System

Abstract

An object of the system according to the embodiment is to enable the bereaved family to talk with the deceased by utilizing the data of the deceased.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, a voice generation unit, and a dialogue model learning unit. The data collection unit collects data of the deceased. The analysis part analyzes the data of the deceased collected by the data collection part. The voice generation part generates voice data of the deceased based on the data analyzed by the analysis part. The dialogue model learning unit learns a dialogue model based on the voice data generated by the voice generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately provide means for bereaved family members to communicate with the deceased using their data, and there is room for improvement in alleviating feelings of loneliness.

[0005] The system according to the embodiment aims to enable bereaved family members to communicate with the deceased by utilizing the data of the deceased. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a voice generation unit, and a dialogue model learning unit. The data collection unit collects data on the deceased. The analysis unit analyzes the data on the deceased collected by the data collection unit. The voice generation unit generates voice data of the deceased based on the data analyzed by the analysis unit. The dialogue model learning unit learns a dialogue model based on the voice data generated by the voice generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize data of the deceased to enable the bereaved to communicate with the deceased. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The AI ​​app of the present invention is a system that uses AI to learn data about the deceased and communicate with their bereaved family members. This system utilizes Google Cloud Video AI, OpenAI Voice Engine, and OpenAI Fine-tuning API to analyze and learn from video and audio data of the deceased, providing the bereaved family with the experience of communicating with the deceased. This allows the AI ​​app to reduce the bereaved family's sense of loneliness and help them share memories with the deceased.

[0029] The AI ​​application according to the embodiment includes a data collection unit, an analysis unit, a voice generation unit, and a dialogue model learning unit. The data collection unit collects data on the deceased. For example, it can collect home videos, voice messages, photos, etc. The data collection unit also analyzes the collected data using Google Cloud Video AI to extract the deceased's characteristics, speaking style, facial expressions, etc. For example, it analyzes videos of the deceased spending time with their family and learns the conversations and facial expressions in those videos. The voice generation unit generates voice data of the deceased based on the analyzed data. For example, it uses the OpenAI Voice Engine to reproduce the deceased's voice based on the deceased's speaking style and vocal characteristics. The dialogue model learning unit learns a dialogue model based on the generated voice data. For example, it uses the OpenAI Fine-tuning API to learn the deceased's thoughts and reactions and builds a model suitable for dialogue with bereaved family members. As a result, the AI ​​application according to the embodiment collects and analyzes data on the deceased, generates voice data, and learns a dialogue model to realize dialogue with bereaved family members. For example, if a family member asks, "What was your day like today?", the AI ​​will respond in the voice of the deceased, "Today was a very good day."

[0030] The data collection unit collects not only video or audio data of the deceased, but also at least one text data such as letters or diaries written by the deceased, and the analysis unit can comprehensively analyze them. The data collection unit, for example, collects video and audio data of the deceased, as well as text data such as letters and diaries written by the deceased. This allows for a more detailed understanding of the deceased's overall picture. The analysis unit, for example, comprehensively analyzes the collected video data, audio data, letters, diaries, etc., to extract the characteristics and thoughts of the deceased. This allows for a more detailed understanding of the deceased's overall picture by collecting and comprehensively analyzing text data such as letters and diaries in addition to video and audio data of the deceased.

[0031] When analyzing the data of the deceased, the data collection unit also collects information about the deceased's lifestyle or hobbies, and the analysis unit can create a more detailed profile based on this information. For example, when analyzing the video data or audio data of the deceased, the data collection unit also collects information about the deceased's lifestyle. For example, the data collection unit stores the deceased's daily routines and favorite foods in a database. For example, the analysis unit creates a detailed profile of the deceased based on the collected information about the lifestyle and hobbies. In this way, by collecting information about the deceased's lifestyle and hobbies and creating a more detailed profile, a more complete picture of the deceased can be obtained.

[0032] When collecting data on the deceased, the data collection unit also collects interview footage from family or friends, and the analysis unit can analyze the deceased's relationships based on the footage. For example, the data collection unit also collects interview footage from family and friends when collecting data on the deceased. For example, footage of family and friends talking about the deceased is stored in a database. For example, the analysis unit analyzes the deceased's relationships based on the collected interview footage. In this way, by collecting interview footage from family and friends and analyzing the deceased's relationships, a more detailed overall picture of the deceased can be obtained.

[0033] When collecting data about the deceased, the data collection unit may include geographic information of places visited by the deceased, and the analysis unit may use this information to enable a conversation about memorable places. For example, the data collection unit may also collect geographic information of places visited by the deceased when collecting data about the deceased. For example, information about places where the deceased traveled and lived may be stored in a database. The analysis unit may, for example, enable a conversation about memorable places based on the collected geographic information. In this way, by collecting geographic information of places visited by the deceased and enabling a conversation about memorable places, memories of the deceased may be felt more deeply.

[0034] The voice generation unit can add background sounds and environmental sounds to the voice data of the deceased to provide a more realistic dialogue experience. The voice generation unit can, for example, add background sounds to the voice data of the deceased to provide a more realistic dialogue experience. For example, it can reproduce the environmental sounds of the location where the deceased is speaking. In this way, adding background sounds and environmental sounds to the voice data of the deceased provides a more realistic dialogue experience.

[0035] The voice generation unit can reproduce voice changes according to the specific health condition or age of the deceased when generating voice data of the deceased. For example, the voice generation unit reproduces voice changes according to the specific health condition of the deceased when generating voice data of the deceased. For example, it reproduces the voice of the deceased when sick and the voice of the deceased when healthy. This provides a more realistic dialogue experience by reproducing voice changes according to the specific health condition or age of the deceased.

[0036] The voice generation unit can support different languages ​​or dialects when generating voice data of the deceased, enabling international use. For example, the voice generation unit creates a system that supports different languages ​​when generating voice data of the deceased. For example, it generates voices of the deceased speaking in English or French. This supports different languages ​​and dialects, enabling international use.

[0037] When generating the voice data of the deceased, the voice generation unit can also reproduce the songs or poetry sung by the deceased, incorporating musical elements. For example, when generating the voice data of the deceased, the voice generation unit reproduces the songs sung by the deceased. For example, songs that the deceased liked or often sang are generated. In this way, by reproducing the songs and poetry sung by the deceased, an interactive experience incorporating musical elements is provided.

[0038] The dialogue model learning unit can also use the contents of the deceased's past SNS posts or emails as learning data when learning the dialogue model. For example, the dialogue model learning unit uses the deceased's past SNS posts as learning data when learning the dialogue model. For example, it learns what topics the deceased talked about on SNS. In this way, by using the contents of the deceased's past SNS posts and emails as learning data, more detailed and personalized dialogue can be provided.

[0039] The dialogue model learning unit can learn detailed information about specific events or occurrences of the deceased during dialogue model learning, thereby enabling specific dialogue. For example, the dialogue model learning unit learns detailed information about specific events of the deceased during dialogue model learning. For example, it learns detailed information about events or occurrences in which the deceased participated. In this way, learning detailed information about specific events or occurrences of the deceased enables specific dialogue.

[0040] The dialogue model learning unit can learn data of deceased people with different cultural or religious backgrounds in learning the dialogue model, thereby realizing diverse dialogues. For example, the dialogue model learning unit learns data of deceased people with different cultural backgrounds in learning the dialogue model. For example, it learns what kind of dialogues the deceased had in different cultural spheres. In this way, diverse dialogues can be realized by learning data of deceased people with different cultural or religious backgrounds.

[0041] The dialogue model learning unit can also learn information about the deceased's pets or hobbies when learning the dialogue model, thereby providing a more personalized dialogue. The dialogue model learning unit, for example, learns information about the deceased's pets when learning the dialogue model. For example, it learns the names, breeds, and stories about pets that the deceased had. In this way, by learning information about the deceased's pets and hobbies, a more personalized dialogue can be provided.

[0042] The dialogue model learning unit can prioritize dialogues with bereaved family members regarding specific anniversaries or events of the deceased. For example, the dialogue model learning unit prioritizes dialogues with bereaved family members regarding specific anniversaries or events of the deceased. For example, the dialogue model learning unit prioritizes dialogues with bereaved family members regarding the deceased's birthday or wedding anniversary. This prioritizes dialogues with bereaved family members regarding specific anniversaries or events of the deceased, thereby improving the quality of dialogues with bereaved family members.

[0043] The dialogue model learning unit can reproduce voice changes according to the specific health condition or age of the deceased when dialogue is with the bereaved family. For example, the dialogue model learning unit reproduces voice changes according to the specific health condition of the deceased when dialogue is with the bereaved family. For example, it reproduces the voice of the deceased when they were ill and the voice of the deceased when they were healthy. This provides a more realistic dialogue experience by reproducing voice changes according to the specific health condition or age of the deceased.

[0044] The dialogue model learning unit can accommodate different languages ​​or dialects in dialogue with bereaved families, enabling international use. The dialogue model learning unit builds a system that accommodates different languages ​​in dialogue with bereaved families, for example, allowing bereaved families to converse in English or French. This allows for international use by accommodating different languages ​​and dialects.

[0045] The dialogue model learning unit can also reproduce songs or poetry recitations sung by the deceased in dialogue with the bereaved family, incorporating musical elements. For example, the dialogue model learning unit reproduces songs sung by the deceased in dialogue with the bereaved family. For example, it reproduces songs that the deceased liked or often sang. In this way, by reciting songs or poetry recitations sung by the deceased, a dialogue experience incorporating musical elements is provided.

[0046] The dialogue model learning unit can prioritize displaying videos or photos related to specific anniversaries or events of the deceased when sharing memories. For example, the dialogue model learning unit prioritizes displaying videos or photos related to specific anniversaries or events of the deceased when sharing memories. For example, it prioritizes displaying videos or photos of the deceased's birthday or wedding anniversary. This prioritizes displaying videos or photos related to specific anniversaries or events of the deceased, thereby improving the quality of sharing memories with bereaved family members.

[0047] The dialogue model learning unit can recreate videos or photos that correspond to the specific health condition or age of the deceased when sharing memories. For example, the dialogue model learning unit recreates videos or photos that correspond to the specific health condition of the deceased when sharing memories. For example, it recreates videos of the deceased when they were ill and photos of the deceased when they were healthy. This allows for more realistic sharing of memories by recreating videos or photos that correspond to the specific health condition or age of the deceased.

[0048] The dialogue model learning unit can accommodate different languages ​​or dialects when sharing memories, enabling international use. The dialogue model learning unit builds a system that accommodates different languages ​​when sharing memories. For example, memories of a deceased person can be displayed in English and French. This allows for international use by accommodating different languages ​​and dialects.

[0049] The dialogue model learning unit can also reproduce songs or poetry recitations sung by the deceased when sharing memories, incorporating musical elements. For example, the dialogue model learning unit reproduces songs sung by the deceased when sharing memories. For example, it reproduces songs that the deceased liked or often sang. This allows for the sharing of memories that incorporate musical elements by reciting songs or poetry recitations sung by the deceased.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When collecting data on the deceased, the data collection unit also collects the usage history of the deceased's digital devices, which the analysis unit can use to analyze the deceased's digital lifestyle. For example, the data collection unit collects the history of the applications and websites the deceased frequently used to understand the deceased's interests. This allows the analysis of the deceased's digital lifestyle and the creation of a more detailed profile.

[0052] When collecting data on the deceased, the data collection unit also collects information on the deceased's favorite items and collections, which the analysis unit can use to analyze the deceased's hobbies and preferences. For example, by collecting information on the stamps and coins the deceased collected, or the musical instruments they played, the analysis unit can understand the deceased's hobbies and preferences. This allows for the creation of a detailed profile of the deceased's hobbies and preferences.

[0053] When collecting data on the deceased, the data collection unit also collects information on the communities and clubs the deceased participated in, and the analysis unit can use this information to analyze the deceased's social network. For example, information on sports clubs and hobby circles the deceased belonged to can be collected to understand the deceased's social network. This allows for the creation of a detailed profile of the deceased's social network.

[0054] When collecting data on the deceased, the data collection unit also collects information on the books and articles the deceased was reading, and the analysis unit can use this information to analyze the deceased's knowledge and range of interests. For example, by collecting information on the books and magazine articles the deceased was reading, the range of knowledge and interests of the deceased can be grasped. This makes it possible to create a detailed profile of the deceased's knowledge and interests.

[0055] When collecting data on the deceased, the data collection unit also collects information on perfumes and aromas used by the deceased, and the analysis unit can use this information to analyze the deceased's olfactory preferences. For example, by collecting the types of perfumes and aromas that the deceased loved, the analysis unit can understand the deceased's olfactory preferences. This allows for the creation of a detailed profile of the deceased's olfactory preferences.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection unit collects data about the deceased. For example, it can collect home videos, audio messages, and photos. The data collection unit then analyzes the collected data using Google Cloud Video AI to extract the deceased's characteristics, speech patterns, and facial expressions. For example, it can analyze footage of the deceased spending time with their family and learn from their conversations and facial expressions. Step 2: The analysis unit analyzes the data of the deceased collected by the data collection unit, extracting the deceased's characteristics, speech patterns, facial expressions, etc. Step 3: The voice generator generates the deceased's voice data based on the analyzed data. For example, using the OpenAI Voice Engine, AI recreates the deceased's voice based on the deceased's speaking style and vocal characteristics. Step 4: The dialogue model training unit trains a dialogue model based on the generated voice data. For example, it uses the OpenAI Fine-tuning API to learn the thoughts and reactions of the deceased and builds a model suitable for dialogue with bereaved family members.

[0058] (Example 2) The AI ​​app of the present invention is a system that uses AI to learn data about the deceased and communicate with their bereaved family members. This system utilizes Google Cloud Video AI, OpenAI Voice Engine, and OpenAI Fine-tuning API to analyze and learn from video and audio data of the deceased, providing the bereaved family with the experience of communicating with the deceased. This allows the AI ​​app to reduce the bereaved family's sense of loneliness and help them share memories with the deceased.

[0059] The AI ​​application according to the embodiment includes a data collection unit, an analysis unit, a voice generation unit, and a dialogue model learning unit. The data collection unit collects data on the deceased. For example, it can collect home videos, voice messages, photos, etc. The data collection unit also analyzes the collected data using Google Cloud Video AI to extract the deceased's characteristics, speaking style, facial expressions, etc. For example, it analyzes videos of the deceased spending time with their family and learns the conversations and facial expressions in those videos. The voice generation unit generates voice data of the deceased based on the analyzed data. For example, it uses the OpenAI Voice Engine to reproduce the deceased's voice based on the deceased's speaking style and vocal characteristics. The dialogue model learning unit learns a dialogue model based on the generated voice data. For example, it uses the OpenAI Fine-tuning API to learn the deceased's thoughts and reactions and builds a model suitable for dialogue with bereaved family members. As a result, the AI ​​application according to the embodiment collects and analyzes data on the deceased, generates voice data, and learns a dialogue model to realize dialogue with bereaved family members. For example, if a family member asks, "What was your day like today?", the AI ​​will respond in the voice of the deceased, "Today was a very good day."

[0060] When collecting data on the deceased, the data collection unit can use an emotion estimation function to analyze the emotional state of the deceased and filter the data based on the emotion. The data collection unit, for example, collects video data of the deceased and analyzes the emotional state of the deceased using the emotion estimation function. For example, it identifies scenes in which the deceased is laughing or feeling moved, and stores these scenes preferentially in the database. In this way, by analyzing the emotional state of the deceased and filtering the data based on the emotion, data that can be more emotionally empathetic can be collected.

[0061] The data collection unit collects not only video or audio data of the deceased, but also at least one text data such as letters or diaries written by the deceased, and the analysis unit can comprehensively analyze them. The data collection unit, for example, collects video and audio data of the deceased, as well as text data such as letters and diaries written by the deceased. This allows for a more detailed understanding of the deceased's overall picture. The analysis unit, for example, comprehensively analyzes the collected video data, audio data, letters, diaries, etc., to extract the characteristics and thoughts of the deceased. This allows for a more detailed understanding of the deceased's overall picture by collecting and comprehensively analyzing text data such as letters and diaries in addition to video and audio data of the deceased.

[0062] When analyzing the data of the deceased, the data collection unit also collects information about the deceased's lifestyle or hobbies, and the analysis unit can create a more detailed profile based on this information. For example, when analyzing the video data or audio data of the deceased, the data collection unit also collects information about the deceased's lifestyle. For example, the data collection unit stores the deceased's daily routines and favorite foods in a database. For example, the analysis unit creates a detailed profile of the deceased based on the collected information about the lifestyle and hobbies. In this way, by collecting information about the deceased's lifestyle and hobbies and creating a more detailed profile, a more complete picture of the deceased can be obtained.

[0063] When collecting data on the deceased, the data collection unit also collects interview footage from family or friends, and the analysis unit can analyze the deceased's relationships based on the footage. For example, the data collection unit also collects interview footage from family and friends when collecting data on the deceased. For example, footage of family and friends talking about the deceased is stored in a database. For example, the analysis unit analyzes the deceased's relationships based on the collected interview footage. In this way, by collecting interview footage from family and friends and analyzing the deceased's relationships, a more detailed overall picture of the deceased can be obtained.

[0064] When collecting data about the deceased, the data collection unit may include geographic information of places visited by the deceased, and the analysis unit may use this information to enable a conversation about memorable places. For example, the data collection unit may also collect geographic information of places visited by the deceased when collecting data about the deceased. For example, information about places where the deceased traveled and lived may be stored in a database. The analysis unit may, for example, enable a conversation about memorable places based on the collected geographic information. In this way, by collecting geographic information of places visited by the deceased and enabling a conversation about memorable places, memories of the deceased may be felt more deeply.

[0065] When collecting data on the deceased, the data collection unit uses an emotion estimation function to analyze the emotional state of the bereaved family members, and can prioritize collecting data that the bereaved family members can most emotionally empathize with. For example, when collecting data on the deceased, the data collection unit analyzes the emotional state of the bereaved family members. For example, it analyzes the emotional reactions of the bereaved family members when they see or hear video or audio of the deceased, and prioritizes collecting data that they can emotionally empathize with. In this way, by analyzing the emotional state of the bereaved family members and prioritizes collecting data that the bereaved family members can most emotionally empathize with, it becomes possible to collect data that is sensitive to the emotions of the bereaved family members.

[0066] The voice generation unit can use the emotion estimation function to recreate the emotional state of the deceased when generating voice data of the deceased, thereby realizing an emotionally rich dialogue. For example, when generating voice data of the deceased, the voice generation unit uses the emotion estimation function to recreate the emotional state of the deceased. For example, it recreates the voice of the deceased when they are happy or sad. This recreates the emotional state of the deceased and realizes an emotionally rich dialogue, thereby improving the quality of dialogue with the bereaved.

[0067] The voice generation unit can add background sounds and environmental sounds to the voice data of the deceased to provide a more realistic dialogue experience. The voice generation unit can, for example, add background sounds to the voice data of the deceased to provide a more realistic dialogue experience. For example, it can reproduce the environmental sounds of the location where the deceased is speaking. In this way, adding background sounds and environmental sounds to the voice data of the deceased provides a more realistic dialogue experience.

[0068] The voice generation unit can reproduce voice changes according to the specific health condition or age of the deceased when generating voice data of the deceased. For example, the voice generation unit reproduces voice changes according to the specific health condition of the deceased when generating voice data of the deceased. For example, it reproduces the voice of the deceased when sick and the voice of the deceased when healthy. This provides a more realistic dialogue experience by reproducing voice changes according to the specific health condition or age of the deceased.

[0069] The voice generation unit can support different languages ​​or dialects when generating voice data of the deceased, enabling international use. For example, the voice generation unit creates a system that supports different languages ​​when generating voice data of the deceased. For example, it generates voices of the deceased speaking in English or French. This supports different languages ​​and dialects, enabling international use.

[0070] When generating the voice data of the deceased, the voice generation unit can also reproduce the songs or poetry sung by the deceased, incorporating musical elements. For example, when generating the voice data of the deceased, the voice generation unit reproduces the songs sung by the deceased. For example, songs that the deceased liked or often sang are generated. In this way, by reproducing the songs and poetry sung by the deceased, an interactive experience incorporating musical elements is provided.

[0071] The voice generation unit can use the emotion estimation function to generate voice data that the bereaved family can most emotionally empathize with. The voice generation unit, for example, uses the emotion estimation function to generate voice data that the bereaved family can most emotionally empathize with. For example, it generates voice that makes the bereaved family feel moved or happy. This generates voice data that the bereaved family can most emotionally empathize with, thereby improving the quality of the dialogue.

[0072] The dialogue model learning unit can learn the emotional state of the deceased using an emotion estimation function when learning the dialogue model, and realize a dialogue based on emotions. For example, when learning the dialogue model, the dialogue model learning unit performs emotion estimation using video and audio data of the deceased to learn the emotional state of the deceased. For example, it learns dialogue when the deceased was happy and dialogue when the deceased was sad. In this way, by learning the emotional state of the deceased and realizing a dialogue based on emotions, a more natural and emotional dialogue can be provided.

[0073] The dialogue model learning unit can also use the contents of the deceased's past SNS posts or emails as learning data when learning the dialogue model. For example, the dialogue model learning unit uses the deceased's past SNS posts as learning data when learning the dialogue model. For example, it learns what topics the deceased talked about on SNS. In this way, by using the contents of the deceased's past SNS posts and emails as learning data, more detailed and personalized dialogue can be provided.

[0074] The dialogue model learning unit can learn detailed information about specific events or occurrences of the deceased during dialogue model learning, thereby enabling specific dialogue. For example, the dialogue model learning unit learns detailed information about specific events of the deceased during dialogue model learning. For example, it learns detailed information about events or occurrences in which the deceased participated. In this way, learning detailed information about specific events or occurrences of the deceased enables specific dialogue.

[0075] The dialogue model learning unit can learn data of deceased people with different cultural or religious backgrounds in learning the dialogue model, thereby realizing diverse dialogues. For example, the dialogue model learning unit learns data of deceased people with different cultural backgrounds in learning the dialogue model. For example, it learns what kind of dialogues the deceased had in different cultural spheres. In this way, diverse dialogues can be realized by learning data of deceased people with different cultural or religious backgrounds.

[0076] The dialogue model learning unit can also learn information about the deceased's pets or hobbies when learning the dialogue model, thereby providing a more personalized dialogue. The dialogue model learning unit, for example, learns information about the deceased's pets when learning the dialogue model. For example, it learns the names, breeds, and stories about pets that the deceased had. In this way, by learning information about the deceased's pets and hobbies, a more personalized dialogue can be provided.

[0077] The dialogue model learning unit can use the emotion estimation function to learn a dialogue model that the bereaved family can most emotionally empathize with. The dialogue model learning unit, for example, uses the emotion estimation function to learn a dialogue model that the bereaved family can most emotionally empathize with. For example, it learns dialogue that moves or delights the bereaved family. In this way, the quality of the dialogue is improved by learning a dialogue model that the bereaved family can most emotionally empathize with.

[0078] The dialogue model learning unit can use the emotion estimation function to analyze the emotional state of the bereaved family members when dialogue with them and respond according to their emotions. The dialogue model learning unit, for example, uses the emotion estimation function to analyze the emotional state of the bereaved family members when dialogue with them. For example, when the bereaved family members are sad, the dialogue model learning unit responds with words of comfort, and when they are happy, the dialogue model learning unit responds with words of empathy. In this way, by analyzing the emotional state of the bereaved family members and responding according to their emotions, a dialogue that is more emotionally empathetic can be provided.

[0079] The dialogue model learning unit can prioritize dialogues with bereaved family members regarding specific anniversaries or events of the deceased. For example, the dialogue model learning unit prioritizes dialogues with bereaved family members regarding specific anniversaries or events of the deceased. For example, the dialogue model learning unit prioritizes dialogues with bereaved family members regarding the deceased's birthday or wedding anniversary. This prioritizes dialogues with bereaved family members regarding specific anniversaries or events of the deceased, thereby improving the quality of dialogues with bereaved family members.

[0080] The dialogue model learning unit can reproduce voice changes according to the specific health condition or age of the deceased when dialogue is with the bereaved family. For example, the dialogue model learning unit reproduces voice changes according to the specific health condition of the deceased when dialogue is with the bereaved family. For example, it reproduces the voice of the deceased when they were ill and the voice of the deceased when they were healthy. This provides a more realistic dialogue experience by reproducing voice changes according to the specific health condition or age of the deceased.

[0081] The dialogue model learning unit can accommodate different languages ​​or dialects in dialogue with bereaved families, enabling international use. The dialogue model learning unit builds a system that accommodates different languages ​​in dialogue with bereaved families, for example, allowing bereaved families to converse in English or French. This allows for international use by accommodating different languages ​​and dialects.

[0082] The dialogue model learning unit can also reproduce songs or poetry recitations sung by the deceased in dialogue with the bereaved family, incorporating musical elements. For example, the dialogue model learning unit reproduces songs sung by the deceased in dialogue with the bereaved family. For example, it reproduces songs that the deceased liked or often sang. In this way, by reciting songs or poetry recitations sung by the deceased, a dialogue experience incorporating musical elements is provided.

[0083] The dialogue model learning unit can use the emotion estimation function to provide a dialogue that the bereaved family can most emotionally empathize with. The dialogue model learning unit, for example, uses the emotion estimation function to provide a dialogue that the bereaved family can most emotionally empathize with. For example, it provides a dialogue that moves or delights the bereaved family. This improves the quality of the dialogue by providing a dialogue that the bereaved family can most emotionally empathize with.

[0084] The dialogue model learning unit can use the emotion estimation function to analyze the emotional state of the bereaved family members when sharing memories and display memories that correspond to their emotions. For example, the dialogue model learning unit uses the emotion estimation function to analyze the emotional state of the bereaved family members when sharing memories. For example, when the bereaved family members are moved, emotional memories are displayed, and when the bereaved family members are happy, happy memories are displayed. In this way, by analyzing the emotional state of the bereaved family members and displaying memories that correspond to their emotions, it becomes possible to share memories that empathize with each other more emotionally.

[0085] The dialogue model learning unit can prioritize displaying videos or photos related to specific anniversaries or events of the deceased when sharing memories. For example, the dialogue model learning unit prioritizes displaying videos or photos related to specific anniversaries or events of the deceased when sharing memories. For example, it prioritizes displaying videos or photos of the deceased's birthday or wedding anniversary. This prioritizes displaying videos or photos related to specific anniversaries or events of the deceased, thereby improving the quality of sharing memories with bereaved family members.

[0086] The dialogue model learning unit can recreate videos or photos that correspond to the specific health condition or age of the deceased when sharing memories. For example, the dialogue model learning unit recreates videos or photos that correspond to the specific health condition of the deceased when sharing memories. For example, it recreates videos of the deceased when they were ill and photos of the deceased when they were healthy. This allows for more realistic sharing of memories by recreating videos or photos that correspond to the specific health condition or age of the deceased.

[0087] The dialogue model learning unit can accommodate different languages ​​or dialects when sharing memories, enabling international use. The dialogue model learning unit builds a system that accommodates different languages ​​when sharing memories. For example, memories of a deceased person can be displayed in English and French. This allows for international use by accommodating different languages ​​and dialects.

[0088] The dialogue model learning unit can also reproduce songs or poetry recitations sung by the deceased when sharing memories, incorporating musical elements. For example, the dialogue model learning unit reproduces songs sung by the deceased when sharing memories. For example, it reproduces songs that the deceased liked or often sang. This allows for the sharing of memories that incorporate musical elements by reciting songs or poetry recitations sung by the deceased.

[0089] The dialogue model learning unit can use the emotion estimation function to display memories that the bereaved family can most emotionally empathize with. The dialogue model learning unit, for example, uses the emotion estimation function to display memories that the bereaved family can most emotionally empathize with. For example, memories that move or delight the bereaved family. This improves the quality of sharing by displaying memories that the bereaved family can most emotionally empathize with.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When collecting data on the deceased, the data collection unit also collects the usage history of the deceased's digital devices, which the analysis unit can use to analyze the deceased's digital lifestyle. For example, the data collection unit collects the history of the applications and websites the deceased frequently used to understand the deceased's interests. This allows the analysis of the deceased's digital lifestyle and the creation of a more detailed profile.

[0092] When collecting data on the deceased, the data collection unit also collects information on the deceased's favorite items and collections, which the analysis unit can use to analyze the deceased's hobbies and preferences. For example, by collecting information on the stamps and coins the deceased collected, or the musical instruments they played, the analysis unit can understand the deceased's hobbies and preferences. This allows for the creation of a detailed profile of the deceased's hobbies and preferences.

[0093] When collecting data on the deceased, the data collection unit also collects information on the communities and clubs the deceased participated in, and the analysis unit can use this information to analyze the deceased's social network. For example, information on sports clubs and hobby circles the deceased belonged to can be collected to understand the deceased's social network. This allows for the creation of a detailed profile of the deceased's social network.

[0094] When collecting data on the deceased, the data collection unit also collects information on the books and articles the deceased was reading, and the analysis unit can use this information to analyze the deceased's knowledge and range of interests. For example, by collecting information on the books and magazine articles the deceased was reading, the range of knowledge and interests of the deceased can be grasped. This makes it possible to create a detailed profile of the deceased's knowledge and interests.

[0095] When collecting data on the deceased, the data collection unit also collects information on perfumes and aromas used by the deceased, and the analysis unit can use this information to analyze the deceased's olfactory preferences. For example, by collecting the types of perfumes and aromas that the deceased loved, the analysis unit can understand the deceased's olfactory preferences. This allows for the creation of a detailed profile of the deceased's olfactory preferences.

[0096] When collecting data on the deceased, the data collection unit can use the emotion estimation function to analyze the emotional state of the deceased and filter the data based on their emotions. For example, it can identify scenes in which the deceased is laughing or feeling emotional, and store these scenes preferentially in the database. This allows the data collection unit to analyze the emotional state of the deceased and filter the data based on their emotions, thereby enabling the collection of data that is more emotionally relatable.

[0097] When generating voice data of the deceased, the voice generation unit uses an emotion estimation function to reproduce the emotional state of the deceased, enabling a conversation rich in emotion. For example, it can reproduce the voice of the deceased when they are happy or sad. This allows the emotional state of the deceased to be reproduced, enabling a conversation rich in emotion, thereby improving the quality of conversation with the bereaved.

[0098] The dialogue model learning unit uses the emotion estimation function to learn the emotional state of the deceased when learning the dialogue model, and can realize a dialogue based on emotions. For example, it learns the dialogue when the deceased was happy and the dialogue when the deceased was sad. In this way, by learning the emotional state of the deceased and realizing a dialogue based on emotions, it is possible to provide a more natural and emotional dialogue.

[0099] When conversing with the bereaved family, the dialogue model learning unit uses the emotion estimation function to analyze the emotional state of the bereaved family and respond accordingly. For example, if the bereaved family is sad, the dialogue model learning unit responds with words of comfort, and if they are happy, the dialogue model learning unit responds with words of empathy. This allows the system to analyze the emotional state of the bereaved family and respond according to their emotions, thereby providing a more emotionally empathetic dialogue.

[0100] When sharing memories, the dialogue model learning unit can use the emotion estimation function to analyze the emotional state of the bereaved family members and display memories that correspond to their emotions. For example, if the bereaved family members are emotional, it will display touching memories, and if they are happy, it will display happy memories. This allows the emotional state of the bereaved family members to be analyzed and memories that correspond to their emotions to be shared in a more emotionally empathetic way.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The data collection unit collects data about the deceased. For example, it can collect home videos, audio messages, and photos. The data collection unit then analyzes the collected data using Google Cloud Video AI to extract the deceased's characteristics, speech patterns, and facial expressions. For example, it can analyze footage of the deceased spending time with their family and learn from their conversations and facial expressions. Step 2: The analysis unit analyzes the data of the deceased collected by the data collection unit, extracting the deceased's characteristics, speech patterns, facial expressions, etc. Step 3: The voice generator generates the deceased's voice data based on the analyzed data. For example, using the OpenAI Voice Engine, AI recreates the deceased's voice based on the deceased's speaking style and vocal characteristics. Step 4: The dialogue model training unit trains a dialogue model based on the generated voice data. For example, it uses the OpenAI Fine-tuning API to learn the thoughts and reactions of the deceased and builds a model suitable for dialogue with bereaved family members.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] 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.

[0145] 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.

[0146] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] 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.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects data on the deceased; an analysis unit that analyzes the data of the deceased collected by the data collection unit; a voice generating unit that generates voice data of the deceased based on the data analyzed by the analyzing unit; a dialogue model learning unit that learns a dialogue model based on the voice data generated by the voice generation unit. A system characterized by:

2. The data collection unit When collecting data on the deceased, an emotion estimation function is used to analyze the emotional state of the deceased and filter the data based on the emotion.

2. The system of claim 1.

3. The data collection unit When collecting data on the deceased, interview footage from family or friends is also collected, and the analysis unit analyzes the deceased's relationships based on this data.

2. The system of claim 1.

4. The voice generation unit When generating voice data for the deceased, the emotional state of the deceased is reproduced using an emotion estimation function, enabling emotionally rich dialogue.

2. The system of claim 1.

5. The dialogue model learning unit When training the dialogue model, the emotional state of the deceased is learned using an emotion estimation function, enabling dialogue based on emotions.

2. The system of claim 1.

6. The dialogue model learning unit When sharing memories, the emotional state of the bereaved can be analyzed using an emotion estimation function, and memories can be displayed according to their emotions.

2. The system of claim 1.

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