System

Abstract

To reproduce voice and words of the deceased, and provide emotional healing.SOLUTION: A system according to the embodiment comprises a reception unit, a reproduction unit, and a provision unit. The reception unit receives data for reproducing voice and words of the deceased from a user. The reproduction unit analyzes the data received by the reception unit, and reproduces the voice and words of the deceased. The provision unit provides the voice and words of the deceased reproduced by the reproduction unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to reproduce the voice and words of the deceased, limiting the means to provide emotional healing.

[0005] The system according to the embodiment aims to reproduce the voice and words of the deceased and provide emotional healing. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a reproduction unit, and a provision unit. The reception unit receives data for reproducing the voice and words of the deceased from a user. The reproduction unit analyzes the data received by the reception unit and reproduces the voice and words of the deceased. The provision unit provides the voice and words of the deceased reproduced by the reproduction unit. Effect of the Invention

[0007] The system according to the embodiment can reproduce the voice and words of the deceased, providing emotional comfort. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Memoria Talk system according to the embodiment of the present invention is a system that provides healing of the heart through sharing memories and emotions with the deceased. In this Memoria Talk system, the user inputs data for reproducing the voice and words of the deceased, and the generation AI analyzes the data to reproduce the voice and words of the deceased. Through the reproduced voice and words of the deceased, the user can enjoy conversations with the deceased and look back on memories together. In addition, a platform is provided that centrally manages memories such as photos, messages, and recorded voices, and allows easy access. For example, the user inputs audio recordings, messages, photos, etc. of the deceased, and the generation AI analyzes them to reproduce the voice and words of the deceased. Through the reproduced voice and words of the deceased, the user can enjoy conversations with the deceased. Furthermore, the Memoria Talk system provides a platform that centrally manages memories such as photos, messages, and recorded voices, and the user can easily access these memories. As a result, the Memoria Talk system allows the user to share memories and emotions with the deceased and obtain healing of the heart.

[0029] The memoria talk system according to the embodiment includes a reception unit, a reproduction unit, and a provision unit. The reception unit receives data for reproducing the voice and words of the deceased from the user. The data input by the user includes, for example, a voice recording of the deceased, a text message, a video message, and the like, but is not limited to such examples. The reception unit can receive, for example, voice recording data. The reception unit can also receive a text message. Furthermore, the reception unit can also receive a video message. The reproduction unit uses a generation AI to analyze the data received by the reception unit and reproduces the voice and words of the deceased. The reproduction unit reproduces the voice of the deceased using, for example, a voice synthesis technology. The reproduction unit can also reproduce the way the deceased speaks using a machine learning algorithm. Furthermore, the reproduction unit can also reproduce phrases and tones that the deceased often used. The provision unit provides the voice and words of the deceased reproduced by the reproduction unit to the user. The provision unit provides, for example, the voice of the deceased as a voice message. Furthermore, the provision unit can also provide the voice of the deceased as a video message. Furthermore, the provision unit can also provide the words of the deceased as a text message. As a result, the Memoria Talk system according to the embodiment allows the user to reproduce the voice and words of the deceased and reminisce about memories.

[0030] The reception unit receives data for reproducing the voice and words of the deceased from the user. Data input by the user includes, but is not limited to, for example, a voice recording of the deceased, a text message, a video message, and the like. The reception unit can receive, for example, voice recording data. The reception unit can also receive a text message. The reception unit can also receive a video message. Specifically, the reception unit provides an interface for the user to upload a file in which the voice of the deceased is recorded. This includes common audio file formats such as MP3 and WAV. For input of a text message, a text box is provided for the user to input memories of the deceased or a specific phrase. For a video message, the user can upload a video of the deceased speaking, which includes video file formats such as MP4 and AVI. The reception unit also has a security function for receiving and storing these data safely. For example, privacy is protected by encrypting data and authenticating the user. The reception unit also provides an intuitive user interface so that the user can easily upload data. This allows the user to provide data without stress, promoting the use of the system. Furthermore, the reception unit classifies the received data and prepares it so that the reproduction unit can analyze it efficiently. For example, voice data is converted into a format for voice analysis, and text data is reshaped into a format for natural language processing. This enables the reception unit to efficiently receive a variety of data from users and smoothly provide the data to the reproduction unit.

[0031] The reproduction unit uses the generation AI to analyze the data received by the reception unit and reproduce the voice and words of the deceased. The reproduction unit reproduces the voice of the deceased using, for example, voice synthesis technology. The reproduction unit can also reproduce the way the deceased spoke using a machine learning algorithm. Furthermore, the reproduction unit can also reproduce the phrases and tone of voice that the deceased often used. Specifically, the reproduction unit first analyzes the voice data and extracts the characteristics of the deceased's voice. This process includes elements such as the pitch, tempo, and intonation of the voice. Next, the reproduction unit uses a machine learning algorithm to learn the way the deceased spoke and the tone of voice. This involves identifying phrases and specific expressions that the deceased often used and building a model to reproduce them. The reproduction unit integrates these data and generates a voice synthesis model to reproduce the voice of the deceased with high accuracy. The generation AI also analyzes text messages and video messages provided by the user and reproduces the words and expressions of the deceased. For example, words and phrases that the deceased often used are extracted from the text messages and reflected in the voice synthesis. In the case of video messages, the facial expressions and gestures of the deceased are also analyzed to achieve a more realistic reproduction. Based on these analysis results, the reproduction unit can generate the voice and words of the deceased in real time. Furthermore, the reproduction unit performs a quality check on the generated voice and words before providing them to the user. This includes algorithms for evaluating the naturalness of the voice and the accuracy of the words. This allows the reproduction unit to provide the user with a high-quality voice and words of the deceased.

[0032] The providing unit provides the voice and words of the deceased reproduced by the reproducing unit to the user. The providing unit provides the voice of the deceased as, for example, a voice message. The providing unit can also provide the voice of the deceased as a video message. Furthermore, the providing unit can also provide the words of the deceased as a text message. Specifically, the providing unit provides an interface for providing the voice and words of the deceased in a format selected by the user. In the case of a voice message, the user can download the reproduced voice or play it in streaming. In the case of a video message, the voice of the deceased is provided in combination with a video provided by the user and a reproduced avatar. In the case of a text message, the words of the deceased are displayed in text format, and the user can save it or share it with others. The providing unit also has a security function for protecting the privacy of the user when providing these messages to the user. For example, the voice and words of the deceased are prevented from being illegally accessed by encrypting the message or restricting access. Furthermore, the providing unit provides an intuitive user interface so that the user can easily access the message. This allows the user to enjoy the voice and words of the deceased without stress. Furthermore, the providing unit can collect feedback from the user and continuously improve the quality and format of the message to be provided. For example, the user can evaluate the naturalness of the reproduced voice and the accuracy of the words, and the reproduction unit's algorithm can be adjusted based on the evaluation results. This allows the provision unit to provide the user with a high-quality voice and words of the deceased, providing a valuable experience for reminiscing on memories.

[0033] The Memoria Talk system includes a management unit that centrally manages memories, such as photos, messages, and recorded voices. The management unit enables the user to easily access the memories. The management unit centrally manages the photos, messages, and recorded voices, for example, by using a database. The management unit can also provide a user interface to enable the user to easily access the memories. Furthermore, the management unit can set access permissions to enable the user to access specific memories. This allows memories to be centrally managed and easily accessed.

[0034] The management unit includes an access unit that allows the user to access memories. The access unit allows the user to easily access memories. For example, the access unit provides a user interface to allow the user to access memories. The access unit may also provide an authentication method to allow the user to securely access memories. Furthermore, the access unit may provide a search function to allow the user to easily find a particular memory. This allows the user to easily access memories.

[0035] The reproducing unit can learn the characteristics of the deceased's voice or speaking style, and reproduce the deceased's voice or words based on that. The reproducing unit learns the characteristics of the deceased's voice and speaking style using a generative AI. For example, the reproducing unit analyzes the deceased's voice data and learns characteristics such as the pitch, rhythm, and intonation of the voice. The reproducing unit can also learn the speech patterns of the deceased and reproduce the deceased's voice and words based on that. Furthermore, the reproducing unit can learn the phrases and tones that the deceased often used, and reproduce the deceased's voice and words based on that. This allows the deceased's voice and words to be reproduced more accurately.

[0036] The providing unit can provide the reproduced voice or words of the deceased to the user. The providing unit provides the reproduced voice or words of the deceased by the reproducing unit to the user. The providing unit provides the voice of the deceased as, for example, a voice message. The providing unit can also provide the voice of the deceased as a video message. Furthermore, the providing unit can also provide the words of the deceased as a text message. This allows the user to experience the voice or words of the deceased.

[0037] The reception unit can analyze the user's past data input history and select an appropriate reception method. The reception unit analyzes the user's past data input history and selects the optimal reception method. For example, the reception unit preferentially receives data formats (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of data that the user has input in the past and suggest the optimal input method. Furthermore, the reception unit can select the optimal reception method for a specific time period from the user's past input history. This makes it possible to receive data in the optimal method based on the user's past history.

[0038] The reception unit can preferentially receive highly relevant data based on the geographical location information of the user when receiving data. The reception unit preferentially receives highly relevant data taking into account the geographical location information of the user when receiving data. For example, when the user is in a specific location, data of memories related to the location is preferentially received. Also, when the user is traveling, data related to the travel destination can be preferentially received. Furthermore, when the user is in the hometown of a deceased person, data related to the location can be preferentially received. In this way, highly relevant data can be received based on the location information of the user.

[0039] The reception unit can receive related data based on the user's social media activity when receiving the data. The reception unit analyzes the user's social media activity when receiving the data and receives the related data. For example, the reception unit preferentially receives data of memories shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive the related data. Furthermore, the reception unit can also preferentially receive data related to places and events tagged by the user on social media. This makes it possible to receive related data based on the user's social media activity.

[0040] The reproduction unit can adjust the level of detail of the reproduction based on the characteristics of the deceased's voice during reproduction. The reproduction unit uses the generation AI to adjust the level of detail of the reproduction based on the characteristics of the deceased's voice during reproduction. For example, if the deceased's voice is important, it can be reproduced with high accuracy based on detailed voice data. Also, if the deceased's voice is not very important, it can be reproduced based on simplified voice data. Furthermore, the level of detail of the reproduction can be dynamically adjusted depending on the importance of the deceased's voice. This allows the deceased's voice to be reproduced with a level of detail that corresponds to the importance of the deceased's voice.

[0041] The reenactment unit can apply a specific reenactment algorithm depending on the category of the deceased during reenactment. The reenactment unit uses the generation AI to apply different reenactment algorithms depending on the category of the deceased during reenactment. For example, if the deceased is a family member, an algorithm that reproduces the deceased in a friendly tone can be applied. Also, if the deceased is a friend, an algorithm that reproduces the deceased in a casual tone can be applied. Furthermore, if the deceased is a coworker, an algorithm that reproduces the deceased in a formal tone can be applied. This allows the deceased to be reproduced using an appropriate algorithm depending on the category of the deceased.

[0042] The reproduction unit can determine the priority of reproduction based on the time of submission of the deceased's data at the time of reproduction. The reproduction unit uses the generation AI to determine the priority of reproduction based on the time of submission of the deceased's data at the time of reproduction. For example, data submitted recently can be preferentially reproduced. Data related to a specific anniversary can also be preferentially reproduced. Furthermore, data submitted by a user at a specific time can also be preferentially reproduced. This allows preferential reproduction based on the time of submission.

[0043] The reproducing unit can adjust the order of reproduction based on the relationships of the deceased at the time of reproduction. The reproducing unit uses the generation AI to adjust the order of reproduction based on the relationships of the deceased at the time of reproduction. For example, if the deceased is particularly important to the user, that data is reproduced with priority. Also, if the deceased is a family member of the user, that data can be reproduced with priority. Furthermore, if the deceased is a friend of the user, that data can be reproduced next. This allows reproduction in an appropriate order based on the relationships of the deceased.

[0044] The provision unit can analyze the user's past usage history and select an appropriate provision method at the time of provision. The provision unit uses AI to analyze the user's past usage history and select the optimal provision method at the time of provision. For example, the provision unit preferentially provides the provision method (voice, text, etc.) that the user has used in the past as preferred. In addition, the optimal timing for provision can be selected from the user's past usage history. Furthermore, the user's past usage history can be analyzed to select the most effective provision method. This makes it possible to provide the optimal method based on the user's past history.

[0045] The providing unit can select an appropriate method of providing the data based on the user's geographical location information at the time of providing the data. The providing unit uses AI to select the optimal method of providing the data by taking into consideration the user's geographical location information at the time of providing the data. For example, if the user is in a specific location, data of memories related to that location can be provided preferentially. Also, if the user is traveling, data related to the travel destination can be provided preferentially. Furthermore, if the user is in the hometown of a deceased person, data related to that location can be provided preferentially. This allows the data to be provided in the optimal manner based on the user's location information.

[0046] The providing unit can suggest a means of provision based on the user's social media activity at the time of provision. The providing unit uses AI to analyze the user's social media activity at the time of provision and suggest a means of provision. For example, data of memories shared by the user on social media can be provided preferentially. In addition, the content of the user's social media posts can be analyzed and related data can be provided. Furthermore, data related to places and events tagged by the user on social media can be provided preferentially. This makes it possible to provide the optimal means based on the user's social media activity.

[0047] The management unit can appropriately adjust the management algorithm by referring to past management data during management. The management unit uses AI to optimize the management algorithm by referring to past management data during management. For example, the management unit analyzes past management data and proposes an optimal management method. In addition, it is also possible to extract specific patterns from past management data and optimize the management algorithm. Furthermore, it is also possible to apply an algorithm that improves management efficiency based on past management data. This makes it possible to apply the optimal management method based on past data.

[0048] The management unit can determine the priority of the managed data based on the time of data submission during management. The management unit uses AI to weight the managed data based on the time of data submission during management. For example, recently submitted data can be preferentially managed. Data related to a specific anniversary can also be preferentially managed. Furthermore, data submitted by a user at a specific time can also be preferentially managed. This allows data to be preferentially managed based on the time of submission.

[0049] The access unit can select an appropriate access method by referring to the user's past access history at the time of access. The access unit uses AI to select the optimal access method by referring to the user's past access history at the time of access. For example, the access unit preferentially provides the access method that the user has used in the past. The optimal access timing can also be selected from the user's past access history. Furthermore, the user's past access history can be analyzed to select the most effective access method. This allows access to be performed in the optimal way based on the user's past history.

[0050] The access unit can select an appropriate access method based on the user's device information at the time of access. The access unit uses AI to select the optimal access method by considering the user's device information at the time of access. For example, if the user is using a smartphone, an access method that matches the screen size can be provided. Also, if the user is using a tablet, an access method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible access method can be provided. This allows access to be made in the optimal way according to the user's device.

[0051] The reproduction unit can analyze the user's past data and select an appropriate reproduction method at the time of reproduction. The reproduction unit uses the generation AI to analyze the user's past data and select the optimal reproduction method at the time of reproduction. For example, the reproduction unit preferentially provides reproduction methods that the user has used in the past. The reproduction unit can also select the optimal reproduction timing from the user's past data. Furthermore, the reproduction unit can analyze the user's past data and select the most effective reproduction method. This allows reproduction to be performed in the optimal way based on the user's past data.

[0052] The reproduction unit can select an appropriate reproduction method based on the user's geographic location information at the time of reproduction. The reproduction unit uses the generation AI to select the optimal reproduction method taking into account the user's geographic location information at the time of reproduction. For example, if the user is in a specific location, data of memories related to that location is preferentially reproduced. Also, if the user is traveling, data related to the travel destination can be preferentially reproduced. Furthermore, if the user is in the hometown of a deceased person, data related to that location can be preferentially reproduced. This allows reproduction in the optimal way based on the user's location information.

[0053] The reproducing unit can suggest a means of reproduction based on the user's social media activity at the time of reproduction. The reproducing unit uses the generative AI to analyze the user's social media activity at the time of reproduction and suggest a means of reproduction. For example, the reproducing unit prioritizes reproducing data of memories shared by the user on social media. It can also analyze the content of the user's social media posts and reproduce related data. Furthermore, it can prioritize reproducing data related to places and events tagged by the user on social media. This allows reproduction to be performed using the optimal means based on the user's social media activity.

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

[0055] The Memoria Talk system may further include a notification unit. The notification unit may notify the user of memory data based on a specific date and time or event set by the user. For example, the notification unit may notify the user of related photos and messages on the birthday or anniversary of the deceased. If the user wishes to look back on memories during a specific time period, the notification unit may send a notification during that time period. Furthermore, the notification unit may suggest the optimal timing for notification based on the user's schedule. This allows the user to look back on important memories without forgetting them.

[0056] The Memoria Talk system may further include a sharing unit. The sharing unit provides a function for a user to share memories with other family members and friends. For example, if a user wants to share memories of a deceased person with family members, the user can easily generate and send a sharing link through the sharing unit. The sharing unit may also collect comments and reactions to the shared memories and provide feedback to the user. Furthermore, the sharing unit may provide privacy settings and limit the scope of sharing. This allows the user to share memories with peace of mind.

[0057] The Memoria Talk system may further include a customization section. The customization section allows the user to individually set the appearance and functions of the system. For example, the user can select a theme color or font that he or she likes. The customization section may also allow the user to enable or disable specific functions. Furthermore, the customization section may suggest optimal settings based on the user's usage history. This allows the user to build an easy-to-use system that suits him or her.

[0058] The Memoria Talk system may further include an archive unit. The archive unit stores past memory data for a long period of time and allows access as needed. For example, if a user wants to look back on memories from several years ago, the user can easily access the memories through the archive unit. The archive unit also provides a data backup function and can prevent data loss. The archive unit also provides a data organization function and allows the user to efficiently manage memories. This allows the user to store important memories for a long period of time and look back on them at any time.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The reception unit receives data for reproducing the voice and words of the deceased from the user. The data input by the user includes, for example, a voice recording of the deceased, a text message, a video message, etc. The reception unit can receive voice recording data, a text message, and a video message. Step 2: The Reproducer uses the Generator AI to analyze the data received by the Receiving Unit and reproduce the voice and words of the deceased. The Reproducer uses voice synthesis technology and machine learning algorithms to reproduce the voice, speech style, phrases, and tone of the deceased. Step 3: The providing unit provides the voice or words of the deceased reproduced by the reproducing unit to the user. The providing unit can provide the voice or words of the deceased as an audio message, a video message, or a text message.

[0061] (Example 2) The Memoria Talk system according to the embodiment of the present invention is a system that provides healing of the heart through sharing memories and emotions with the deceased. In this Memoria Talk system, the user inputs data for reproducing the voice and words of the deceased, and the generation AI analyzes the data to reproduce the voice and words of the deceased. Through the reproduced voice and words of the deceased, the user can enjoy conversations with the deceased and look back on memories together. In addition, a platform is provided that centrally manages memories such as photos, messages, and recorded voices, and allows easy access. For example, the user inputs audio recordings, messages, photos, etc. of the deceased, and the generation AI analyzes them to reproduce the voice and words of the deceased. Through the reproduced voice and words of the deceased, the user can enjoy conversations with the deceased. Furthermore, the Memoria Talk system provides a platform that centrally manages memories such as photos, messages, and recorded voices, and the user can easily access these memories. As a result, the Memoria Talk system allows the user to share memories and emotions with the deceased and obtain healing of the heart.

[0062] The memoria talk system according to the embodiment includes a reception unit, a reproduction unit, and a provision unit. The reception unit receives data for reproducing the voice and words of the deceased from the user. The data input by the user includes, for example, a voice recording of the deceased, a text message, a video message, and the like, but is not limited to such examples. The reception unit can receive, for example, voice recording data. The reception unit can also receive a text message. Furthermore, the reception unit can also receive a video message. The reproduction unit uses a generation AI to analyze the data received by the reception unit and reproduces the voice and words of the deceased. The reproduction unit reproduces the voice of the deceased using, for example, a voice synthesis technology. The reproduction unit can also reproduce the way the deceased speaks using a machine learning algorithm. Furthermore, the reproduction unit can also reproduce phrases and tones that the deceased often used. The provision unit provides the voice and words of the deceased reproduced by the reproduction unit to the user. The provision unit provides, for example, the voice of the deceased as a voice message. Furthermore, the provision unit can also provide the voice of the deceased as a video message. Furthermore, the provision unit can also provide the words of the deceased as a text message. As a result, the Memoria Talk system according to the embodiment allows the user to reproduce the voice and words of the deceased and reminisce about memories.

[0063] The reception unit receives data for reproducing the voice and words of the deceased from the user. Data input by the user includes, but is not limited to, for example, a voice recording of the deceased, a text message, a video message, and the like. The reception unit can receive, for example, voice recording data. The reception unit can also receive a text message. The reception unit can also receive a video message. Specifically, the reception unit provides an interface for the user to upload a file in which the voice of the deceased is recorded. This includes common audio file formats such as MP3 and WAV. For input of a text message, a text box is provided for the user to input memories of the deceased or a specific phrase. For a video message, the user can upload a video of the deceased speaking, which includes video file formats such as MP4 and AVI. The reception unit also has a security function for receiving and storing these data safely. For example, privacy is protected by encrypting data and authenticating the user. The reception unit also provides an intuitive user interface so that the user can easily upload data. This allows the user to provide data without stress, promoting the use of the system. Furthermore, the reception unit classifies the received data and prepares it so that the reproduction unit can analyze it efficiently. For example, voice data is converted into a format for voice analysis, and text data is reshaped into a format for natural language processing. This enables the reception unit to efficiently receive a variety of data from users and smoothly provide the data to the reproduction unit.

[0064] The reproduction unit uses the generation AI to analyze the data received by the reception unit and reproduce the voice and words of the deceased. The reproduction unit reproduces the voice of the deceased using, for example, voice synthesis technology. The reproduction unit can also reproduce the way the deceased spoke using a machine learning algorithm. Furthermore, the reproduction unit can also reproduce the phrases and tone of voice that the deceased often used. Specifically, the reproduction unit first analyzes the voice data and extracts the characteristics of the deceased's voice. This process includes elements such as the pitch, tempo, and intonation of the voice. Next, the reproduction unit uses a machine learning algorithm to learn the way the deceased spoke and the tone of voice. This involves identifying phrases and specific expressions that the deceased often used and building a model to reproduce them. The reproduction unit integrates these data and generates a voice synthesis model to reproduce the voice of the deceased with high accuracy. The generation AI also analyzes text messages and video messages provided by the user and reproduces the words and expressions of the deceased. For example, words and phrases that the deceased often used are extracted from the text messages and reflected in the voice synthesis. In the case of video messages, the facial expressions and gestures of the deceased are also analyzed to achieve a more realistic reproduction. Based on these analysis results, the reproduction unit can generate the voice and words of the deceased in real time. Furthermore, the reproduction unit performs a quality check on the generated voice and words before providing them to the user. This includes algorithms for evaluating the naturalness of the voice and the accuracy of the words. This allows the reproduction unit to provide the user with a high-quality voice and words of the deceased.

[0065] The providing unit provides the voice and words of the deceased reproduced by the reproducing unit to the user. The providing unit provides the voice of the deceased as, for example, a voice message. The providing unit can also provide the voice of the deceased as a video message. Furthermore, the providing unit can also provide the words of the deceased as a text message. Specifically, the providing unit provides an interface for providing the voice and words of the deceased in a format selected by the user. In the case of a voice message, the user can download the reproduced voice or play it in streaming. In the case of a video message, the voice of the deceased is provided in combination with a video provided by the user and a reproduced avatar. In the case of a text message, the words of the deceased are displayed in text format, and the user can save it or share it with others. The providing unit also has a security function for protecting the privacy of the user when providing these messages to the user. For example, the voice and words of the deceased are prevented from being illegally accessed by encrypting the message or restricting access. Furthermore, the providing unit provides an intuitive user interface so that the user can easily access the message. This allows the user to enjoy the voice and words of the deceased without stress. Furthermore, the providing unit can collect feedback from the user and continuously improve the quality and format of the message to be provided. For example, the user can evaluate the naturalness of the reproduced voice and the accuracy of the words, and the reproduction unit's algorithm can be adjusted based on the evaluation results. This allows the provision unit to provide the user with a high-quality voice and words of the deceased, providing a valuable experience for reminiscing on memories.

[0066] The Memoria Talk system includes a management unit that centrally manages memories, such as photos, messages, and recorded voices. The management unit enables the user to easily access the memories. The management unit centrally manages the photos, messages, and recorded voices, for example, by using a database. The management unit can also provide a user interface to enable the user to easily access the memories. Furthermore, the management unit can set access permissions to enable the user to access specific memories. This allows memories to be centrally managed and easily accessed.

[0067] The management unit includes an access unit that allows the user to access memories. The access unit allows the user to easily access memories. For example, the access unit provides a user interface to allow the user to access memories. The access unit may also provide an authentication method to allow the user to securely access memories. Furthermore, the access unit may provide a search function to allow the user to easily find a particular memory. This allows the user to easily access memories.

[0068] The reproducing unit can learn the characteristics of the deceased's voice or speaking style, and reproduce the deceased's voice or words based on that. The reproducing unit learns the characteristics of the deceased's voice and speaking style using a generative AI. For example, the reproducing unit analyzes the deceased's voice data and learns characteristics such as the pitch, rhythm, and intonation of the voice. The reproducing unit can also learn the speech patterns of the deceased and reproduce the deceased's voice and words based on that. Furthermore, the reproducing unit can learn the phrases and tones that the deceased often used, and reproduce the deceased's voice and words based on that. This allows the deceased's voice and words to be reproduced more accurately.

[0069] The providing unit can provide the reproduced voice or words of the deceased to the user. The providing unit provides the reproduced voice or words of the deceased by the reproducing unit to the user. The providing unit provides the voice of the deceased as, for example, a voice message. The providing unit can also provide the voice of the deceased as a video message. Furthermore, the providing unit can also provide the words of the deceased as a text message. This allows the user to experience the voice or words of the deceased.

[0070] The reception unit can estimate the user's emotion and determine the timing of data reception based on the estimated user's emotion. The reception unit estimates the user's emotion and adjusts the timing of data reception based on the estimated user's emotion. For example, if the user is sad, data reception can be performed slowly and the user can wait until the user calms down. Also, if the user is excited, data can be quickly received and the reproduction process can be started immediately. Furthermore, if the user is relaxed, detailed data input can be prompted to collect more information. This allows data to be received at an appropriate timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0071] The reception unit can analyze the user's past data input history and select an appropriate reception method. The reception unit analyzes the user's past data input history and selects the optimal reception method. For example, the reception unit preferentially receives data formats (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of data that the user has input in the past and suggest the optimal input method. Furthermore, the reception unit can select the optimal reception method for a specific time period from the user's past input history. This makes it possible to receive data in the optimal method based on the user's past history.

[0072] The reception unit can filter the data based on the user's current mood or field of interest when receiving the data. The reception unit filters the data based on the user's current mood or field of interest when receiving the data. For example, if the user is sad, data related to positive memories can be preferentially received. Also, if the user has a specific field of interest, data related to that field can be preferentially received. Furthermore, appropriate data can be filtered and received based on the user's current mood. This makes it possible to preferentially receive data according to the user's mood or interest. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0073] The reception unit can estimate the user's emotion and determine the priority of the data to be received based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of the data to be received based on the estimated user's emotion. For example, when the user is sad, data with high emotional value is preferentially received. In addition, when the user is relaxed, detailed data can be preferentially received. Furthermore, when the user is in a hurry, important data can be quickly received. This allows data to be received in a priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0074] The reception unit can preferentially receive highly relevant data based on the geographical location information of the user when receiving data. The reception unit preferentially receives highly relevant data taking into account the geographical location information of the user when receiving data. For example, when the user is in a specific location, data of memories related to the location is preferentially received. Also, when the user is traveling, data related to the travel destination can be preferentially received. Furthermore, when the user is in the hometown of a deceased person, data related to the location can be preferentially received. In this way, highly relevant data can be received based on the location information of the user.

[0075] The reception unit can receive related data based on the user's social media activity when receiving the data. The reception unit analyzes the user's social media activity when receiving the data and receives the related data. For example, the reception unit preferentially receives data of memories shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive the related data. Furthermore, the reception unit can also preferentially receive data related to places and events tagged by the user on social media. This makes it possible to receive related data based on the user's social media activity.

[0076] The reproduction unit can estimate the user's emotion and determine the expression method of reproduction based on the estimated user's emotion. The reproduction unit uses the generation AI to estimate the user's emotion and adjust the expression method of reproduction based on the estimated user's emotion. For example, if the user is sad, the voice of the deceased is reproduced in a gentle tone. Also, if the user is relaxed, the voice of the deceased can be reproduced in a natural tone. Furthermore, if the user is excited, the voice of the deceased can be reproduced in a lively tone. In this way, the voice of the deceased can be reproduced in an expression method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] The reproduction unit can adjust the level of detail of the reproduction based on the characteristics of the deceased's voice during reproduction. The reproduction unit uses the generation AI to adjust the level of detail of the reproduction based on the characteristics of the deceased's voice during reproduction. For example, if the deceased's voice is important, it can be reproduced with high accuracy based on detailed voice data. Also, if the deceased's voice is not very important, it can be reproduced based on simplified voice data. Furthermore, the level of detail of the reproduction can be dynamically adjusted depending on the importance of the deceased's voice. This allows the deceased's voice to be reproduced with a level of detail that corresponds to the importance of the deceased's voice.

[0078] The reenactment unit can apply a specific reenactment algorithm depending on the category of the deceased during reenactment. The reenactment unit uses the generation AI to apply different reenactment algorithms depending on the category of the deceased during reenactment. For example, if the deceased is a family member, an algorithm that reproduces the deceased in a friendly tone can be applied. Also, if the deceased is a friend, an algorithm that reproduces the deceased in a casual tone can be applied. Furthermore, if the deceased is a coworker, an algorithm that reproduces the deceased in a formal tone can be applied. This allows the deceased to be reproduced using an appropriate algorithm depending on the category of the deceased.

[0079] The reproducing unit can estimate the user's emotion and determine the length of the reproduction based on the estimated user's emotion. The reproducing unit uses the generating AI to estimate the user's emotion and adjust the length of the reproduction based on the estimated user's emotion. For example, if the user is sad, a short reproduction is provided to reduce the emotional burden. Also, if the user is relaxed, a longer reproduction can be provided to allow the user to look back on memories in depth. Furthermore, if the user is in a hurry, a short reproduction that covers the main points can be provided. This allows reproduction at an appropriate length according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generating AI. The generating AI can be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples.

[0080] The reproduction unit can determine the priority of reproduction based on the time of submission of the deceased's data at the time of reproduction. The reproduction unit uses the generation AI to determine the priority of reproduction based on the time of submission of the deceased's data at the time of reproduction. For example, data submitted recently can be preferentially reproduced. Data related to a specific anniversary can also be preferentially reproduced. Furthermore, data submitted by a user at a specific time can also be preferentially reproduced. This allows preferential reproduction based on the time of submission.

[0081] The reproducing unit can adjust the order of reproduction based on the relationships of the deceased at the time of reproduction. The reproducing unit uses the generation AI to adjust the order of reproduction based on the relationships of the deceased at the time of reproduction. For example, if the deceased is particularly important to the user, that data is reproduced with priority. Also, if the deceased is a family member of the user, that data can be reproduced with priority. Furthermore, if the deceased is a friend of the user, that data can be reproduced next. This allows reproduction in an appropriate order based on the relationships of the deceased.

[0082] The providing unit can estimate the user's emotion and determine a method of providing based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and adjusts the method of providing based on the estimated user's emotion. For example, when the user is sad, the voice of the deceased is provided in a gentle tone. When the user is relaxed, the voice of the deceased can also be provided in a natural tone. Furthermore, when the user is excited, the voice of the deceased can also be provided in a lively tone. This makes it possible to provide the voice of the deceased in a manner according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The provision unit can analyze the user's past usage history and select an appropriate provision method at the time of provision. The provision unit uses AI to analyze the user's past usage history and select the optimal provision method at the time of provision. For example, the provision unit preferentially provides the provision method (voice, text, etc.) that the user has used in the past as preferred. In addition, the optimal timing for provision can be selected from the user's past usage history. Furthermore, the user's past usage history can be analyzed to select the most effective provision method. This makes it possible to provide the optimal method based on the user's past history.

[0084] The providing unit can determine the means of provision based on the user's current mood at the time of provision. The providing unit customizes the means of provision based on the user's current mood at the time of provision using AI. For example, when the user is sad, the voice of the deceased can be provided in a gentle voice. Also, when the user is relaxed, the voice of the deceased can be provided in a natural voice. Furthermore, when the user is excited, the voice of the deceased can be provided in a lively voice. This allows the voice to be provided in a manner according to the user's mood. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The providing unit can estimate the user's emotion and determine the priority of provision based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and determine the priority of provision based on the estimated user's emotion. For example, when the user is sad, data with high emotional value is provided preferentially. Also, when the user is relaxed, detailed data can be provided preferentially. Furthermore, when the user is in a hurry, important data can be provided quickly. This allows data to be provided in a priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The providing unit can select an appropriate method of providing the data based on the user's geographical location information at the time of providing the data. The providing unit uses AI to select the optimal method of providing the data by taking into consideration the user's geographical location information at the time of providing the data. For example, if the user is in a specific location, data of memories related to that location can be provided preferentially. Also, if the user is traveling, data related to the travel destination can be provided preferentially. Furthermore, if the user is in the hometown of a deceased person, data related to that location can be provided preferentially. This allows the data to be provided in the optimal manner based on the user's location information.

[0087] The providing unit can suggest a means of provision based on the user's social media activity at the time of provision. The providing unit uses AI to analyze the user's social media activity at the time of provision and suggest a means of provision. For example, data of memories shared by the user on social media can be provided preferentially. In addition, the content of the user's social media posts can be analyzed and related data can be provided. Furthermore, data related to places and events tagged by the user on social media can be provided preferentially. This makes it possible to provide the optimal means based on the user's social media activity.

[0088] The management unit can estimate the user's emotion and determine the selection of management data based on the estimated user's emotion. The management unit uses AI to estimate the user's emotion and selects management data based on the estimated user's emotion. For example, when the user is sad, data with high emotional value is preferentially managed. In addition, when the user is relaxed, detailed data can be preferentially managed. Furthermore, when the user is in a hurry, important data can be quickly managed. This allows data according to the user's emotion to be preferentially managed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The management unit can appropriately adjust the management algorithm by referring to past management data during management. The management unit uses AI to optimize the management algorithm by referring to past management data during management. For example, the management unit analyzes past management data and proposes an optimal management method. In addition, it is also possible to extract specific patterns from past management data and optimize the management algorithm. Furthermore, it is also possible to apply an algorithm that improves management efficiency based on past management data. This makes it possible to apply the optimal management method based on past data.

[0090] The management unit can estimate the user's emotions and determine the frequency of management based on the estimated user's emotions. The management unit uses AI to estimate the user's emotions and adjust the frequency of management based on the estimated user's emotions. For example, if the user is sad, the frequency of management is reduced to reduce the emotional burden. Also, if the user is relaxed, the frequency of management can be increased to collect detailed data. Furthermore, if the user is in a hurry, the frequency of management can be adjusted to quickly manage important data. This allows data to be managed at a frequency according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The management unit can determine the priority of the managed data based on the time of data submission during management. The management unit uses AI to weight the managed data based on the time of data submission during management. For example, recently submitted data can be preferentially managed. Data related to a specific anniversary can also be preferentially managed. Furthermore, data submitted by a user at a specific time can also be preferentially managed. This allows data to be preferentially managed based on the time of submission.

[0092] The access unit can estimate the user's emotion and determine the access method based on the estimated user's emotion. The access unit uses AI to estimate the user's emotion and adjust the access method based on the estimated user's emotion. For example, if the user is sad, a simple and intuitive access method is provided. Also, if the user is relaxed, an access method including detailed information can be provided. Furthermore, if the user is in a hurry, a method for quick access can be provided. This allows access in a manner according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The access unit can select an appropriate access method by referring to the user's past access history at the time of access. The access unit uses AI to select the optimal access method by referring to the user's past access history at the time of access. For example, the access unit preferentially provides the access method that the user has used in the past. The optimal access timing can also be selected from the user's past access history. Furthermore, the user's past access history can be analyzed to select the most effective access method. This allows access to be performed in the optimal way based on the user's past history.

[0094] The access unit can estimate the user's emotion and determine the priority of access based on the estimated user's emotion. The access unit uses AI to estimate the user's emotion and determine the priority of access based on the estimated user's emotion. For example, when the user is sad, data with high emotional value can be accessed preferentially. In addition, when the user is relaxed, detailed data can be accessed preferentially. Furthermore, when the user is in a hurry, important data can be accessed quickly. This allows access to be prioritized according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] The access unit can select an appropriate access method based on the user's device information at the time of access. The access unit uses AI to select the optimal access method by considering the user's device information at the time of access. For example, if the user is using a smartphone, an access method that matches the screen size can be provided. Also, if the user is using a tablet, an access method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible access method can be provided. This allows access to be made in the optimal way according to the user's device.

[0096] The reproduction unit can estimate the user's emotion and determine a reproduction method based on the estimated user's emotion. The reproduction unit estimates the user's emotion using the generation AI and adjusts the reproduction method based on the estimated user's emotion. For example, if the user is sad, the voice of the deceased is reproduced in a gentle tone. Also, if the user is relaxed, the voice of the deceased can be reproduced in a natural tone. Furthermore, if the user is excited, the voice of the deceased can be reproduced in a lively tone. In this way, the voice of the deceased can be reproduced in a manner according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The reproduction unit can analyze the user's past data and select an appropriate reproduction method at the time of reproduction. The reproduction unit uses the generation AI to analyze the user's past data and select the optimal reproduction method at the time of reproduction. For example, the reproduction unit preferentially provides reproduction methods that the user has used in the past. The reproduction unit can also select the optimal reproduction timing from the user's past data. Furthermore, the reproduction unit can analyze the user's past data and select the most effective reproduction method. This allows reproduction to be performed in the optimal way based on the user's past data.

[0098] The reproduction unit can determine the means of reproduction based on the user's current feelings at the time of reproduction. The reproduction unit customizes the means of reproduction based on the user's current feelings at the time of reproduction using the generation AI. For example, if the user is sad, the voice of the deceased is reproduced in a gentle voice. Also, if the user is relaxed, the voice of the deceased can be reproduced in a natural voice. Furthermore, if the user is excited, the voice of the deceased can be reproduced in a lively voice. This allows reproduction by a means according to the user's feelings. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] The reproduction unit can estimate the user's emotion and determine the priority of reproduction based on the estimated user's emotion. The reproduction unit estimates the user's emotion using the generation AI and determines the priority of reproduction based on the estimated user's emotion. For example, when the user is sad, data with high emotional value is preferentially reproduced. In addition, when the user is relaxed, detailed data can be preferentially reproduced. Furthermore, when the user is in a hurry, important data can be quickly reproduced. This allows reproduction to be performed with a priority according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The reproduction unit can select an appropriate reproduction method based on the user's geographic location information at the time of reproduction. The reproduction unit uses the generation AI to select the optimal reproduction method taking into account the user's geographic location information at the time of reproduction. For example, if the user is in a specific location, data of memories related to that location is preferentially reproduced. Also, if the user is traveling, data related to the travel destination can be preferentially reproduced. Furthermore, if the user is in the hometown of a deceased person, data related to that location can be preferentially reproduced. This allows reproduction in the optimal way based on the user's location information.

[0101] The reproducing unit can suggest a means of reproduction based on the user's social media activity at the time of reproduction. The reproducing unit uses the generative AI to analyze the user's social media activity at the time of reproduction and suggest a means of reproduction. For example, the reproducing unit prioritizes reproducing data of memories shared by the user on social media. It can also analyze the content of the user's social media posts and reproduce related data. Furthermore, it can prioritize reproducing data related to places and events tagged by the user on social media. This allows reproduction to be performed using the optimal means based on the user's social media activity.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The Memoria Talk system may further include an emotion analysis unit. The emotion analysis unit may analyze emotions from data input by the user and adjust the system's response based on the emotions. For example, if the user is sad, the system may provide kind words or encouraging messages. Also, if the user is happy, the system may provide congratulatory messages or positive feedback. Furthermore, the emotion analysis unit may track changes in the user's emotions and analyze long-term emotion trends. This may allow the system to provide appropriate support according to the user's emotions.

[0104] The Memoria Talk system may further include a notification unit. The notification unit may notify the user of memory data based on a specific date and time or event set by the user. For example, the notification unit may notify the user of related photos and messages on the birthday or anniversary of the deceased. If the user wishes to look back on memories during a specific time period, the notification unit may send a notification during that time period. Furthermore, the notification unit may suggest the optimal timing for notification based on the user's schedule. This allows the user to look back on important memories without forgetting them.

[0105] The Memoria Talk system may further include a sharing unit. The sharing unit provides a function for a user to share memories with other family members and friends. For example, if a user wants to share memories of a deceased person with family members, the user can easily generate and send a sharing link through the sharing unit. The sharing unit may also collect comments and reactions to the shared memories and provide feedback to the user. Furthermore, the sharing unit may provide privacy settings and limit the scope of sharing. This allows the user to share memories with peace of mind.

[0106] The Memoria Talk system may further include a customization section. The customization section allows the user to individually set the appearance and functions of the system. For example, the user can select a theme color or font that he or she likes. The customization section may also allow the user to enable or disable specific functions. Furthermore, the customization section may suggest optimal settings based on the user's usage history. This allows the user to build an easy-to-use system that suits him or her.

[0107] The Memoria Talk system may further include an archive unit. The archive unit stores past memory data for a long period of time and allows access as needed. For example, if a user wants to look back on memories from several years ago, the user can easily access the memories through the archive unit. The archive unit also provides a data backup function and can prevent data loss. The archive unit also provides a data organization function and allows the user to efficiently manage memories. This allows the user to store important memories for a long period of time and look back on them at any time.

[0108] The Memoria Talk system can further include a reminder unit. The reminder unit can remind the user of memory data based on a specific date and time or event set by the user. For example, the reminder unit can remind the user of related photos and messages on the birthday or anniversary of the deceased. Also, if the user wants to look back on memories at a specific time period, the reminder unit can send a reminder at that time period. Furthermore, the reminder unit can suggest the optimal timing for the reminder based on the user's schedule. This allows the user to look back on important memories without forgetting them.

[0109] The Memoria Talk system may further include an emotion feedback unit. The emotion feedback unit provides a function of feeding back emotions after the user uses the system. For example, the user may input emotions felt after hearing the voice of the deceased. The emotion feedback unit may also track changes in the user's emotions and analyze long-term trends in emotions. The emotion feedback unit may also adjust the system's response based on the user's emotions. This allows appropriate support to be provided according to the user's emotions.

[0110] The Memoria Talk system may further include an emotion history unit. The emotion history unit provides a function for recording and analyzing changes in the user's emotions over the long term. For example, the emotion felt by the user each time the user uses the system may be recorded, and the user's emotional tendencies may be analyzed based on the data. The emotion history unit may also adjust the system's response based on changes in the user's emotions. Furthermore, the emotion history unit provides a function for the user to look back on past emotion history. This allows the user to understand changes in his or her emotions and receive appropriate support.

[0111] The Memoria Talk system may further include an emotion prediction unit. The emotion prediction unit provides a function for predicting future emotions based on the user's past emotion data. For example, the emotion prediction unit predicts how the user will feel at a specific date and time or event, and adjusts the system's response based on the prediction. The emotion prediction unit may also track changes in the user's emotions in real time and update the prediction results. Furthermore, the emotion prediction unit supports the user to take appropriate measures based on the prediction results. This allows the user to prepare for future emotions.

[0112] The Memoria Talk system may further include an emotion alert unit. The emotion alert unit provides a function of issuing an alert when the user's emotion exceeds a certain threshold. For example, if the user is extremely sad, the system issues an alert and urges the user to rest. Also, if the user is overly excited, the system may issue an alert and suggest ways to relax. Furthermore, the emotion alert unit may monitor changes in the user's emotion in real time and issue an alert at an appropriate time. This allows the user to properly manage their emotions and maintain a healthy state.

[0113] The process flow of the second embodiment will be briefly described below.

[0114] Step 1: The reception unit receives data for reproducing the voice and words of the deceased from the user. The data input by the user includes, for example, a voice recording of the deceased, a text message, a video message, etc. The reception unit can receive voice recording data, a text message, and a video message. Step 2: The Reproducer uses the Generator AI to analyze the data received by the Receiving Unit and reproduce the voice and words of the deceased. The Reproducer uses voice synthesis technology and machine learning algorithms to reproduce the voice, speech style, phrases, and tone of the deceased. Step 3: The providing unit provides the voice or words of the deceased reproduced by the reproducing unit to the user. The providing unit can provide the voice or words of the deceased as an audio message, a video message, or a text message.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0117] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned reception unit, reproduction unit, provision unit, and management unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and receives data for reproducing the voice and words of the deceased from the user. The reproduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data using a generation AI to reproduce the voice and words of the deceased. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the reproduced voice and words of the deceased to the user. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and centrally manages photos, messages, and recorded voices, allowing the user to easily access memories. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0126] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned reception unit, reproduction unit, provision unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and receives data for reproducing the voice and words of the deceased from the user. The reproduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data using a generation AI to reproduce the voice and words of the deceased. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the reproduced voice and words of the deceased to the user. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and centrally manages photos, messages, and recorded voices, allowing the user to easily access memories. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements including the above-mentioned reception unit, reproduction unit, provision unit, and management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives data for reproducing the voice and words of the deceased from the user. The reproduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data using a generation AI to reproduce the voice and words of the deceased. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the reproduced voice and words of the deceased to the user. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and centrally manages photos, messages, and recorded voices, allowing the user to easily access memories. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] 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.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] Each of the multiple elements including the above-mentioned reception unit, reproduction unit, provision unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and receives data for reproducing the voice and words of the deceased from the user. The reproduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data using a generation AI to reproduce the voice and words of the deceased. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the reproduced voice and words of the deceased to the user. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and centrally manages photos, messages, and recorded voices, allowing the user to easily access memories. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0169] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0172] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0173] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0175] 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.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0178] 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.

[0179] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0180] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0184] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0186] (Appendix 1) A reception unit that receives data for reproducing the voice and words of the deceased from a user; A reproduction unit that analyzes the data received by the reception unit and reproduces the voice and words of the deceased; A providing unit that provides the voice or words of the deceased reproduced by the reproducing unit. A system characterized by: (Appendix 2) Equipped with a management section that centrally manages photos, messages, and recorded audio memories 2. The system of claim 1, (Appendix 3) The management unit Provide an access section that allows the user to access memories 3. The system of claim 2, (Appendix 4) The reproducing portion comprises: Learn the characteristics of the voice or speaking style of the deceased and use that to reproduce the voice or words of the deceased 2. The system of claim 1, (Appendix 5) The providing unit is Providing a user with a recreated voice or words of a deceased person 2. The system of claim 1, (Appendix 6) The reception unit is A method for estimating a user's emotions and determining the timing of data acceptance based on the estimated user's emotions 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past data entry history and select the appropriate reception method 2. The system of claim 1, (Appendix 8) The reception unit is As data is received, it is filtered based on the user's current mood or interests. 2. The system of claim 1, (Appendix 9) The reception unit is Estimate user emotions and prioritize data to be accepted based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When accepting data, prioritize data that is more relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 11) The reception unit is At the time of data entry, relevant data is received based on the user's social media activity. 2. The system of claim 1, (Appendix 12) The reproducing portion comprises: Estimate the user's emotion and determine the representation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 13) The reproducing portion comprises: During reconstruction, the level of detail in the reconstruction is adjusted based on the characteristics of the deceased's voice 2. The system of claim 1, (Appendix 14) The reproducing portion comprises: During reconstruction, specific reconstruction algorithms are applied depending on the category of the deceased 2. The system of claim 1, (Appendix 15) The reproducing portion comprises: A user's emotion is estimated, and the reproduction length is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The reproducing portion comprises: During reconstruction, reconstruction priorities are determined based on when the deceased's data was submitted. 2. The system of claim 1, (Appendix 17) The reproducing portion comprises: During reenactments, adjust the order of reenactments based on the relationships of the deceased 2. The system of claim 1, (Appendix 18) The providing unit is A user's emotion is estimated, and a method of providing the information is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The providing unit is When providing the service, the appropriate method of providing the service is selected by analyzing the user's past usage history. 2. The system of claim 1, (Appendix 20) The providing unit is At the time of delivery, the delivery method is determined based on the user's current mood 2. The system of claim 1, (Appendix 21) The providing unit is Estimate user sentiment and determine priority of offerings based on the estimated user sentiment 2. The system of claim 1, (Appendix 22) The providing unit is At the time of delivery, the appropriate delivery method is selected based on the user's geographic location information. 2. The system of claim 1, (Appendix 23) The providing unit is At the time of delivery, suggest delivery methods based on the user's social media activity 2. The system of claim 1, (Appendix 24) The management unit A user's emotion is estimated, and a selection of management data is determined based on the estimated user's emotion. 3. The system of claim 2, (Appendix 25) The management unit During management, past management data is referenced to appropriately adjust management algorithms. 3. The system of claim 2, (Appendix 26) The management unit A user's emotion is estimated, and a frequency of management is determined based on the estimated user's emotion. 3. The system of claim 2, (Appendix 27) The management unit During administration, prioritize administration data based on when the data was submitted 3. The system of claim 2, (Appendix 28) The access unit is A user's emotion is estimated, and an access method is determined based on the estimated user's emotion. 4. The system of claim 3. (Appendix 29) The access unit is When accessing, the appropriate access method is selected by referring to the user's past access history. 4. The system of claim 3. (Appendix 30) The access unit is Estimate user emotions and determine access priorities based on the estimated user emotions. 4. The system of claim 3. (Appendix 31) The access unit is When accessing, select the appropriate access method based on the user's device information 4. The system of claim 3. (Appendix 32) The reproducing portion comprises: Estimate the user's emotions and determine the reproduction method based on the estimated user's emotions. 2. The system of claim 1, (Appendix 33) The reproducing portion comprises: When reproducing, analyze the user's past data and select the appropriate reproduction method. 2. The system of claim 1, (Appendix 34) The reproducing portion comprises: At the time of reproduction, the means of reproduction is determined based on the user's current mood. 2. The system of claim 1, (Appendix 35) The reproducing portion comprises: Estimate the user's emotions and determine the priority of reproduction based on the estimated user's emotions. 2. The system of claim 1, (Appendix 36) The reproducing portion comprises: When reproducing, select the appropriate reproduction method based on the user's geographic location information. 2. The system of claim 1, (Appendix 37) The reproducing portion comprises: When replaying, we suggest ways to replay based on the user's social media activity. 2. The system of claim 1, [Explanation of symbols]

[0187] 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 reception unit that receives data for reproducing the voice and words of the deceased from a user; A reproduction unit that analyzes the data received by the reception unit and reproduces the voice and words of the deceased; A providing unit that provides the voice or words of the deceased reproduced by the reproducing unit. A system characterized by:

2. Equipped with a management section that centrally manages photos, messages, and recorded audio memories 2. The system of claim 1 .

3. The management unit Provide an access section that allows the user to access memories 3. The system of claim 2.

4. The reproducing portion comprises: Learn the characteristics of the voice or speaking style of the deceased and use that to reproduce the voice or words of the deceased 2. The system of claim 1 .

5. The reception unit is A method for estimating a user's emotions and determining the timing of data acceptance based on the estimated user's emotions 2. The system of claim 1 .

6. The reception unit is Analyze the user's past data entry history and select the appropriate reception method 2. The system of claim 1 .

7. The reception unit is As data is received, it is filtered based on the user's current mood or interests.

2. The system of claim 1 .

8. The reception unit is Estimate user emotions and prioritize data to be accepted based on the estimated user emotions.

2. The system of claim 1 .

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