System

Abstract

To alleviate the sense of loss of a bereaved family by utilizing photos and audio data of the deceased.SOLUTION: A system according to an embodiment comprises a taking-in unit, an analysis unit, a synthesis unit, and a response generation unit. The taking-in unit takes in a photo or voice data of the deceased. The analysis unit analyzes the data taken in by the taking-in unit. The synthesis unit performs synthesis on the basis of the data analyzed by the analysis unit. The response generation unit generates a response to a user's inquiry.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] Conventional technology has limited the means available for remembering the deceased, and has the problem of not being able to adequately alleviate the sense of loss felt by bereaved families.

[0005] The system of the embodiment aims to ease the sense of loss felt by bereaved families by utilizing photographs and audio data of the deceased. [Means for solving the problem]

[0006] The system according to the embodiment includes an import unit, an analysis unit, a synthesis unit, and a response generation unit. The import unit imports photographic or audio data of the deceased. The analysis unit analyzes the data imported by the import unit. The synthesis unit performs synthesis based on the data analyzed by the analysis unit. The response generation unit generates a response to a user's question. Effect of the Invention

[0007] The system according to the embodiment can ease the sense of loss felt by bereaved families by utilizing photographs and audio data of the deceased. [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 a 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 palliative care system according to the embodiment of the present invention is a system for bereaved families who remember the deceased. This system utilizes a generation AI (for example, gpt-4 (registered trademark) or Gemini) and AI technology to provide a mechanism for interacting with the deceased through a screen. Specifically, the system takes in a photo and voice data of the deceased, and the AI ​​synthesizes it so that it sounds like the deceased is speaking. Next, the generation AI stores the characteristics of the deceased, and responds to the user's questions as if the deceased is answering them. This system can provide a place of comfort for elderly people living alone who have lost a loved one and are plagued by loneliness. For example, the user takes in a photo and voice data of the deceased into the system. At this time, the photo and voice data of the deceased are analyzed by AI, and the characteristics of the deceased are extracted. For example, the tone of voice, speech, and facial expressions of the deceased are analyzed. Next, based on the analyzed characteristics, the AI ​​synthesizes it so that it sounds like the deceased is speaking. For example, the tone of voice and speech of the deceased are reproduced, and the system responds to the user's questions as if the deceased is answering them. At this time, the generation AI stores the characteristics of the deceased, and generates an appropriate response to the user's questions. Furthermore, responses to questions posed by the user are displayed on the screen as if the deceased were answering them. For example, if the user asks, "How was your day?", the deceased responds, "It was a very good day today." In this way, the user can have an experience as if they were having a conversation with the deceased. This system can provide a place of comfort for elderly people living alone who have lost a loved one and are plagued by loneliness. By conversing with the deceased, the user can find peace of mind. In addition, by reproducing the voice and facial expression of the deceased, the user can look back on memories with the deceased. For example, by talking about the deceased's favorite topics and reminiscences, the user can feel a bond with the deceased. In this way, the palliative care system can provide a place of comfort for bereaved families who are mourning the deceased.

[0029] The palliative care system according to the embodiment includes an import unit, an analysis unit, a synthesis unit, and a response generation unit. The import unit imports a photo or audio data of the deceased. For example, the import unit inputs a photo or audio data of the deceased provided by a user into the system. The import unit can import, for example, image files in JPEG format or PNG format, and audio files in MP3 format or WAV format. The analysis unit analyzes the data imported by the import unit. For example, the analysis unit analyzes the tone of voice, speech, facial expression, etc. of the deceased. The analysis unit uses a voice analysis algorithm to extract the characteristics of the deceased's voice. For example, the analysis unit performs voice waveform analysis and frequency analysis to analyze the tone of voice and speech of the deceased. In addition, the analysis unit uses image analysis technology to analyze the facial expression of the deceased. For example, the analysis unit uses face recognition technology and facial expression analysis algorithms to analyze the facial expression of the deceased. The synthesis unit performs synthesis based on the data analyzed by the analysis unit. For example, the synthesis unit reproduces the tone of voice and speech of the deceased. The synthesis unit reproduces the voice of the deceased using a voice synthesis technique. For example, the synthesis unit reproduces the tone of the deceased's voice and speaking style using a voice filtering technique. The synthesis unit also reproduces the facial expression of the deceased using an image synthesis technique. For example, the synthesis unit reproduces the facial expression of the deceased using an image filtering technique. The response generation unit generates a response to a user's question. For example, the response generation unit generates an appropriate response to the user's question using a natural language generation technique. The response generation unit generates a response using a generation AI as if the deceased were answering the user's question. For example, when the user asks "How was today?", the response generation unit generates a response such that the deceased answers "Today was a very good day." As a result, the palliative care system according to the embodiment can provide an experience as if the user were interacting with the deceased by capturing, analyzing, synthesizing, and generating a response from the photo and voice data of the deceased.

[0030] The capture unit captures the photo or audio data of the deceased. For example, the capture unit inputs the photo and audio data of the deceased provided by the user into the system. The capture unit can capture, for example, image files in JPEG format or PNG format, and audio files in MP3 format or WAV format. Specifically, the capture unit has a function of automatically recognizing the image and audio files uploaded by the user from a smartphone or a PC and saving them in the system. In the case of an image file, the capture unit automatically adjusts the resolution and file size and saves them in an optimal state. In the case of an audio file, noise reduction and volume adjustment are performed and the file is saved as clear audio data. Furthermore, the capture unit supports multiple file formats, and the system can automatically convert data in an appropriate format regardless of the format provided by the user. For example, capture from various data sources is possible, such as image files scanned from old photos and audio files digitized from cassette tapes. This allows the capture unit to easily provide the deceased's photo and audio data to the system, improving the usability of the entire system.

[0031] The analysis unit analyzes the data captured by the capture unit. For example, the analysis unit analyzes the tone of the deceased's voice, speech style, facial expression, etc. The analysis unit uses a voice analysis algorithm to extract the characteristics of the deceased's voice. Specifically, the analysis unit performs voice waveform analysis and frequency analysis to analyze the tone of the deceased's voice and speech style. In the voice waveform analysis, the waveform of the voice data is analyzed along the time axis to grasp the strength and rhythm of the voice. In the frequency analysis, the voice data is broken down into frequency components and the pitch and timbre of the voice are analyzed. In addition, the analysis unit uses image analysis technology to analyze the facial expression of the deceased. For example, the analysis unit uses face recognition technology and facial expression analysis algorithms to analyze the facial expression of the deceased. In the face recognition technology, the position and feature points of the face are detected from the image data, and changes in facial expression are analyzed. In the facial expression analysis algorithm, the movement and shape of the feature points of the face are analyzed to identify emotions such as smiling and sadness. In addition, the analysis unit stores the features of the deceased's voice and facial expression in a database, making them available for use in the subsequent synthesis unit and response generation unit. This enables the analysis unit to accurately grasp the characteristics of the deceased's voice and facial expressions, improving the accuracy of the entire system.

[0032] The synthesis unit performs synthesis based on the data analyzed by the analysis unit. For example, the synthesis unit reproduces the tone of the deceased's voice and manner of speaking. The synthesis unit reproduces the voice of the deceased using a voice synthesis technology. Specifically, the synthesis unit reproduces the tone of the deceased's voice and manner of speaking using a voice filtering technology. In the voice filtering technology, the tone and rhythm of the synthetic voice are adjusted based on the voice characteristics extracted by the analysis unit to make it closer to the voice of the deceased. In addition, the synthesis unit reproduces the facial expression of the deceased using an image synthesis technology. For example, the synthesis unit reproduces the facial expression of the deceased using an image filtering technology. In the image filtering technology, the facial expression of the synthetic image is adjusted based on the facial expression characteristics extracted by the analysis unit to make it closer to the facial expression of the deceased. In addition, the synthesis unit reproduces natural movements as if the deceased is speaking using a technology that synchronizes the voice and the image. For example, the mouth movement is adjusted to match the rhythm of the voice to realistically reproduce the facial expression while speaking. As a result, the synthesis unit reproduces the voice and facial expression of the deceased with high accuracy, and can provide the user with an experience as if the deceased is actually speaking.

[0033] The response generation unit generates a response to a user's question. For example, the response generation unit uses natural language generation technology to generate an appropriate response to the user's question. The response generation unit uses generation AI to generate a response that sounds as if the deceased were answering the user's question. Specifically, the response generation unit analyzes the user's question and generates an appropriate response based on the content. For example, when the user asks, "How was today?", the response generation unit generates a response such as "It was a very good day today" while reproducing the tone of voice and speaking style of the deceased. The response generation unit can learn the past statements and behavior patterns of the deceased and generate a response based on them. For example, by incorporating words and phrases that the deceased often used, a more natural response is generated. In addition, the response generation unit can adjust the content of the response according to the user's emotions and situation. For example, if the user is sad, the response generation unit selects words of comfort and responds. In this way, the response generation unit can provide the user with an experience as if the deceased was actually speaking to them, and support the user's mental care.

[0034] The palliative care system includes a storage unit that stores the characteristics of the deceased. The storage unit stores the characteristics of the deceased. For example, the storage unit stores the tone of voice, manner of speech, facial expressions, and the like of the deceased. The storage unit can store the characteristics of the deceased using AI. For example, the storage unit can use a voice analysis algorithm to store the tone of voice and manner of speech of the deceased. In addition, the storage unit can use an image analysis technique to store the facial expressions of the deceased. In this way, the storage unit can realize a more natural dialogue by storing the characteristics of the deceased.

[0035] The palliative care system includes a reception unit that receives a user's question. The reception unit receives the user's question. For example, the reception unit can receive voice input and text input. The reception unit can receive the user's question using AI. For example, the reception unit can convert the user's voice input into text using voice recognition technology. In addition, the reception unit can analyze the user's text input using natural language processing technology. In this way, the reception unit can smoothly start a dialogue by receiving the user's question.

[0036] The palliative care system includes a display unit that displays the response. The display unit displays the response. For example, the display unit can display the response on a screen. The display unit enables the user to visually confirm the dialogue. For example, the display unit can adjust the screen layout and font size to display the response so that it is easy for the user to see. In this way, the display unit can enable the user to visually confirm the dialogue by displaying the response.

[0037] The analysis unit can analyze the tone of voice or speaking style, and facial expressions of the deceased. The analysis unit analyzes the tone of voice, speaking style, and facial expressions of the deceased. For example, the analysis unit can analyze the tone of voice and speaking style of the deceased by performing voice waveform analysis and frequency analysis. In addition, the analysis unit can analyze the facial expressions of the deceased by using face recognition technology and facial expression analysis algorithms. In this way, the analysis unit can realize a more realistic dialogue by analyzing the tone of voice, speaking style, and facial expressions of the deceased.

[0038] The synthesis unit can reproduce the tone of voice and the manner of speaking of the deceased. The synthesis unit reproduces the tone of voice and the manner of speaking of the deceased. For example, the synthesis unit can reproduce the voice of the deceased using a voice synthesis technology. The synthesis unit can reproduce the tone of voice and the manner of speaking of the deceased using a voice filtering technology. In this way, the synthesis unit can provide an experience as if the user were conversing with the deceased by reproducing the tone of voice and the manner of speaking of the deceased.

[0039] The import unit can analyze the user's past import history and select an appropriate import method when importing photos and audio data of the deceased. The import unit can suggest an optimal import method based on, for example, the format of data imported by the user in the past. The import unit can also adjust the import speed based on the amount of data imported by the user in the past. Furthermore, the import unit can select an import target based on the type of data imported by the user in the past. In this way, the import unit can provide an optimal import method by analyzing the past import history.

[0040] The capture unit can filter the photographs and audio data of the deceased based on the user's current living situation and areas of interest when capturing the photographs and audio data of the deceased. For example, when the user is busy, the capture unit can preferentially capture only important data. Also, when the user has a specific area of ​​interest, the capture unit can preferentially capture data related to that area. Furthermore, the capture unit can adjust the amount of captured data according to the user's living situation. In this way, the capture unit can provide useful information to the user by capturing data according to the user's living situation and areas of interest.

[0041] When capturing photographs and audio data of the deceased, the capturing unit can preferentially capture highly relevant data in consideration of the geographical location information of the user. For example, when the user is in the hometown of the deceased, the capturing unit can preferentially capture data related to that place. Also, when the user is traveling, the capturing unit can preferentially capture data related to the travel destination. Furthermore, when the user frequently visits a particular place, the capturing unit can preferentially capture data related to that place. In this way, the capturing unit can provide highly relevant data based on the geographical location information of the user.

[0042] The capture unit can analyze the user's social media activity and capture related data when capturing the photo and audio data of the deceased. For example, the capture unit can capture photo and audio data shared by the user on social media. The capture unit can also capture data related to the deceased mentioned by the user on social media. Furthermore, the capture unit can capture data related to the deceased shared by the user's social media friends. In this way, the capture unit can provide related data based on the user's social media activity.

[0043] When analyzing the photo or voice data of the deceased, the analysis unit can improve the accuracy of the analysis by referring to the past behavioral history of the deceased. The analysis unit can improve the analysis accuracy of the voice data, for example, based on the past speech history of the deceased. In addition, the analysis unit can improve the analysis accuracy of the photo data based on the past photo history of the deceased. Furthermore, the analysis unit can optimize the analysis algorithm based on the past behavioral patterns of the deceased. This allows the analysis unit to improve the accuracy of the analysis by referring to the past behavioral history of the deceased.

[0044] When analyzing the photo and audio data of the deceased, the analysis unit can apply different analysis algorithms based on the deceased's field of interest. For example, if the deceased was interested in music, the analysis unit can preferentially analyze music-related data. Furthermore, if the deceased was interested in sports, the analysis unit can preferentially analyze sports-related data. Furthermore, if the deceased was interested in literature, the analysis unit can preferentially analyze literature-related data. This allows the analysis unit to provide more appropriate analysis results by applying analysis algorithms based on the deceased's field of interest.

[0045] The analysis unit can take into consideration the geographical background of the deceased when analyzing the photo and voice data of the deceased. For example, if the deceased lived in a specific area, the analysis unit can prioritize analyzing data related to that area. In addition, if the deceased liked to travel, the analysis unit can prioritize analyzing data related to travel destinations. Furthermore, if the deceased frequently visited a specific place, the analysis unit can prioritize analyzing data related to that place. In this way, the analysis unit can provide more appropriate analysis results by taking into consideration the geographical background of the deceased.

[0046] The analysis unit can improve the accuracy of the analysis when analyzing the photo or voice data of the deceased by referring to related literature and materials. The analysis unit can improve the analysis accuracy of the voice data by referring to literature written by the deceased, for example. The analysis unit can also improve the analysis accuracy of the photo data by referring to materials of a project in which the deceased was involved. Furthermore, the analysis unit can optimize the analysis algorithm by referring to records of events in which the deceased participated. This allows the analysis unit to improve the accuracy of the analysis by referring to related literature and materials.

[0047] The synthesis unit can improve the accuracy of synthesis by referring to the past voice data of the deceased when synthesizing the voice and facial expression of the deceased. The synthesis unit can improve the synthesis accuracy of the voice data based on, for example, the past utterances of the deceased. Furthermore, the synthesis unit can improve the synthesis accuracy of the facial expression data based on the past photos of the deceased. Furthermore, the synthesis unit can optimize the synthesis algorithm based on the past behavior patterns of the deceased. In this way, the synthesis unit can improve the accuracy of synthesis by referring to the past voice data of the deceased.

[0048] The synthesis unit can apply different synthesis algorithms based on the area of ​​interest of the deceased when synthesizing the voice and facial expression of the deceased. For example, if the deceased was interested in music, the synthesis unit can synthesize a music-related voice and facial expression. Also, if the deceased was interested in sports, the synthesis unit can synthesize a sports-related voice and facial expression. Furthermore, if the deceased was interested in literature, the synthesis unit can synthesize a literature-related voice and facial expression. In this way, the synthesis unit can provide a more appropriate synthesis result by applying a synthesis algorithm based on the area of ​​interest of the deceased.

[0049] The synthesis unit can synthesize the voice and facial expression of the deceased while taking into consideration the geographical background of the deceased. For example, if the deceased lived in a specific area, the synthesis unit can synthesize a voice and facial expression related to that area. In addition, if the deceased liked to travel, the synthesis unit can synthesize a voice and facial expression related to the travel destination. Furthermore, if the deceased frequently visited a specific place, the synthesis unit can synthesize a voice and facial expression related to that place. In this way, the synthesis unit can provide a more appropriate synthesis result by taking into consideration the geographical background of the deceased.

[0050] The synthesis unit can improve the accuracy of synthesis by referring to related literature and materials when synthesizing the voice and facial expressions of the deceased. The synthesis unit can improve the synthesis accuracy of the voice data by referring to literature written by the deceased, for example. The synthesis unit can also improve the synthesis accuracy of the facial expression data by referring to materials of a project in which the deceased was involved. Furthermore, the synthesis unit can optimize the synthesis algorithm by referring to records of events in which the deceased participated. This allows the synthesis unit to improve the accuracy of synthesis by referring to related literature and materials.

[0051] The response generation unit can improve the accuracy of the response when generating a response by referring to the past utterance history of the deceased. The response generation unit can generate a natural response, for example, based on the past utterances of the deceased. Furthermore, the response generation unit can maintain the consistency of the response based on the past conversation patterns of the deceased. Furthermore, the response generation unit can generate an appropriate response based on the content of the past utterances of the deceased. Thereby, the response generation unit can improve the accuracy of the response by referring to the past utterance history of the deceased.

[0052] The response generation unit can apply different response generation algorithms based on the deceased's field of interest when generating a response. For example, if the deceased was interested in music, the response generation unit can generate a response related to music. If the deceased was interested in sports, the response generation unit can generate a response related to sports. If the deceased was interested in literature, the response generation unit can generate a response related to literature. This allows the response generation unit to provide a more appropriate response by applying a response generation algorithm based on the deceased's field of interest.

[0053] The response generation unit can generate a response taking into consideration the geographical background of the deceased when generating a response. For example, if the deceased lived in a specific area, the response generation unit can generate a response related to the area. In addition, if the deceased liked to travel, the response generation unit can generate a response related to a travel destination. Furthermore, if the deceased frequently visited a specific place, the response generation unit can generate a response related to the place. In this way, the response generation unit can provide a more appropriate response by taking into consideration the geographical background of the deceased.

[0054] The response generation unit can improve the accuracy of the response by referring to related literature and materials when generating a response. The response generation unit can improve the accuracy of the response by referring to literature written by the deceased, for example. The response generation unit can also maintain the consistency of the response by referring to materials of projects in which the deceased was involved. Furthermore, the response generation unit can generate an appropriate response by referring to records of events in which the deceased participated. This allows the response generation unit to improve the accuracy of the response by referring to related literature and materials.

[0055] The memory unit can appropriately adjust the memory algorithm by referring to the past behavior history of the deceased when storing. The memory unit can optimize the memory algorithm based on, for example, the past speech history of the deceased. The memory unit can also optimize the memory algorithm based on the past photo history of the deceased. Furthermore, the memory unit can optimize the memory algorithm based on the past behavior patterns of the deceased. In this way, the memory unit can optimize the memory algorithm by referring to the past behavior history of the deceased.

[0056] The storage unit can weight the stored data based on the area of ​​interest of the deceased when storing. For example, if the deceased was interested in music, the storage unit can store the data with emphasis on music-related data. Furthermore, if the deceased was interested in sports, the storage unit can store the data with emphasis on sports-related data. Furthermore, if the deceased was interested in literature, the storage unit can store the data with emphasis on literature-related data. In this way, the storage unit can store more appropriate data by weighting the stored data based on the area of ​​interest of the deceased.

[0057] When receiving a question, the reception unit can select the optimal reception method by referring to the user's past question history. For example, the reception unit can preferentially suggest a question method that the user has used frequently in the past. The reception unit can also suggest a related question method based on the content of a question used by the user in the past. Furthermore, the reception unit can predict and suggest a question method to be used in a specific time period from the user's past question history. In this way, the reception unit can provide the optimal reception method by referring to the user's past question history.

[0058] The reception unit can select the optimal reception method in consideration of the device information of the user when receiving the inquiry. For example, when the user is using a smartphone, the reception unit can preferentially suggest voice input. Furthermore, when the user is using a tablet, the reception unit can preferentially suggest touch input. Furthermore, when the user is using a desktop, the reception unit can preferentially suggest keyboard input. This allows the reception unit to provide the optimal reception method based on the device information of the user.

[0059] When displaying, the display unit can select the optimal display method by referring to the user's past operation history. The display unit can, for example, suggest the optimal display method based on the display method used by the user in the past. The display unit can also predict and suggest the display method to be used in a specific time period from the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and suggest the most efficient display method. In this way, the display unit can provide the optimal display method by referring to the user's past operation history.

[0060] The display unit can select the optimal display method in consideration of the user's device information at the time of display. For example, when the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, when the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can provide a detailed display method. This allows the display unit to provide the optimal display method based on the user's device information.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] When importing photographs or audio data of the deceased, the palliative care system can analyze the user's past import history and select an appropriate import method. For example, the system can suggest an optimal import method based on the format of data imported by the user in the past. The system can also adjust the import speed based on the amount of data imported by the user in the past. Furthermore, the system can select the data to be imported based on the type of data imported by the user in the past. In this way, the system can provide an optimal import method by analyzing the past import history.

[0063] When importing photographs and audio data of the deceased, the palliative care system can filter the data based on the user's current living situation and areas of interest. For example, if the user is busy, only important data can be imported preferentially. Also, if the user has a specific area of ​​interest, data related to that area can be imported preferentially. Furthermore, the amount of imported data can be adjusted according to the user's living situation. This makes it possible to provide useful information to the user by importing data according to the user's living situation and areas of interest.

[0064] When analyzing the photo and voice data of the deceased, the palliative care system can improve the accuracy of the analysis by referring to the past behavioral history of the deceased. For example, the accuracy of the analysis of voice data can be improved based on the past speech history of the deceased. Also, the accuracy of the analysis of photo data can be improved based on the past photo history of the deceased. Furthermore, the analysis algorithm can be optimized based on the past behavioral patterns of the deceased. In this way, the accuracy of the analysis can be improved by referring to the past behavioral history of the deceased.

[0065] When analyzing the photo and audio data of the deceased, the palliative care system can apply different analysis algorithms based on the deceased's areas of interest. For example, if the deceased was interested in music, music-related data can be preferentially analyzed. Also, if the deceased was interested in sports, sports-related data can be preferentially analyzed. Furthermore, if the deceased was interested in literature, literature-related data can be preferentially analyzed. In this way, by applying analysis algorithms based on the deceased's areas of interest, more appropriate analysis results can be provided.

[0066] When analyzing the photographs and audio data of the deceased, the palliative care system can improve the accuracy of the analysis by referring to related literature and materials. For example, the accuracy of the analysis of the audio data can be improved by referring to literature written by the deceased. In addition, the accuracy of the analysis of the photo data can be improved by referring to materials of projects in which the deceased was involved. Furthermore, the analysis algorithm can be optimized by referring to records of events in which the deceased participated. In this way, the accuracy of the analysis can be improved by referring to related literature and materials.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The import unit imports a photo or audio data of the deceased. For example, the import unit inputs a photo or audio data of the deceased provided by a user into the system. The import unit can import image files in JPEG or PNG format, and audio files in MP3 or WAV format. Step 2: The analysis unit analyzes the data captured by the capture unit. For example, the analysis unit analyzes the tone of voice, speaking style, facial expressions, etc. of the deceased. The analysis unit uses a voice analysis algorithm to extract characteristics of the deceased's voice. For example, it performs voice waveform analysis and frequency analysis to analyze the tone of voice and speaking style of the deceased. It also uses image analysis technology to analyze the facial expressions of the deceased. For example, it uses face recognition technology and facial expression analysis algorithms to analyze the facial expressions of the deceased. Step 3: The synthesis unit performs synthesis based on the data analyzed by the analysis unit. For example, the synthesis unit reproduces the tone of voice and manner of speaking of the deceased. The synthesis unit reproduces the voice of the deceased using voice synthesis technology. For example, the synthesis unit reproduces the tone of voice and manner of speaking of the deceased using voice filtering technology. The synthesis unit also reproduces the facial expressions of the deceased using image synthesis technology. For example, the synthesis unit reproduces the facial expressions of the deceased using image filtering technology. Step 4: The response generation unit generates a response to the user's question. For example, natural language generation technology is used to generate an appropriate response to the user's question. A generation AI is used to generate a response that sounds like the deceased would be answering the user's question. For example, if the user asks, "How was today?", a response is generated that sounds like the deceased would be saying, "Today was a very good day."

[0069] (Example 2) The palliative care system according to the embodiment of the present invention is a system for bereaved families who remember the deceased. This system utilizes a generation AI (for example, gpt-4 or Gemini) and AI technology to provide a mechanism for interacting with the deceased through a screen. Specifically, the system takes in a photo and voice data of the deceased, and the AI ​​synthesizes it so that it sounds like the deceased is speaking. Next, the generation AI stores the characteristics of the deceased, and responds to the user's questions as if the deceased is answering them. This system can provide a place of comfort for elderly people living alone who have lost a loved one and are plagued by loneliness. For example, the user takes in a photo and voice data of the deceased into the system. At this time, the photo and voice data of the deceased are analyzed by AI, and the characteristics of the deceased are extracted. For example, the tone of voice, speech, facial expressions, etc. of the deceased are analyzed. Next, based on the analyzed characteristics, the AI ​​synthesizes it so that it sounds like the deceased is speaking. For example, the tone of voice and speech of the deceased are reproduced, and the system responds to the user's questions as if the deceased is answering them. At this time, the generation AI stores the characteristics of the deceased, and generates an appropriate response to the user's questions. Furthermore, responses to questions posed by the user are displayed on the screen as if the deceased were answering them. For example, if the user asks, "How was your day?", the deceased responds, "It was a very good day today." In this way, the user can have an experience as if they were having a conversation with the deceased. This system can provide a place of comfort for elderly people living alone who have lost a loved one and are plagued by loneliness. By conversing with the deceased, the user can find peace of mind. In addition, by reproducing the voice and facial expression of the deceased, the user can look back on memories with the deceased. For example, by talking about the deceased's favorite topics and reminiscences, the user can feel a bond with the deceased. In this way, the palliative care system can provide a place of comfort for bereaved families who are mourning the deceased.

[0070] The palliative care system according to the embodiment includes an import unit, an analysis unit, a synthesis unit, and a response generation unit. The import unit imports a photo or audio data of the deceased. For example, the import unit inputs a photo or audio data of the deceased provided by a user into the system. The import unit can import, for example, image files in JPEG format or PNG format, and audio files in MP3 format or WAV format. The analysis unit analyzes the data imported by the import unit. For example, the analysis unit analyzes the tone of voice, speech, facial expression, etc. of the deceased. The analysis unit uses a voice analysis algorithm to extract the characteristics of the deceased's voice. For example, the analysis unit performs voice waveform analysis and frequency analysis to analyze the tone of voice and speech of the deceased. In addition, the analysis unit uses image analysis technology to analyze the facial expression of the deceased. For example, the analysis unit uses face recognition technology and facial expression analysis algorithms to analyze the facial expression of the deceased. The synthesis unit performs synthesis based on the data analyzed by the analysis unit. For example, the synthesis unit reproduces the tone of voice and speech of the deceased. The synthesis unit reproduces the voice of the deceased using a voice synthesis technique. For example, the synthesis unit reproduces the tone of the deceased's voice and speaking style using a voice filtering technique. The synthesis unit also reproduces the facial expression of the deceased using an image synthesis technique. For example, the synthesis unit reproduces the facial expression of the deceased using an image filtering technique. The response generation unit generates a response to a user's question. For example, the response generation unit generates an appropriate response to the user's question using a natural language generation technique. The response generation unit generates a response using a generation AI as if the deceased were answering the user's question. For example, when the user asks "How was today?", the response generation unit generates a response such that the deceased answers "Today was a very good day." As a result, the palliative care system according to the embodiment can provide an experience as if the user were interacting with the deceased by capturing, analyzing, synthesizing, and generating a response from the photo and voice data of the deceased.

[0071] The capture unit captures the photo or audio data of the deceased. For example, the capture unit inputs the photo and audio data of the deceased provided by the user into the system. The capture unit can capture, for example, image files in JPEG format or PNG format, and audio files in MP3 format or WAV format. Specifically, the capture unit has a function of automatically recognizing the image and audio files uploaded by the user from a smartphone or a PC and saving them in the system. In the case of an image file, the capture unit automatically adjusts the resolution and file size and saves them in an optimal state. In the case of an audio file, noise reduction and volume adjustment are performed and the file is saved as clear audio data. Furthermore, the capture unit supports multiple file formats, and the system can automatically convert data in an appropriate format regardless of the format provided by the user. For example, capture from various data sources is possible, such as image files scanned from old photos and audio files digitized from cassette tapes. This allows the capture unit to easily provide the deceased's photo and audio data to the system, improving the usability of the entire system.

[0072] The analysis unit analyzes the data captured by the capture unit. For example, the analysis unit analyzes the tone of the deceased's voice, speech style, facial expression, etc. The analysis unit uses a voice analysis algorithm to extract the characteristics of the deceased's voice. Specifically, the analysis unit performs voice waveform analysis and frequency analysis to analyze the tone of the deceased's voice and speech style. In the voice waveform analysis, the waveform of the voice data is analyzed along the time axis to grasp the strength and rhythm of the voice. In the frequency analysis, the voice data is broken down into frequency components and the pitch and timbre of the voice are analyzed. In addition, the analysis unit uses image analysis technology to analyze the facial expression of the deceased. For example, the analysis unit uses face recognition technology and facial expression analysis algorithms to analyze the facial expression of the deceased. In the face recognition technology, the position and feature points of the face are detected from the image data, and changes in facial expression are analyzed. In the facial expression analysis algorithm, the movement and shape of the feature points of the face are analyzed to identify emotions such as smiling and sadness. In addition, the analysis unit stores the features of the deceased's voice and facial expression in a database, making them available for use in the subsequent synthesis unit and response generation unit. This enables the analysis unit to accurately grasp the characteristics of the deceased's voice and facial expressions, improving the accuracy of the entire system.

[0073] The synthesis unit performs synthesis based on the data analyzed by the analysis unit. For example, the synthesis unit reproduces the tone of the deceased's voice and manner of speaking. The synthesis unit reproduces the voice of the deceased using a voice synthesis technology. Specifically, the synthesis unit reproduces the tone of the deceased's voice and manner of speaking using a voice filtering technology. In the voice filtering technology, the tone and rhythm of the synthetic voice are adjusted based on the voice characteristics extracted by the analysis unit to make it closer to the voice of the deceased. In addition, the synthesis unit reproduces the facial expression of the deceased using an image synthesis technology. For example, the synthesis unit reproduces the facial expression of the deceased using an image filtering technology. In the image filtering technology, the facial expression of the synthetic image is adjusted based on the facial expression characteristics extracted by the analysis unit to make it closer to the facial expression of the deceased. In addition, the synthesis unit reproduces natural movements as if the deceased is speaking using a technology that synchronizes the voice and the image. For example, the mouth movement is adjusted to match the rhythm of the voice to realistically reproduce the facial expression while speaking. As a result, the synthesis unit reproduces the voice and facial expression of the deceased with high accuracy, and can provide the user with an experience as if the deceased is actually speaking.

[0074] The response generation unit generates a response to a user's question. For example, the response generation unit uses natural language generation technology to generate an appropriate response to the user's question. The response generation unit uses generation AI to generate a response that sounds as if the deceased were answering the user's question. Specifically, the response generation unit analyzes the user's question and generates an appropriate response based on the content. For example, when the user asks, "How was today?", the response generation unit generates a response such as "It was a very good day today" while reproducing the tone of voice and speaking style of the deceased. The response generation unit can learn the past statements and behavior patterns of the deceased and generate a response based on them. For example, by incorporating words and phrases that the deceased often used, a more natural response is generated. In addition, the response generation unit can adjust the content of the response according to the user's emotions and situation. For example, if the user is sad, the response generation unit selects words of comfort and responds. In this way, the response generation unit can provide the user with an experience as if the deceased was actually speaking to them, and support the user's mental care.

[0075] The palliative care system includes a storage unit that stores the characteristics of the deceased. The storage unit stores the characteristics of the deceased. For example, the storage unit stores the tone of voice, manner of speech, facial expressions, and the like of the deceased. The storage unit can store the characteristics of the deceased using AI. For example, the storage unit can use a voice analysis algorithm to store the tone of voice and manner of speech of the deceased. In addition, the storage unit can use an image analysis technique to store the facial expressions of the deceased. In this way, the storage unit can realize a more natural dialogue by storing the characteristics of the deceased.

[0076] The palliative care system includes a reception unit that receives a user's question. The reception unit receives the user's question. For example, the reception unit can receive voice input and text input. The reception unit can receive the user's question using AI. For example, the reception unit can convert the user's voice input into text using voice recognition technology. In addition, the reception unit can analyze the user's text input using natural language processing technology. In this way, the reception unit can smoothly start a dialogue by receiving the user's question.

[0077] The palliative care system includes a display unit that displays the response. The display unit displays the response. For example, the display unit can display the response on a screen. The display unit enables the user to visually confirm the dialogue. For example, the display unit can adjust the screen layout and font size to display the response so that it is easy for the user to see. In this way, the display unit can enable the user to visually confirm the dialogue by displaying the response.

[0078] The analysis unit can analyze the tone of voice or speaking style, and facial expressions of the deceased. The analysis unit analyzes the tone of voice, speaking style, and facial expressions of the deceased. For example, the analysis unit can analyze the tone of voice and speaking style of the deceased by performing voice waveform analysis and frequency analysis. In addition, the analysis unit can analyze the facial expressions of the deceased by using face recognition technology and facial expression analysis algorithms. In this way, the analysis unit can realize a more realistic dialogue by analyzing the tone of voice, speaking style, and facial expressions of the deceased.

[0079] The synthesis unit can reproduce the tone of voice and the manner of speaking of the deceased. The synthesis unit reproduces the tone of voice and the manner of speaking of the deceased. For example, the synthesis unit can reproduce the voice of the deceased using a voice synthesis technology. The synthesis unit can reproduce the tone of voice and the manner of speaking of the deceased using a voice filtering technology. In this way, the synthesis unit can provide an experience as if the user were conversing with the deceased by reproducing the tone of voice and the manner of speaking of the deceased.

[0080] The capture unit can estimate the user's emotion and adjust the capture timing of the photo and voice data of the deceased person based on the estimated emotion of the user. For example, when the user is sad, the capture unit can delay the capture timing and wait until the user calms down. Also, when the user is relaxed, the capture unit can immediately start the capture. Furthermore, when the user is in a hurry, the capture unit can quickly perform the capture. In this way, the capture unit can reduce the burden on the user by adjusting the capture timing 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 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.

[0081] The import unit can analyze the user's past import history and select an appropriate import method when importing photos and audio data of the deceased. The import unit can suggest an optimal import method based on, for example, the format of data imported by the user in the past. The import unit can also adjust the import speed based on the amount of data imported by the user in the past. Furthermore, the import unit can select an import target based on the type of data imported by the user in the past. In this way, the import unit can provide an optimal import method by analyzing the past import history.

[0082] The capture unit can filter the photographs and audio data of the deceased based on the user's current living situation and areas of interest when capturing the photographs and audio data of the deceased. For example, when the user is busy, the capture unit can preferentially capture only important data. Also, when the user has a specific area of ​​interest, the capture unit can preferentially capture data related to that area. Furthermore, the capture unit can adjust the amount of captured data according to the user's living situation. In this way, the capture unit can provide useful information to the user by capturing data according to the user's living situation and areas of interest.

[0083] The import unit can estimate the user's emotion and determine the priority of the data to be imported based on the estimated user's emotion. For example, when the user is sad, the import unit can preferentially import data that eases the emotion. Moreover, when the user is relaxed, the import unit can import all data equally. Furthermore, when the user is excited, the import unit can preferentially import data that attracts the user's interest. In this way, the import unit can provide optimal data for the user by determining the priority of data according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] When capturing photographs and audio data of the deceased, the capturing unit can preferentially capture highly relevant data in consideration of the geographical location information of the user. For example, when the user is in the hometown of the deceased, the capturing unit can preferentially capture data related to that place. Also, when the user is traveling, the capturing unit can preferentially capture data related to the travel destination. Furthermore, when the user frequently visits a particular place, the capturing unit can preferentially capture data related to that place. In this way, the capturing unit can provide highly relevant data based on the geographical location information of the user.

[0085] The capture unit can analyze the user's social media activity and capture related data when capturing the photo and audio data of the deceased. For example, the capture unit can capture photo and audio data shared by the user on social media. The capture unit can also capture data related to the deceased mentioned by the user on social media. Furthermore, the capture unit can capture data related to the deceased shared by the user's social media friends. In this way, the capture unit can provide related data based on the user's social media activity.

[0086] The analysis unit can estimate the user's emotion and adjust the analysis method of the deceased's features based on the estimated user's emotion. For example, when the user is sad, the analysis unit can prioritize analyzing features that soothe the emotion. Also, when the user is relaxed, the analysis unit can analyze all features equally. Furthermore, when the user is excited, the analysis unit can prioritize analyzing features that attract interest. This allows the analysis unit to provide more appropriate analysis results by adjusting the analysis method 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When analyzing the photo or voice data of the deceased, the analysis unit can improve the accuracy of the analysis by referring to the past behavioral history of the deceased. The analysis unit can improve the analysis accuracy of the voice data, for example, based on the past speech history of the deceased. In addition, the analysis unit can improve the analysis accuracy of the photo data based on the past photo history of the deceased. Furthermore, the analysis unit can optimize the analysis algorithm based on the past behavioral patterns of the deceased. This allows the analysis unit to improve the accuracy of the analysis by referring to the past behavioral history of the deceased.

[0088] When analyzing the photo and audio data of the deceased, the analysis unit can apply different analysis algorithms based on the deceased's field of interest. For example, if the deceased was interested in music, the analysis unit can preferentially analyze music-related data. Furthermore, if the deceased was interested in sports, the analysis unit can preferentially analyze sports-related data. Furthermore, if the deceased was interested in literature, the analysis unit can preferentially analyze literature-related data. This allows the analysis unit to provide more appropriate analysis results by applying analysis algorithms based on the deceased's field of interest.

[0089] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is sad, the analysis unit can provide a display method that alleviates the emotion. Furthermore, when the user is relaxed, the analysis unit can display detailed analysis results. Furthermore, when the user is excited, the analysis unit can provide a visually stimulating display method. Thereby, the analysis unit can provide a more appropriate display by adjusting the display method of the analysis result 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.

[0090] The analysis unit can take into consideration the geographical background of the deceased when analyzing the photo and voice data of the deceased. For example, if the deceased lived in a specific area, the analysis unit can prioritize analyzing data related to that area. In addition, if the deceased liked to travel, the analysis unit can prioritize analyzing data related to travel destinations. Furthermore, if the deceased frequently visited a specific place, the analysis unit can prioritize analyzing data related to that place. In this way, the analysis unit can provide more appropriate analysis results by taking into consideration the geographical background of the deceased.

[0091] The analysis unit can improve the accuracy of the analysis when analyzing the photo or voice data of the deceased by referring to related literature and materials. The analysis unit can improve the analysis accuracy of the voice data by referring to literature written by the deceased, for example. The analysis unit can also improve the analysis accuracy of the photo data by referring to materials of a project in which the deceased was involved. Furthermore, the analysis unit can optimize the analysis algorithm by referring to records of events in which the deceased participated. This allows the analysis unit to improve the accuracy of the analysis by referring to related literature and materials.

[0092] The synthesis unit can estimate the user's emotion and adjust the synthesis method of the voice and facial expression of the deceased person based on the estimated user's emotion. For example, when the user is sad, the synthesis unit can synthesize a voice and facial expression that soothes the emotion. Furthermore, when the user is relaxed, the synthesis unit can synthesize a natural voice and facial expression. Furthermore, when the user is excited, the synthesis unit can synthesize a visually stimulating voice and facial expression. This allows the synthesis unit to provide a more appropriate synthesis result by adjusting the synthesis 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 (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The synthesis unit can improve the accuracy of synthesis by referring to the past voice data of the deceased when synthesizing the voice and facial expression of the deceased. The synthesis unit can improve the synthesis accuracy of the voice data based on, for example, the past utterances of the deceased. Furthermore, the synthesis unit can improve the synthesis accuracy of the facial expression data based on the past photos of the deceased. Furthermore, the synthesis unit can optimize the synthesis algorithm based on the past behavior patterns of the deceased. In this way, the synthesis unit can improve the accuracy of synthesis by referring to the past voice data of the deceased.

[0094] The synthesis unit can apply different synthesis algorithms based on the area of ​​interest of the deceased when synthesizing the voice and facial expression of the deceased. For example, if the deceased was interested in music, the synthesis unit can synthesize a music-related voice and facial expression. Also, if the deceased was interested in sports, the synthesis unit can synthesize a sports-related voice and facial expression. Furthermore, if the deceased was interested in literature, the synthesis unit can synthesize a literature-related voice and facial expression. In this way, the synthesis unit can provide a more appropriate synthesis result by applying a synthesis algorithm based on the area of ​​interest of the deceased.

[0095] The synthesis unit can estimate the user's emotion and adjust the display method of the synthesis result based on the estimated user's emotion. For example, when the user is sad, the synthesis unit can provide a display method that alleviates the emotion. Also, when the user is relaxed, the synthesis unit can display a detailed synthesis result. Furthermore, when the user is excited, the synthesis unit can provide a visually stimulating display method. Thereby, the synthesis unit can provide a more appropriate display by adjusting the display method of the synthesis result 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.

[0096] The synthesis unit can synthesize the voice and facial expression of the deceased while taking into consideration the geographical background of the deceased. For example, if the deceased lived in a specific area, the synthesis unit can synthesize a voice and facial expression related to that area. In addition, if the deceased liked to travel, the synthesis unit can synthesize a voice and facial expression related to the travel destination. Furthermore, if the deceased frequently visited a specific place, the synthesis unit can synthesize a voice and facial expression related to that place. In this way, the synthesis unit can provide a more appropriate synthesis result by taking into consideration the geographical background of the deceased.

[0097] The synthesis unit can improve the accuracy of synthesis by referring to related literature and materials when synthesizing the voice and facial expressions of the deceased. The synthesis unit can improve the synthesis accuracy of the voice data by referring to literature written by the deceased, for example. The synthesis unit can also improve the synthesis accuracy of the facial expression data by referring to materials of a project in which the deceased was involved. Furthermore, the synthesis unit can optimize the synthesis algorithm by referring to records of events in which the deceased participated. This allows the synthesis unit to improve the accuracy of synthesis by referring to related literature and materials.

[0098] The response generation unit can estimate the user's emotion and adjust the response generation method based on the estimated user's emotion. For example, when the user is sad, the response generation unit can generate a comforting response. Also, when the user is relaxed, the response generation unit can generate a response that continues a natural conversation. Furthermore, when the user is excited, the response generation unit can generate an interesting response. Thereby, the response generation unit can provide a more appropriate response by adjusting the response generation method 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.

[0099] The response generation unit can improve the accuracy of the response when generating a response by referring to the past utterance history of the deceased. The response generation unit can generate a natural response, for example, based on the past utterances of the deceased. Furthermore, the response generation unit can maintain the consistency of the response based on the past conversation patterns of the deceased. Furthermore, the response generation unit can generate an appropriate response based on the content of the past utterances of the deceased. Thereby, the response generation unit can improve the accuracy of the response by referring to the past utterance history of the deceased.

[0100] The response generation unit can apply different response generation algorithms based on the deceased's field of interest when generating a response. For example, if the deceased was interested in music, the response generation unit can generate a response related to music. If the deceased was interested in sports, the response generation unit can generate a response related to sports. If the deceased was interested in literature, the response generation unit can generate a response related to literature. This allows the response generation unit to provide a more appropriate response by applying a response generation algorithm based on the deceased's field of interest.

[0101] The response generation unit can estimate the user's emotion and adjust the display method of the response based on the estimated user's emotion. For example, when the user is sad, the response generation unit can provide a display method that alleviates the emotion. Moreover, when the user is relaxed, the response generation unit can display a detailed response. Furthermore, when the user is excited, the response generation unit can provide a visually stimulating display method. Thereby, the response generation unit can provide a more appropriate display by adjusting the display method of the response 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.

[0102] The response generation unit can generate a response taking into consideration the geographical background of the deceased when generating a response. For example, if the deceased lived in a specific area, the response generation unit can generate a response related to the area. In addition, if the deceased liked to travel, the response generation unit can generate a response related to a travel destination. Furthermore, if the deceased frequently visited a specific place, the response generation unit can generate a response related to the place. In this way, the response generation unit can provide a more appropriate response by taking into consideration the geographical background of the deceased.

[0103] The response generation unit can improve the accuracy of the response by referring to related literature and materials when generating a response. The response generation unit can improve the accuracy of the response by referring to literature written by the deceased, for example. The response generation unit can also maintain the consistency of the response by referring to materials of projects in which the deceased was involved. Furthermore, the response generation unit can generate an appropriate response by referring to records of events in which the deceased participated. This allows the response generation unit to improve the accuracy of the response by referring to related literature and materials.

[0104] The storage unit can estimate the user's emotion and select stored data based on the estimated user's emotion. For example, when the user is sad, the storage unit can preferentially store data that soothes the emotion. Also, when the user is relaxed, the storage unit can store all data equally. Furthermore, when the user is excited, the storage unit can preferentially store data that attracts the user's interest. In this way, the storage unit can store more appropriate data by selecting stored data 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.

[0105] The memory unit can appropriately adjust the memory algorithm by referring to the past behavior history of the deceased when storing. The memory unit can optimize the memory algorithm based on, for example, the past speech history of the deceased. The memory unit can also optimize the memory algorithm based on the past photo history of the deceased. Furthermore, the memory unit can optimize the memory algorithm based on the past behavior patterns of the deceased. In this way, the memory unit can optimize the memory algorithm by referring to the past behavior history of the deceased.

[0106] The memory unit can estimate the user's emotion and adjust the frequency of memory based on the estimated user's emotion. For example, when the user is sad, the memory unit can update the memory data frequently. Also, when the user is relaxed, the memory unit can update the memory data at a moderate frequency. Furthermore, when the user is excited, the memory unit can update the memory data as necessary. Thereby, the memory unit can store data at a more appropriate frequency by adjusting the frequency of memory 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.

[0107] The storage unit can weight the stored data based on the area of ​​interest of the deceased when storing. For example, if the deceased was interested in music, the storage unit can store the data with emphasis on music-related data. Furthermore, if the deceased was interested in sports, the storage unit can store the data with emphasis on sports-related data. Furthermore, if the deceased was interested in literature, the storage unit can store the data with emphasis on literature-related data. In this way, the storage unit can store more appropriate data by weighting the stored data based on the area of ​​interest of the deceased.

[0108] The reception unit can estimate the user's emotion and adjust the method of receiving the question based on the estimated user's emotion. For example, when the user is sad, the reception unit can provide a simple interface and minimize input procedures. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly receive the question. Thereby, the reception unit can provide a more appropriate reception method by adjusting the method of receiving the question 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 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.

[0109] When receiving a question, the reception unit can select the optimal reception method by referring to the user's past question history. For example, the reception unit can preferentially suggest a question method that the user has used frequently in the past. The reception unit can also suggest a related question method based on the content of a question used by the user in the past. Furthermore, the reception unit can predict and suggest a question method to be used in a specific time period from the user's past question history. In this way, the reception unit can provide the optimal reception method by referring to the user's past question history.

[0110] The reception unit can estimate the user's emotion and determine the priority of questions based on the estimated user's emotion. For example, when the user is sad, the reception unit can preferentially receive questions that ease the emotion. Furthermore, when the user is relaxed, the reception unit can equally receive all questions. Furthermore, when the user is excited, the reception unit can preferentially receive questions that attract the user's interest. In this way, the reception unit can receive more appropriate questions by determining the priority of questions 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.

[0111] The reception unit can select the optimal reception method in consideration of the device information of the user when receiving the inquiry. For example, when the user is using a smartphone, the reception unit can preferentially suggest voice input. Furthermore, when the user is using a tablet, the reception unit can preferentially suggest touch input. Furthermore, when the user is using a desktop, the reception unit can preferentially suggest keyboard input. This allows the reception unit to provide the optimal reception method based on the device information of the user.

[0112] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, when the user is sad, the display unit can provide a display method that alleviates the emotion. Also, when the user is relaxed, the display unit can provide a detailed display. Furthermore, when the user is excited, the display unit can provide a visually stimulating display method. Thereby, the display unit can provide a more appropriate display by adjusting the display method 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.

[0113] When displaying, the display unit can select the optimal display method by referring to the user's past operation history. The display unit can, for example, suggest the optimal display method based on the display method used by the user in the past. The display unit can also predict and suggest the display method to be used in a specific time period from the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and suggest the most efficient display method. In this way, the display unit can provide the optimal display method by referring to the user's past operation history.

[0114] The display unit can estimate the user's emotion and determine the priority of the display contents based on the estimated user's emotion. For example, when the user is sad, the display unit can preferentially display content that eases the emotion. Also, when the user is relaxed, the display unit can display all the contents evenly. Furthermore, when the user is excited, the display unit can preferentially display interesting content. In this way, the display unit can display more appropriate content by determining the priority of the display contents 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.

[0115] The display unit can select the optimal display method in consideration of the user's device information at the time of display. For example, when the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, when the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. Furthermore, when the user is using a desktop, the display unit can provide a detailed display method. This allows the display unit to provide the optimal display method based on the user's device information.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The palliative care system can estimate the user's emotions and adjust the method of capturing photos and audio data of the deceased based on the estimated emotions. For example, if the user is sad, the system can delay the capture and wait until the user calms down. If the user is relaxed, the system can start the capture immediately. Furthermore, if the user is in a hurry, the system can quickly capture. This makes it possible to provide a flexible capture method according to the user's emotions.

[0118] The palliative care system can estimate the user's emotions and adjust the analysis method of the deceased's features based on the estimated emotions. For example, if the user is sad, features that soothe the emotions can be analyzed with priority. If the user is relaxed, all features can be analyzed equally. If the user is excited, features that attract interest can be analyzed with priority. This makes it possible to provide an analysis method that corresponds to the user's emotions.

[0119] The palliative care system can estimate the user's emotions and adjust the synthesis method of the voice and facial expression of the deceased based on the estimated emotions. For example, if the user is sad, a voice and facial expression that soothes the emotion can be synthesized. If the user is relaxed, a natural voice and facial expression can be synthesized. Furthermore, if the user is excited, a visually stimulating voice and facial expression can be synthesized. This makes it possible to provide a synthesis method according to the user's emotions.

[0120] The palliative care system can estimate the user's emotion and adjust the response generation method based on the estimated emotion. For example, if the user is sad, a comforting response can be generated. If the user is relaxed, a response that continues a natural conversation can be generated. If the user is excited, an interesting response can be generated. In this way, a response according to the user's emotion can be provided.

[0121] The palliative care system can estimate the user's emotion and adjust the display method based on the estimated emotion. For example, if the user is sad, a display method that soothes the emotion can be provided. If the user is relaxed, a detailed display can be provided. If the user is excited, a visually stimulating display method can be provided. In this way, a display method according to the user's emotion can be provided.

[0122] When importing photographs or audio data of the deceased, the palliative care system can analyze the user's past import history and select an appropriate import method. For example, the system can suggest an optimal import method based on the format of data imported by the user in the past. The system can also adjust the import speed based on the amount of data imported by the user in the past. Furthermore, the system can select the data to be imported based on the type of data imported by the user in the past. In this way, the system can provide an optimal import method by analyzing the past import history.

[0123] When importing photographs and audio data of the deceased, the palliative care system can filter the data based on the user's current living situation and areas of interest. For example, if the user is busy, only important data can be imported preferentially. Also, if the user has a specific area of ​​interest, data related to that area can be imported preferentially. Furthermore, the amount of imported data can be adjusted according to the user's living situation. This makes it possible to provide useful information to the user by importing data according to the user's living situation and areas of interest.

[0124] When analyzing the photo and voice data of the deceased, the palliative care system can improve the accuracy of the analysis by referring to the past behavioral history of the deceased. For example, the accuracy of the analysis of voice data can be improved based on the past speech history of the deceased. Also, the accuracy of the analysis of photo data can be improved based on the past photo history of the deceased. Furthermore, the analysis algorithm can be optimized based on the past behavioral patterns of the deceased. In this way, the accuracy of the analysis can be improved by referring to the past behavioral history of the deceased.

[0125] When analyzing the photo and audio data of the deceased, the palliative care system can apply different analysis algorithms based on the deceased's areas of interest. For example, if the deceased was interested in music, music-related data can be preferentially analyzed. Also, if the deceased was interested in sports, sports-related data can be preferentially analyzed. Furthermore, if the deceased was interested in literature, literature-related data can be preferentially analyzed. In this way, by applying analysis algorithms based on the deceased's areas of interest, more appropriate analysis results can be provided.

[0126] When analyzing the photographs and audio data of the deceased, the palliative care system can improve the accuracy of the analysis by referring to related literature and materials. For example, the accuracy of the analysis of the audio data can be improved by referring to literature written by the deceased. In addition, the accuracy of the analysis of the photo data can be improved by referring to materials of projects in which the deceased was involved. Furthermore, the analysis algorithm can be optimized by referring to records of events in which the deceased participated. In this way, the accuracy of the analysis can be improved by referring to related literature and materials.

[0127] The process flow of the second embodiment will be briefly described below.

[0128] Step 1: The import unit imports a photo or audio data of the deceased. For example, the import unit inputs a photo or audio data of the deceased provided by a user into the system. The import unit can import image files in JPEG or PNG format, and audio files in MP3 or WAV format. Step 2: The analysis unit analyzes the data captured by the capture unit. For example, the analysis unit analyzes the tone of voice, speaking style, facial expressions, etc. of the deceased. The analysis unit uses a voice analysis algorithm to extract characteristics of the deceased's voice. For example, it performs voice waveform analysis and frequency analysis to analyze the tone of voice and speaking style of the deceased. It also uses image analysis technology to analyze the facial expressions of the deceased. For example, it uses face recognition technology and facial expression analysis algorithms to analyze the facial expressions of the deceased. Step 3: The synthesis unit performs synthesis based on the data analyzed by the analysis unit. For example, the synthesis unit reproduces the tone of voice and manner of speaking of the deceased. The synthesis unit reproduces the voice of the deceased using voice synthesis technology. For example, the synthesis unit reproduces the tone of voice and manner of speaking of the deceased using voice filtering technology. The synthesis unit also reproduces the facial expressions of the deceased using image synthesis technology. For example, the synthesis unit reproduces the facial expressions of the deceased using image filtering technology. Step 4: The response generation unit generates a response to the user's question. For example, natural language generation technology is used to generate an appropriate response to the user's question. A generation AI is used to generate a response that sounds like the deceased would be answering the user's question. For example, if the user asks, "How was today?", a response is generated that sounds like the deceased would be saying, "Today was a very good day."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements including the above-mentioned import unit, analysis unit, synthesis unit, response generation unit, storage unit, reception unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the import unit is realized by the control unit 46A of the smart device 14, and inputs the photo and voice data of the deceased provided by the user into the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the tone of voice, speech, facial expression, etc. of the deceased. The synthesis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reproduces the tone of voice and speech of the deceased. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response as if the deceased were answering a question from the user. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the characteristics of the deceased. The reception unit is realized, for example, by the control unit 46A of the smart device 14, and receives a question from the user. The display unit is realized by, for example, the control unit 46A of the smart device 14, and displays the response on the screen. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements including the above-mentioned import unit, analysis unit, synthesis unit, response generation unit, storage unit, reception unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the import unit is realized by the control unit 46A of the smart glasses 214, and inputs the photo and voice data of the deceased provided by the user to the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the tone of voice, speaking style, facial expression, etc. of the deceased. The synthesis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reproduces the tone of voice and speaking style of the deceased. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response as if the deceased were answering a question from the user. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the characteristics of the deceased. The reception unit is realized, for example, by the control unit 46A of the smart glasses 214, and accepts a question from the user. The display unit is realized by, for example, the control unit 46A of the smart glasses 214, and displays the response on the screen. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements including the above-mentioned import unit, analysis unit, synthesis unit, response generation unit, storage unit, reception unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the import unit is realized by the control unit 46A of the headset type terminal 314, and inputs the photo and voice data of the deceased provided by the user into the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the tone of voice, speech style, facial expression, etc. of the deceased. The synthesis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reproduces the tone of voice and speech style of the deceased. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response as if the deceased were answering a question from the user. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the characteristics of the deceased. The reception unit is realized, for example, by the control unit 46A of the headset type terminal 314, and receives a question from the user. The display unit is realized by, for example, the control unit 46A of the headset type terminal 314, and displays the response on the screen. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements including the above-mentioned import unit, analysis unit, synthesis unit, response generation unit, storage unit, reception unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the import unit is realized by the control unit 46A of the robot 414, and inputs the photograph and voice data of the deceased provided by the user into the system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the tone of voice, speech, facial expression, etc. of the deceased. The synthesis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and reproduces the tone of voice and speech of the deceased. The response generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a response as if the deceased were answering a question from the user. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the characteristics of the deceased. The reception unit is realized, for example, by the control unit 46A of the robot 414, and receives a question from the user. The display unit is realized by, for example, the control unit 46A of the robot 414, and displays the response on a screen. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Appendix 1) An import unit that imports photographs or audio data of the deceased; an analysis unit that analyzes the data captured by the capture unit; A synthesis unit that performs synthesis based on the data analyzed by the analysis unit; A response generating unit that generates a response to a user's question. A system characterized by: (Appendix 2) Equipped with a memory unit that memorizes the characteristics of the deceased 2. The system of claim 1, (Appendix 3) Equipped with a reception unit that accepts inquiries from users 2. The system of claim 1, (Appendix 4) Equipped with a display that displays the response 2. The system of claim 1, (Appendix 5) The analysis unit is Analyzing the tone of the deceased's voice, speech pattern, and facial expressions 2. The system of claim 1, (Appendix 6) The synthesis unit is Recreate the tone and manner of speech of the deceased 2. The system of claim 1, (Appendix 7) The capture unit is The system estimates the user's emotions and adjusts the timing of capturing photos and audio data of the deceased person based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The capture unit is When importing photos and audio data of the deceased, the system analyzes the user's past import history and selects the appropriate import method. 2. The system of claim 1, (Appendix 9) The capture unit is When capturing photos and audio of the deceased, filtering is performed based on the user's current living situation and areas of interest. 2. The system of claim 1, (Appendix 10) The capture unit is Infer user sentiment and prioritize data to be ingested based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The capture unit is When importing photos and audio data of the deceased, the system takes into account the user's geographic location information to prioritize importing the most relevant data. 2. The system of claim 1, (Appendix 12) The capture unit is When capturing photos and audio of the deceased, the system analyzes the user's social media activity and captures related data. 2. The system of claim 1, (Appendix 13) The analysis unit is The method estimates the user's emotions and adjusts the analysis method of the characteristics of the deceased person based on the estimated user's emotions. 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing photos and audio data of the deceased, the accuracy of the analysis can be improved by referring to the deceased's past behavioral history. 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing the photos and audio data of the deceased, different analysis algorithms are applied based on the deceased's areas of interest. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is When analyzing photographs and audio data of the deceased, the geographical background of the deceased is taken into account. 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing photographs and audio data of the deceased, refer to related literature and materials to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 19) The synthesis unit is The system estimates the user's emotions and adjusts the synthesis method of the deceased's voice and facial expressions based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The synthesis unit is When synthesizing the voice and facial expressions of the deceased, the accuracy of the synthesis is improved by referencing the deceased's past voice data. 2. The system of claim 1, (Appendix 21) The synthesis unit is When synthesizing the voice and facial expressions of the deceased, different synthesis algorithms are applied based on the deceased's areas of interest. 2. The system of claim 1, (Appendix 22) The synthesis unit is Estimate the user's emotions and adjust the way the synthesis results are displayed based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The synthesis unit is When synthesizing the voice and facial expressions of the deceased, the geographical background of the deceased is taken into consideration. 2. The system of claim 1, (Appendix 24) The synthesis unit is When synthesizing the voices and facial expressions of the deceased, we will refer to relevant literature and materials to improve the accuracy of the synthesis. 2. The system of claim 1, (Appendix 25) The response generation unit: Inferring a user's emotions and adjusting the way responses are generated based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The response generation unit: When generating a response, the accuracy of the response is improved by referring to the deceased person's past speech history. 2. The system of claim 1, (Appendix 27) The response generation unit: When generating responses, different response generation algorithms are applied based on the deceased person's areas of interest. 2. The system of claim 1, (Appendix 28) The response generation unit: Inferring a user's emotions and adjusting the way responses are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 29) The response generation unit: When generating a response, the geographical background of the deceased is taken into account 2. The system of claim 1, (Appendix 30) The response generation unit: When generating responses, refer to relevant literature and materials to improve the accuracy of the response 2. The system of claim 1, (Appendix 31) The storage unit is A user's emotion is estimated, and memory data is selected based on the estimated user's emotion. 3. The system of claim 2, (Appendix 32) The storage unit is When memorizing, the memory algorithm is adjusted appropriately by referring to the deceased person's past behavioral history. 3. The system of claim 2, (Appendix 33) The storage unit is Inferring a user's emotions and adjusting the frequency of memories based on the estimated user's emotions 3. The system of claim 2, (Appendix 34) The storage unit is During the memorial process, the memorial data is weighted based on the deceased's areas of interest. 3. The system of claim 2, (Appendix 35) The reception unit is Estimate the user's emotions and adjust the way questions are received based on the estimated user's emotions 4. The system of claim 3. (Appendix 36) The reception unit is When accepting an inquiry, the optimal acceptance method is selected by referring to the user's past inquiry history. 4. The system of claim 3. (Appendix 37) The reception unit is Estimate the user's emotions and prioritize questions based on the estimated user emotions. 4. The system of claim 3. (Appendix 38) The reception unit is When accepting an inquiry, the optimal acceptance method is selected taking into account the user's device information. 4. The system of claim 3. (Appendix 39) The display unit is Estimate user's emotions and adjust the display method based on the estimated user's emotions 5. The system of claim 4. (Appendix 40) The display unit is When displaying, the optimal display method is selected by referring to the user's past operation history. 5. The system of claim 4. (Appendix 41) The display unit is Estimate the user's emotions and prioritize the content to be displayed based on the estimated user's emotions. 5. The system of claim 4. (Appendix 42) The display unit is When displaying, the optimal display method is selected taking into account the user's device information. 5. The system of claim 4. [Explanation of symbols]

[0201] 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. An import unit that imports photographs or audio data of the deceased; an analysis unit that analyzes the data captured by the capture unit; A synthesis unit that performs synthesis based on the data analyzed by the analysis unit; A response generating unit that generates a response to a user's question. A system characterized by:

2. Equipped with a memory unit that memorizes the characteristics of the deceased 2. The system of claim 1 .

3. Equipped with a reception unit that accepts inquiries from users 2. The system of claim 1 .

4. Equipped with a display that displays the response 2. The system of claim 1 .

5. The analysis unit is Analyzing the tone of the deceased's voice, speech pattern, and facial expressions 2. The system of claim 1 .

6. The synthesis unit is Recreate the tone and manner of speech of the deceased 2. The system of claim 1 .

7. The capture unit is The system estimates the user's emotions and adjusts the timing of capturing photos and audio data of the deceased person based on the estimated user's emotions.

2. The system of claim 1 .

8. The capture unit is When importing photos and audio data of the deceased, the system analyzes the user's past import history and selects the appropriate import method.

2. The system of claim 1 .

9. The capture unit is When capturing photos and audio of the deceased, filtering is performed based on the user's current living situation and areas of interest.

2. The system of claim 1 .

10. The capture unit is Infer user sentiment and prioritize data to be ingested based on the inferred user sentiment 2. The system of claim 1 .

11. The capture unit is When importing photos and audio data of the deceased, the system takes into account the user's geographic location to prioritize importing the most relevant data.

2. The system of claim 1 .

Images