteristics of the deceased.

[0065] The model creation unit can create a model capable of communicating with the deceased based on the analyzed data. The model creation unit creates a model that reproduces the characteristics of the deceased based on, for example, analyzed photo data. For example, the model creation unit reflects the subjects and locations frequently photographed by the deceased in the model. The model creation unit can also create a model that reproduces the behavioral patterns of the deceased based on analyzed video data. For example, the model creation unit reflects the behavior of the deceased in the situations in which they were photographed in the model. The model creation unit can also create a model that reproduces the language and communication style of the deceased based on the analyzed message history. For example, the model creation unit reflects the language used by the deceased and the topics they discussed in the model. This enables realistic communication with the deceased. Some or all of the above-described processing in the model creation unit may be performed using, or without, a generation AI. For example, the model creation unit can input analyzed data into the generation AI and cause the generation AI to create a model capable of communicating with the deceased.

[0066] The management unit can centrally manage the created model and make it accessible as needed. For example, the management unit can store the created model in a database so that users can access it as needed. For example, the management unit can store the created model in cloud storage and make it accessible via the Internet. The management unit can also periodically update the created model to reflect the latest information. For example, when new life logs of a deceased person are collected, the management unit can update the model with that data. The management unit can also use the created model for mental care of users. For example, the management unit can provide users with a model that can communicate with the deceased person, allowing them to feel reunited with the deceased. This allows the created model to be efficiently managed and accessed as needed. Some or all of the above-mentioned processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the created model into a generation AI and have the generation AI manage and update the model.

[0067] The management unit can be utilized for mental care of the user. For example, the management unit provides the user with a model capable of communicating with the deceased, allowing the user to feel as if they are reunited with the deceased. For example, the management unit provides a model that can converse with the user by recreating the content and facial expressions of the deceased when they were alive. The management unit can also provide a function that allows the user to reminisce about memories with the deceased to provide mental care for the user. For example, the management unit can display photos and videos of the deceased, allowing the user to reminisce about memories with the deceased. This is useful for mental care of the user. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input a model capable of communicating with the deceased into the generation AI and cause the generation AI to execute a dialogue to provide mental care for the user.

[0068] The management unit can support digital end-of-life planning based on life logs collected during the deceased's lifetime. For example, the management unit organizes the life logs collected by the deceased during their lifetime and centrally manages them as digital legacies. For example, the management unit stores the deceased's photos, videos, message history, etc. as digital legacies and makes them accessible as needed. The management unit can also provide a function to support digital end-of-life planning based on the life logs collected by the deceased during their lifetime. For example, the management unit allows the deceased to collect their own life logs during their lifetime and have a generation AI learn from them, making it easier to organize their digital legacies. This allows for efficient digital end-of-life planning. Some or all of the above-described processing in the management unit may be performed using, or without, the generation AI. For example, the management unit can input the deceased's life logs into the generation AI and have the generation AI organize the digital legacies.

[0069] The collection unit can estimate the user's emotions and adjust the timing of collecting life logs based on the estimated user emotions. For example, if the user is sad, the collection unit delays the collection timing and starts collection when the user calms down. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can advance the collection timing to smoothly collect life logs. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is busy, the collection unit can adjust the collection timing to match the user's schedule. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the collection timing to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0070] The collection unit can analyze the deceased's past lifelog submission history and select the optimal collection method. For example, the collection unit prioritizes collecting lifelogs from devices frequently used by the deceased. For example, the collection unit acquires lifelog data from the deceased's smartphone or computer and selects the optimal collection method. Furthermore, if the deceased submitted many lifelogs during a specific time period, the collection unit can concentrate collection on that time period. For example, the collection unit analyzes the deceased's lifelog submission history and selects the optimal collection timing. Furthermore, if the deceased submitted many lifelogs at a specific event or location, the collection unit can prioritize collecting data related to that event or location. For example, the collection unit collects lifelogs from events the deceased participated in and places the deceased visited and selects the optimal collection method. This allows the optimal collection method to be selected. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the deceased's lifelog submission history into the generation AI and have the generation AI select the optimal collection method.

[0071] When collecting life logs, the collection unit can filter them based on the deceased's living situation and areas of interest. For example, the collection unit prioritizes collecting life logs related to the deceased's hobbies. For example, the collection unit collects and filters photos and videos related to the deceased's hobbies taken by the deceased. Furthermore, if the deceased spent a lot of time in a particular place, the collection unit can prioritize collecting life logs related to that place. For example, the collection unit collects and filters location information and photos of places frequently visited by the deceased. Furthermore, if the deceased frequently interacted with certain people, the collection unit can prioritize collecting life logs related to interactions with those people. For example, the collection unit collects and filters the message history and social media activity history of the deceased. This allows filtering based on the deceased's living situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the deceased's life logs into a generation AI and have the generation AI perform filtering.

[0072] The collection unit can estimate the user's emotions and determine the priority of life logs to be collected based on the estimated user emotions. For example, if the user is sad, the collection unit prioritizes collecting life logs with high emotional value. For example, the collection unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and determines the priority of life logs to be collected. Furthermore, if the user is relaxed, the collection unit can prioritize collecting everyday life logs. For example, the collection unit can record the user's voice, estimate the user's emotions using voice analysis technology, and determine the priority of life logs to be collected. Furthermore, if the user is excited, the collection unit can prioritize collecting life logs related to special events. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and determine the priority of life logs to be collected. This allows the priority of life logs to be collected to be determined based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of the life logs to be collected.

[0073] When collecting life logs, the collection unit can prioritize collecting highly relevant logs by taking into account the geographical location information of the deceased. For example, the collection unit prioritizes collecting life logs related to places frequently visited by the deceased. For example, the collection unit acquires the location information of the deceased and collects photos and videos related to the frequently visited places. The collection unit can also prioritize collecting life logs related to areas where the deceased lived. For example, the collection unit collects location information and photos of areas where the deceased lived and prioritizes collecting highly relevant logs. The collection unit can also prioritize collecting life logs related to places where the deceased traveled. For example, the collection unit collects location information and photos of places where the deceased traveled and prioritizes collecting highly relevant logs. This makes it possible to prioritize collecting highly relevant logs by taking into account the geographical location information of the deceased. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the location information of the deceased to the generation AI and cause the generation AI to collect highly relevant logs.

[0074] The collection unit can analyze the deceased's social media activities and collect related logs when collecting life logs. For example, the collection unit prioritizes collection of social media data to which the deceased frequently posted. For example, the collection unit obtains posting data from the deceased's social media accounts and collects related logs. If the deceased used a specific hashtag, the collection unit can also collect data related to that hashtag. For example, the collection unit analyzes the hashtags used by the deceased and collects related posting data. If the deceased participated in a specific group or community, the collection unit can also collect data related to that activity. For example, the collection unit collects posting data from groups and communities in which the deceased participated and collects related logs. This makes it possible to analyze the deceased's social media activities and collect related logs. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the deceased's social media data into the generation AI and cause the generation AI to collect related logs.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. For example, if the user is sad, the analysis unit reduces emotional expression and uses a calmer expression. For example, the analysis unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and adjusts the analysis presentation method. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and adjust the analysis presentation method. The analysis unit can also provide visually stimulating analysis results if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and adjust the analysis presentation method. This allows the analysis presentation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expression of the analysis.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the life log. For example, the analysis unit performs a detailed analysis on life logs with high importance. For example, the analysis unit may perform a detailed analysis of photos and videos frequently taken by the deceased to identify the deceased's hobbies and interests. The analysis unit can also perform a simplified analysis on life logs with low importance. For example, the analysis unit may perform a simplified analysis of photos and videos taken by the deceased only once. The analysis unit can also perform an analysis with a moderate level of detail on life logs with medium importance. For example, the analysis unit may analyze photos and videos taken by the deceased several times with a moderate level of detail. This allows the level of detail of the analysis to be adjusted based on the importance of the life log. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the importance of the life log to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the life log. For example, the analysis unit applies an image analysis algorithm to photo data. For example, the analysis unit may analyze photos taken by the deceased using an image analysis algorithm to identify the deceased's characteristics and hobbies. The analysis unit can also apply a video analysis algorithm to video data. For example, the analysis unit may analyze videos taken by the deceased using a video analysis algorithm to identify the deceased's behavioral patterns. The analysis unit can also apply a text analysis algorithm to message history. For example, the analysis unit may analyze messages sent and received by the deceased using a text analysis algorithm to identify the deceased's language and communication style. This allows an appropriate analysis algorithm to be applied depending on the category of the life log. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input the category of the life log into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and adjusts the length of the analysis. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and adjust the length of the analysis. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and adjust the length of the analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0079] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the life log. For example, the analysis unit prioritizes analysis of recently submitted life logs. For example, the analysis unit prioritizes analysis of photos and videos recently taken by the deceased to reflect the most recent information. The analysis unit can also prioritize analysis of life logs related to a specific event. For example, the analysis unit prioritizes analysis of photos and videos of events attended by the deceased to identify details of the event. The analysis unit can also prioritize analysis of life logs within a period specified by the user. For example, the analysis unit prioritizes analysis of photos and videos taken within a period specified by the user to identify information from the specified period. This allows the analysis priority to be determined based on the time of submission of the life log. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the time of submission of the life log to the generation AI and have the generation AI determine the analysis priority.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the life logs. For example, the analysis unit prioritizes analysis of highly relevant life logs. For example, the analysis unit prioritizes analysis of photos and videos frequently taken by the deceased to identify highly relevant information. The analysis unit can also postpone analysis of less relevant life logs. For example, the analysis unit may analyze photos and videos taken only once by the deceased later. The analysis unit can also analyze life logs with a moderate degree of relevance in an appropriate order. For example, the analysis unit may analyze photos and videos taken by the deceased several times in an appropriate order. This makes it possible to adjust the order of analysis based on the relevance of the life logs. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the life logs into the generation AI and have the generation AI adjust the order of analysis.

[0081] The model creation unit can estimate the user's emotions and adjust the model creation method based on the estimated user emotions. For example, if the user is sad, the model creation unit creates a model with less emotional expression. For example, the model creation unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and adjusts the model creation method. The model creation unit can also create a detailed model if the user is relaxed. For example, the model creation unit records the user's voice, estimates the user's emotions using voice analysis technology, and adjusts the model creation method. The model creation unit can also create a visually stimulating model if the user is excited. For example, the model creation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the user's emotions using an emotion estimation algorithm, and adjusts the model creation method. This allows the model creation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the model creation unit may be performed using, or without, the generation AI. For example, the model creation unit may input user emotion data into the generation AI and cause the generation AI to adjust the model creation method.

[0082] When creating a model, the model creation unit can analyze the deceased's past behavioral patterns and select the optimal model creation method. The model creation unit, for example, reflects the deceased's frequently used language in the model. For example, the model creation unit analyzes the deceased's message history and reflects the deceased's frequently used language in the model. The model creation unit can also reflect specific behavioral patterns, if the deceased had them, in the model. For example, the model creation unit analyzes video data of the deceased and reflects the deceased's behavioral patterns in specific situations in the model. The model creation unit can also reflect specific facial expressions or gestures, if the deceased frequently used them, in the model. For example, the model creation unit analyzes photos and videos of the deceased and reflects the deceased's frequently used facial expressions and gestures in the model. This allows the optimal model creation method to be selected based on the deceased's past behavioral patterns. Some or all of the above-described processing in the model creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the model creation unit can input the behavioral patterns of the deceased into the generation AI and have the generation AI select the optimal model creation method.

[0083] When creating a model, the model creation unit can customize the means for creating the model based on the living conditions of the deceased. For example, if the deceased had a specific occupation, the model creation unit reflects elements related to that occupation in the model. For example, the model creation unit analyzes photos and videos related to the deceased's occupation and reflects elements related to the occupation in the model. Furthermore, if the deceased had a specific hobby, the model creation unit can also reflect elements related to that hobby in the model. For example, the model creation unit analyzes photos and videos related to the deceased's hobby and reflects elements related to the hobby in the model. Furthermore, if the deceased lived in a specific area, the model creation unit can also reflect elements related to that area in the model. For example, the model creation unit analyzes photos and videos of the area where the deceased lived and reflects elements related to the area in the model. This makes it possible to customize the means for creating the model based on the living conditions of the deceased. Some or all of the above-mentioned processing in the model creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the model creation unit can input the living conditions of the deceased person into the generation AI and have the generation AI customize the means for creating the model.

[0084] The model creation unit can estimate the user's emotions and determine the priority of model creation based on the estimated user emotions. For example, if the user is sad, the model creation unit prioritizes creating models with high emotional value. For example, the model creation unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and determines the priority of model creation. Furthermore, if the user is relaxed, the model creation unit can prioritize creating models related to everyday activities. For example, the model creation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and determine the priority of model creation. Furthermore, if the user is excited, the model creation unit can prioritize creating models related to special events. For example, the model creation unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and determine the priority of model creation. This allows the priority of model creation to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the model creation unit may be performed using, or without, the generation AI. For example, the model creation unit may input user emotion data into the generation AI and have the generation AI determine the priority of model creation.

[0085] When creating a model, the model creation unit can select an optimal model creation method by taking into account the geographical location information of the deceased. The model creation unit, for example, reflects elements related to places frequently visited by the deceased in the model. For example, the model creation unit acquires the location information of the deceased, analyzes photos and videos related to the frequently visited places, and reflects them in the model. The model creation unit can also reflect elements related to the area where the deceased lived in the model. For example, the model creation unit analyzes location information and photos of the area where the deceased lived and reflects them in the model. The model creation unit can also reflect elements related to places where the deceased traveled in the model. For example, the model creation unit analyzes location information and photos of places where the deceased traveled and reflects them in the model. This makes it possible to select an optimal model creation method by taking into account the geographical location information of the deceased. Some or all of the above-described processing in the model creation unit may be performed using, or without, a generation AI. For example, the model creation unit can input the geographical location information of the deceased into the generation AI and cause the generation AI to select an optimal model creation method.

[0086] When creating a model, the model creation unit can analyze the social media activity of the deceased and suggest methods for creating the model. The model creation unit, for example, incorporates social media data to which the deceased frequently posted into the model. For example, the model creation unit obtains posting data from the deceased's social media accounts and incorporates it into the model. Furthermore, if the deceased used a specific hashtag, the model creation unit can also incorporate elements related to that hashtag into the model. For example, the model creation unit analyzes the hashtags used by the deceased and incorporates them into the model. Furthermore, if the deceased participated in a specific group or community, the model creation unit can also incorporate elements related to that activity into the model. For example, the model creation unit analyzes posting data from the groups or communities the deceased participated in and incorporates it into the model. This makes it possible to analyze the deceased's social media activity and propose methods for creating the model. Some or all of the above-described processing in the model creation unit may be performed using, or without, a generation AI. For example, the model creation unit may input the deceased's social media data into the generation AI and have the generation AI execute a proposal for methods for creating the model.

[0087] The management unit can estimate the user's emotions and adjust the management method based on the estimated user emotions. For example, if the user is sad, the management unit provides a management method that tone down emotional expressions. For example, the management unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and adjusts the management method accordingly. The management unit can also provide a more detailed management method if the user is relaxed. For example, the management unit can record the user's voice, estimate the user's emotions using voice analysis technology, and adjust the management method accordingly. The management unit can also provide a more visually stimulating management method if the user is excited. For example, the management unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and adjust the management method accordingly. This allows the management method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the management unit may input user emotion data into the generation AI and have the generation AI adjust the management method.

[0088] During data management, the management unit can select the optimal management method by referring to the deceased's past data management history. The management unit selects the management method based on, for example, the devices and applications frequently used by the deceased. For example, the management unit analyzes the deceased's device usage history and selects the optimal management method. Furthermore, if the deceased managed data during a specific time period, the management unit can provide a management method tailored to that time period. For example, the management unit analyzes the deceased's data management history and selects the optimal management time period. Furthermore, if the deceased managed data at a specific event or location, the management unit can provide a management method related to that event or location. For example, the management unit analyzes the data management history of events the deceased attended and places visited and selects the optimal management method. This allows the optimal management method to be selected by referring to the deceased's past data management history. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the deceased's data management history into the generation AI and have the generation AI select the optimal management method.

[0089] During management, the management unit can customize the management means based on the living conditions of the deceased. For example, if the deceased had a particular occupation, the management unit provides data management means related to that occupation. For example, the management unit analyzes data related to the deceased's occupation and provides management means related to the occupation. Furthermore, if the deceased had a particular hobby, the management unit can also provide data management means related to the hobby. For example, the management unit analyzes data related to the deceased's hobby and provides management means related to the hobby. Furthermore, if the deceased lived in a particular area, the management unit can also provide data management means related to the area. For example, the management unit analyzes data on the area where the deceased lived and provides management means related to the area. This allows the management means to be customized based on the living conditions of the deceased. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the living conditions of the deceased into the generation AI and have the generation AI customize the management means.

[0090] The management unit can estimate the user's emotions and determine management priorities based on the estimated user emotions. For example, if the user is sad, the management unit prioritizes managing data with high emotional value. For example, the management unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and determines management priorities. Furthermore, if the user is relaxed, the management unit can prioritize managing everyday data. For example, the management unit can record the user's voice, estimate the user's emotions using voice analysis technology, and determine management priorities. Furthermore, if the user is excited, the management unit can prioritize managing data related to special events. For example, the management unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and determine management priorities. This allows management priorities to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the management unit may input user emotion data into the generation AI and have the generation AI determine management priorities.

[0091] During management, the management unit can select the optimal management method by taking into account the geographical location information of the deceased. For example, the management unit prioritizes management of data related to places frequently visited by the deceased. For example, the management unit acquires the location information of the deceased, analyzes photos and videos related to the frequently visited places, and selects a management method. The management unit can also prioritize management of data related to areas where the deceased lived. For example, the management unit analyzes location information and photos of areas where the deceased lived and selects a management method. The management unit can also prioritize management of data related to places where the deceased traveled. For example, the management unit analyzes location information and photos of places where the deceased traveled and selects a management method. This allows the optimal management method to be selected by taking into account the geographical location information of the deceased. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the geographical location information of the deceased into the generation AI and have the generation AI select the optimal management method.

[0092] During management, the management unit can analyze the deceased's social media activity and suggest management measures. For example, the management unit prioritizes management of social media data to which the deceased frequently posted. For example, the management unit obtains posting data from the deceased's social media accounts and suggests management measures. Furthermore, if the deceased used a specific hashtag, the management unit can prioritize management of data related to that hashtag. For example, the management unit can analyze the hashtags used by the deceased and suggest management measures. Furthermore, if the deceased participated in a specific group or community, the management unit can prioritize management of data related to that activity. For example, the management unit can analyze posting data from the groups or communities the deceased participated in and suggest management measures. In this way, the deceased's social media activity can be analyzed and management measures can be suggested. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the deceased's social media data into a generation AI and have the generation AI execute the suggested management measures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, model creation unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the deceased's life log using the camera 42 and microphone 38B of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the characteristics and behavioral patterns of the deceased. The model creation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, creates a model capable of communicating with the deceased based on the analyzed data. The management unit, implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, centrally manages the created models and makes them accessible as needed. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, model creation unit, and management unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the deceased's life log using the camera 42 and microphone 238 of the smart glasses 214, which is then analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the characteristics and behavioral patterns of the deceased. The model creation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, creates a model capable of communicating with the deceased based on the analyzed data. The management unit, implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, centrally manages the created models and makes them accessible as needed. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, model creation unit, and management unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the deceased's life log using the camera 42 and microphone 238 of the headset-type terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the characteristics and behavioral patterns of the deceased. The model creation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, creates a model capable of communicating with the deceased based on the analyzed data. The management unit, implemented, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, centrally manages the created models and makes them accessible as needed. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, model creation unit, and management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the deceased's life log using the camera 42 and microphone 238 of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the characteristics and behavioral patterns of the deceased. The model creation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a model capable of communicating with the deceased based on the analyzed data. The management unit, realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, centrally manages the created models and makes them accessible as needed.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When collecting the deceased's life log, the collection unit can also collect the deceased's health data. For example, the collection unit may acquire data such as heart rate, step count, and sleep patterns from a wearable device used by the deceased and store the data as a life log. The collection unit may also collect medical records and health checkup results from the deceased's life. For example, the collection unit may acquire data from the deceased's electronic medical records at the hospital and store the data as a life log. This allows for a detailed understanding of the deceased's health condition and lifestyle habits. Some or all of the above-described processing by the collection unit may be performed using or without the generation AI. For example, the collection unit may input the deceased's health data into the generation AI and have the generation AI classify and organize the data.

[0095] The analysis unit can estimate the emotions of the deceased when analyzing the collected life log. For example, the analysis unit can analyze facial expressions from photos and videos of the deceased to estimate the emotions the deceased was feeling at the time. The analysis unit can also perform text analysis on the deceased's message history to estimate the emotions of the deceased. For example, the analysis unit can analyze the content and context of messages sent by the deceased to estimate the emotions of the deceased. Furthermore, the analysis unit can analyze the deceased's voice data and estimate emotions from the tone and pitch of the voice. This allows for detailed analysis of the life log, including the emotions of the deceased. Emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit can input the deceased's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0096] The model creation unit can create a model specialized in the deceased's hobbies and interests based on the deceased's life log. For example, if the deceased's hobby was music, a model including music-related topics and anecdotes can be created. If the deceased's hobby was traveling, a model based on photos and videos of the deceased's travel destinations can be created. Furthermore, if the deceased's hobby was cooking, a model including cooking recipes and cooking methods can be created. This enables realistic communication specialized in the deceased's hobbies and interests. Some or all of the above-mentioned processing in the model creation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the model creation unit can input data related to the deceased's hobbies and interests into the generation AI and have the generation AI create a specialized model.

[0097] When using the created model for mental care of a user, the management unit can estimate the user's emotions and adjust the model's presentation method based on the estimated emotions. For example, if the user is sad, a gentle dialogue can be provided to reduce the emotional burden. If the user is relaxed, a detailed reminiscence can be provided. Furthermore, if the user is excited, visually stimulating content can be provided. This enables mental care tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without the generation AI. For example, the management unit can input the user's emotional data into the generation AI and have the generation AI adjust the presentation method.

[0098] When centrally managing the created models, the management unit can determine management priorities based on the importance of the deceased's life log. For example, photos and videos frequently taken by the deceased can be managed with priority. Photos and videos taken by the deceased at important events can also be managed with priority. Furthermore, if the deceased frequently interacted with certain people, data related to interactions with those people can be managed with priority. This allows for efficient management of life logs with high importance. Some or all of the above-described processing in the management unit may be performed using or without the generation AI. For example, the management unit can input the importance of the deceased's life log into the generation AI and have the generation AI determine the management priorities.

[0099] When collecting the deceased's life log, the collection unit can analyze the deceased's social media activities and prioritize the collection of related data. For example, it can collect data on social media posts that the deceased frequently posted on. If the deceased used a specific hashtag, it can also collect data related to that hashtag. Furthermore, if the deceased participated in a specific group or community, it can also collect data related to that activity. This allows for a detailed understanding of the deceased's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the deceased's social media data into the generation AI and have the generation AI collect related data.

[0100] When analyzing the collected life log, the analysis unit can analyze the deceased's health data and identify the deceased's health condition and lifestyle habits. For example, the analysis unit can analyze data such as the deceased's heart rate, number of steps, and sleep patterns to identify the deceased's health condition. The analysis unit can also analyze the deceased's medical records and health checkup results to identify the deceased's medical history and health risks. Furthermore, the analysis unit can analyze the deceased's diet and exercise records to identify the deceased's lifestyle habits. This allows for a detailed understanding of the deceased's health condition and lifestyle habits. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the deceased's health data into the generation AI and have the generation AI identify the deceased's health condition and lifestyle habits.

[0101] The model creation unit can create a model that recreates the emotions of the deceased based on the deceased's life log. For example, it can analyze facial expressions from photos and videos taken by the deceased while they were alive and recreate the emotions of the deceased. It can also perform text analysis of the deceased's message history to recreate the emotions of the deceased. It can also analyze the deceased's voice data and recreate emotions from the tone and pitch of the voice. This enables realistic communication that includes the emotions of the deceased. The reproduction of emotions is achieved using an emotion engine or a generative AI. Some or all of the above-mentioned processing in the model creation unit may be performed using a generative AI, or may be performed without a generative AI. For example, the model creation unit can input the deceased's emotional data into a generative AI and have the generative AI recreate the emotions.

[0102] When centrally managing the created models, the management unit can determine management priorities based on the time of submission of the deceased person's lifelogs. For example, it can prioritize management of recently submitted lifelogs. It can also prioritize management of lifelogs related to a specific event. It can also prioritize management of lifelogs within a period specified by the user. This allows for efficient management based on the time of submission. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit can input the time of submission of the lifelogs into the generation AI and have the generation AI determine the management priorities.

[0103] When using the created model for mental care of a user, the management unit can estimate the user's emotions and adjust the update frequency of the model based on the estimated emotions. For example, if the user is sad, the model can be updated frequently to provide new memories. Alternatively, if the user is relaxed, the model can be updated at a moderate frequency. Furthermore, if the user is excited, updates related to special events can be performed. This enables mental care according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above-mentioned processing in the management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit can input the user's emotional data into the generation AI and have the generation AI adjust the update frequency.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects the deceased person's life log. The life log includes photos, videos, message history, location information, social media activity history, etc. For example, it collects photos and videos taken by the deceased person during their lifetime, messages sent and received, and location information of places visited. Step 2: The analysis unit analyzes the life logs collected by the collection unit. Using generative AI, the analysis unit learns the characteristics, behavioral patterns, hobbies, and interests of the deceased. For example, it analyzes the deceased's speech, facial expressions, behavior, and the contents of photos they frequently took. Step 3: The model creation unit creates a model capable of communicating with the deceased based on the data analyzed by the analysis unit. Using generative AI, it recreates the characteristics and behavioral patterns of the deceased and creates a model that can converse with the user. For example, it creates a model that incorporates topics related to the music and movies that the deceased liked. Step 4: The Management Department centrally manages the models created by the Model Creation Department and makes them accessible as needed. Using generative AI, the created models are stored in a database so that users can access them as needed. The created models are also regularly updated to reflect the latest information.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] 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.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] 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.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] 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.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 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 life logs; an analysis unit that analyzes the life log collected by the collection unit; a model creation unit that creates a model based on the data analyzed by the analysis unit; a management unit that manages the model created by the model creation unit; Equipped with A system characterized by:

2. The collecting unit Collecting the deceased person's life log, including photos, videos, and message history The system of claim 1 .

3. The analysis unit Analyzing collected life logs and learning the characteristics and behavioral patterns of the deceased The system of claim 1 .

4. The model creation unit Based on the analyzed data, we will create a model that can communicate with the deceased. The system of claim 1 .

5. The management unit Manage the created models in a central location and make them accessible when needed The system of claim 1 .

6. The management unit Used for mental care of users The system of claim 1 .

7. The management unit Supporting digital end-of-life planning based on life logs collected during one's lifetime The system of claim 1 .

8. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting life logs based on the estimated user emotions. The system of claim 1 .

Images