system

Abstract

An object of the system according to the embodiment is to alleviate loneliness and loneliness by reproducing the conversation pattern and personality of the deceased or the pet and allowing the user to have a conversation with the deceased or the pet.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a conversation unit. The collection unit collects a conversation history or SNS data. The analysis part analyzes the data collected by the collection part and reproduces the conversation pattern and personality of the deceased or the pet. The generation unit generates an avatar on the basis of the data analyzed by the analysis unit. The conversation unit has a conversation with the avatar generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 technologies do not adequately provide means to alleviate the loneliness and sadness that comes with parting from a deceased loved one or pet, and there is room for improvement.

[0005] The system according to the embodiment aims to reproduce the conversation patterns and personalities of deceased people or pets, and to allow users to alleviate loneliness and sadness by having conversations with the deceased or pets. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a conversation unit. The collection unit collects conversation history or SNS data. The analysis unit analyzes the data collected by the collection unit and recreates the conversation patterns and personality of the deceased person or pet. The generation unit generates an avatar based on the data analyzed by the analysis unit. The conversation unit converses with the avatar generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment reproduces the conversation patterns and personalities of the deceased person or pet, allowing the user to alleviate loneliness and sadness by conversing with the deceased person or pet. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention allows users to feel the presence of a deceased person or pet even after their death. This system collects conversation history and social media data, and a generation AI generates an avatar of the deceased person or pet based on this information. For example, the system registers personal information, such as the conversation history, conversation timing, personality, and social media accounts, of the deceased person or pet on a platform. The generation AI then analyzes this information and generates an avatar that reproduces the conversation patterns and personality of the deceased person or pet. The generated avatar can reproduce the same conversations the deceased person or pet had before their death. This allows surviving family and friends to alleviate loneliness and sadness by conversing with the deceased person or pet. This allows the system to feel the presence of the deceased person or pet even after their death. For example, by using an avatar to converse in the same way the deceased person had before their death, users can feel as if the deceased were still alive. Furthermore, by conversing with a pet avatar, users can feel as if the pet is still nearby. This allows family and friends to feel the presence of the deceased person or pet even after their death and alleviate loneliness and sadness.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a conversation unit. The collection unit collects conversation history or SNS data. For example, the collection unit collects conversation history or SNS data of the deceased person or pet. The collection unit can collect data such as text messages, voice messages, and posted content. The analysis unit analyzes the collected data and recreates the conversation patterns and personality of the deceased person or pet. The analysis unit analyzes the data using, for example, natural language processing technology. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit recreates the conversation patterns and personality based on the frequency of conversations, the choice of words used, and the like. The generation unit generates an avatar based on the analyzed data. For example, the generation unit creates a 3D model. The generation unit can also perform voice synthesis. The generation unit generates an avatar that recreates the conversation patterns and personality of the deceased person or pet. The conversation unit converses with the generated avatar. The conversation unit conducts a conversation based on, for example, the flow of dialogue and the timing of responses. The conversation unit can also reproduce the same conversations that the deceased person or pet used to make in life. This allows the system according to the embodiment to make the presence of the deceased person or pet felt even after death. For example, when family or friends talk to the avatar, the avatar responds in the same way that the deceased person or pet used to make in life. This allows family and friends to ease their loneliness by talking to the deceased person or pet.

[0030] The collection unit can collect conversation history or social media data of the deceased person or pet. The collection unit collects, for example, conversation history or social media data of the deceased person or pet. The collection unit can collect data such as text messages, voice messages, and posted content. By collecting the conversation history or social media data of the deceased person or pet, information necessary for generating an avatar can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data of the deceased person or pet into the generation AI and have the generation AI collect the data.

[0031] The analysis unit can analyze the collected data and recreate the conversation patterns and personality of the deceased or pet. The analysis unit, for example, analyzes the collected data using natural language processing technology. For example, the analysis unit analyzes text data and recreates the conversation patterns of the deceased or pet. The analysis unit can also analyze data using machine learning algorithms. For example, the analysis unit analyzes audio data and recreates the personality of the deceased or pet. The analysis unit can also recreate the conversation patterns and personality based on the frequency of conversations and the choice of words used. In this way, the conversation patterns and personality of the deceased or pet can be recreated by analyzing the collected data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI recreate the conversation patterns and personality.

[0032] The generation unit can generate an avatar based on the analyzed data. The generation unit, for example, creates a 3D model based on the analyzed data. For example, the generation unit generates a 3D model that reproduces the appearance of the deceased person or pet. The generation unit can also perform voice synthesis. For example, the generation unit performs voice synthesis to reproduce the voice of the deceased person or pet. The generation unit can also generate an avatar that reproduces the conversation pattern and personality of the deceased person or pet. In this way, by generating an avatar based on the analyzed data, it is possible to reproduce the conversations that the deceased person or pet had when they were alive. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the analyzed data into a generation AI and cause the generation AI to generate an avatar.

[0033] The conversation unit can converse with the generated avatar. For example, the conversation unit converses with the generated avatar based on the flow of the dialogue and the timing of responses. For example, the conversation unit reproduces the responses that the deceased person or pet would have used when they were alive. Furthermore, when family or friends speak to the avatar, the avatar can respond in the same way that the deceased person or pet would have used when they were alive. This allows the user to feel the presence of the deceased person or pet by conversing with the generated avatar. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input a dialogue model for conversing with the generated avatar into the generation AI and have the generation AI execute the conversation.

[0034] The conversation unit enables the avatar to respond in the same way as the deceased or pet did when a family member or friend speaks to the avatar. For example, when a family member or friend speaks to the avatar, the avatar responds in the same way as the deceased or pet did when they were alive. For example, the conversation unit reproduces the language and timing of the deceased or pet's conversation. The conversation unit can also reproduce the emotional expressions of the deceased or pet. For example, the conversation unit reproduces responses that express joy or sadness of the deceased or pet. In this way, when family members or friends speak to the avatar, the avatar responds in the same way as the deceased or pet did when they were alive, thereby alleviating loneliness and sadness. Some or all of the above-described processing in the conversation unit may be performed using, or without, AI. For example, the conversation unit may input the content of what family members or friends say to the avatar into a generation AI and have the generation AI execute the avatar's responses.

[0035] The collection unit can analyze the past conversation history of the deceased or pet and select an appropriate collection method. For example, the collection unit analyzes the past conversation history of the deceased or pet and selects the optimal collection method. For example, the collection unit prioritizes collecting data from social media platforms frequently used by the deceased. Furthermore, if the pet was active during a particular time period, the collection unit can focus on collecting data from that time period. Furthermore, if the deceased talked a lot about a particular topic, the collection unit can prioritize collecting data related to that topic. This allows the optimal collection method to be selected by analyzing the past conversation history of the deceased or pet. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the past conversation history of the deceased or pet into the generation AI and have the generation AI select the optimal collection method.

[0036] The collection unit may filter data based on the living conditions or areas of interest of the deceased or pet when collecting data. For example, the collection unit may filter data based on the living conditions or areas of interest of the deceased or pet when collecting data. For example, the collection unit may preferentially collect data related to the hobbies of the deceased. If the pet was interested in a particular food, the collection unit may collect data related to that food. If the deceased frequently visited a particular place, the collection unit may collect data related to that place. By filtering data based on the living conditions or areas of interest of the deceased or pet, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may cause the generation AI to perform filtering based on the living conditions or areas of interest of the deceased or pet.

[0037] The collection unit can select the optimal collection means depending on the input method of the deceased or pet when collecting data. For example, the collection unit selects the optimal collection means depending on the input method of the deceased or pet (voice, text, image, etc.) when collecting data. For example, if the deceased used many voice messages, the collection unit can prioritize collecting voice data. Also, if many photos of the pet are left behind, the collection unit can also prioritize collecting image data. Also, if the deceased frequently used text messages, the collection unit can prioritize collecting text data. In this way, by selecting the optimal collection means depending on the input method of the deceased or pet, data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to perform data collection depending on the input method of the deceased or pet.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the deceased person or pet when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the deceased person or pet when collecting data. For example, if the deceased lived in a particular area, the collection unit can prioritize collecting data related to that area. Furthermore, if the pet frequently visited a particular park, the collection unit can also collect data related to that park. Furthermore, if the deceased loved to travel, the collection unit can prioritize collecting data related to places the pet visited. In this way, by collecting data by taking into account the geographical location information of the deceased person or pet, more relevant data can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the deceased person or pet into the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the social media activity of the deceased or pet to collect relevant data during data collection. For example, the collection unit can analyze the social media activity of the deceased or pet during data collection and collect relevant data. For example, the collection unit can collect data from social media platforms to which the deceased frequently posted. Furthermore, if many photos of the pet were posted, the collection unit can also collect those photos. Furthermore, if the deceased used a specific hashtag, the collection unit can collect data related to that hashtag. This allows for efficient collection of relevant data by analyzing the social media activity of the deceased or pet. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the social media activity of the deceased or pet into the generation AI and cause the generation AI to collect relevant data.

[0040] The collection unit can customize the collection method by reflecting the past feedback of the deceased person or pet when collecting data. For example, the collection unit customizes the collection method by reflecting the past feedback of the deceased person or pet when collecting data. For example, if the deceased person preferred a particular data collection method, the collection unit can preferentially use that method. Also, if the pet was active during a particular time period, the collection unit can focus on collecting data during that time period. Also, if the deceased person talked a lot about a particular topic, the collection unit can preferentially collect data related to that topic. In this way, by reflecting the past feedback of the deceased person or pet, a more appropriate collection method can be selected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past feedback of the deceased person or pet into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis of important data. The analysis unit can also perform a simplified analysis of general data. The analysis unit can also perform a focused analysis of data related to a specific topic. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply an appropriate analysis algorithm depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to past analysis results of the deceased or pet during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the deceased or pet during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the deceased's past conversation patterns. The analysis unit can also improve the accuracy of the analysis by referring to the pet's past behavioral patterns. The analysis unit can also improve the accuracy of the analysis by referring to the deceased's past social media posts. In this way, the accuracy of the analysis can be improved by referring to the past analysis results of the deceased or pet. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results of the deceased or pet into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also prioritize analyzing data submitted within a specific period. The analysis unit can also prioritize analyzing data from a period specified by the user. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can appropriately adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also prioritize analysis of data related to a specific topic. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the level of expertise of the deceased or pet during the analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the level of expertise of the deceased or pet during the analysis. For example, if the deceased had specialized knowledge, the analysis unit can use a lot of technical terms. Alternatively, if the deceased had general knowledge, the analysis unit can refrain from using technical terms. The analysis unit can also use technical terms related to the behavior of the pet. This allows for appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the level of expertise of the deceased or pet. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the level of expertise of the deceased or pet into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The generation unit can analyze the past behavioral patterns of the deceased person or pet and select an appropriate generation method when generating an avatar. For example, when generating an avatar, the generation unit analyzes the past behavioral patterns of the deceased person or pet and selects the optimal generation method. For example, the generation unit generates an avatar based on the behaviors frequently performed by the deceased person. Furthermore, if the pet was active during a specific time period, the generation unit can generate an avatar that reproduces the behavior of that time period. Furthermore, if the deceased talked a lot about a specific topic, the generation unit can generate an avatar related to that topic. In this way, the optimal avatar generation method can be selected by analyzing the past behavioral patterns of the deceased person or pet. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the past behavioral patterns of the deceased person or pet into the generation AI and have the generation AI select the optimal generation method.

[0048] The generation unit can customize the generation means based on the current living situation of the deceased person or pet when generating an avatar. For example, when generating an avatar, the generation unit customizes the generation means based on the current living situation of the deceased person or pet. For example, if the deceased had a particular lifestyle, the generation unit can generate an avatar that reflects that lifestyle. Furthermore, if the pet lived in a particular environment, the generation unit can generate an avatar that recreates that environment. Furthermore, if the deceased had a particular hobby, the generation unit can generate an avatar related to that hobby. By customizing the generation means based on the current living situation of the deceased person or pet, more realistic avatars can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the current living situation of the deceased person or pet into the generation AI and have the generation AI customize the generation means.

[0049] The generation unit can improve the generation method when generating an avatar by reflecting feedback from the deceased or pet. For example, when generating an avatar, the generation unit can improve the generation method by reflecting feedback from the deceased or pet. For example, if the deceased left specific feedback, the generation unit can generate an avatar that reflects that feedback. Also, if the pet liked a specific behavior, the generation unit can generate an avatar that reflects that behavior. Also, if the deceased talked a lot about a specific topic, the generation unit can generate an avatar related to that topic. In this way, by reflecting feedback from the deceased or pet, a more appropriate avatar generation method can be selected. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input feedback from the deceased or pet into the generation AI and cause the generation AI to improve the generation method.

[0050] The generation unit can select an appropriate generation method when generating an avatar, taking into account the geographical location information of the deceased person or pet. For example, when generating an avatar, the generation unit selects the optimal generation method by taking into account the geographical location information of the deceased person or pet. For example, if the deceased lived in a specific area, the generation unit can generate an avatar with a background related to that area. Furthermore, if the pet frequently visited a specific park, the generation unit can generate an avatar with the park as its background. Furthermore, if the deceased loved traveling, the generation unit can generate an avatar with a background related to places the deceased visited. In this way, by taking into account the geographical location information of the deceased person or pet, a more relevant avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the geographical location information of the deceased person or pet into the generation AI and have the generation AI select the optimal generation method.

[0051] The generation unit can analyze the social media activity of the deceased person or pet and suggest a generation method when generating an avatar. For example, the generation unit can analyze the social media activity of the deceased person or pet and suggest a generation method when generating an avatar. For example, the generation unit can collect data from social media platforms to which the deceased frequently posted and generate an avatar based on that data. Furthermore, if the deceased posted many photos of their pet, the generation unit can generate an avatar based on those photos. Furthermore, if the deceased used a specific hashtag, the generation unit can generate an avatar based on data related to that hashtag. By analyzing the social media activity of the deceased person or pet, more appropriate avatar generation methods can be suggested. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the social media activity of the deceased person or pet into the generation AI and have the generation AI execute the suggested generation method.

[0052] The generation unit can customize the generation method when generating an avatar by reflecting past feedback from the deceased or pet. For example, when generating an avatar, the generation unit customizes the generation method by reflecting past feedback from the deceased or pet. For example, if the deceased left specific feedback, the generation unit can generate an avatar that reflects that feedback. Also, if the pet liked a specific behavior, the generation unit can generate an avatar that reflects that behavior. Also, if the deceased talked a lot about a specific topic, the generation unit can generate an avatar related to that topic. In this way, by reflecting past feedback from the deceased or pet, a more appropriate avatar generation method can be selected. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past feedback from the deceased or pet into the generation AI and cause the generation AI to customize the generation method.

[0053] The conversation unit can select the optimal conversation method by analyzing the past conversation patterns of the deceased or pet during a conversation. For example, the conversation unit can select the optimal conversation method by analyzing the past conversation patterns of the deceased or pet during a conversation. For example, the conversation unit can conduct a conversation based on the language frequently used by the deceased. If the pet was active during a specific time period, the conversation unit can also conduct a conversation that reproduces the behavior of that time period. If the deceased talked a lot about a specific topic, the conversation unit can also conduct a conversation related to that topic. This makes it possible to select the optimal conversation method by analyzing the past conversation patterns of the deceased or pet. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without AI. For example, the conversation unit can input the past conversation patterns of the deceased or pet into a generation AI and have the generation AI select the optimal conversation method.

[0054] The conversation unit can customize the conversation means based on the current living situation of the deceased or pet during a conversation. For example, the conversation unit customizes the conversation means based on the current living situation of the deceased or pet during a conversation. For example, if the deceased had a particular lifestyle, the conversation unit can hold a conversation that reflects that lifestyle. Furthermore, if the pet lived in a particular environment, the conversation unit can hold a conversation that recreates that environment. Furthermore, if the deceased had a particular hobby, the conversation unit can hold a conversation related to that hobby. By customizing the conversation means based on the current living situation of the deceased or pet, more realistic conversations can be provided. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without AI. For example, the conversation unit can input the current living situation of the deceased or pet into the generation AI and cause the generation AI to customize the conversation means.

[0055] The conversation unit can improve the conversation method by reflecting feedback from the deceased or pet during a conversation. For example, the conversation unit can improve the conversation method by reflecting feedback from the deceased or pet during a conversation. For example, if the deceased left specific feedback, the conversation unit can conduct a conversation that reflects that feedback. Furthermore, if the pet liked a specific behavior, the conversation unit can conduct a conversation that reflects that behavior. Furthermore, if the deceased talked a lot about a specific topic, the conversation unit can conduct a conversation related to that topic. In this way, by reflecting feedback from the deceased or pet, a more appropriate conversation method can be selected. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input feedback from the deceased or pet into the generation AI and cause the generation AI to improve the conversation method.

[0056] The conversation unit can select the optimal conversation method during a conversation by taking into account the geographic location information of the deceased or pet. For example, the conversation unit selects the optimal conversation method by taking into account the geographic location information of the deceased or pet during a conversation. For example, if the deceased lived in a particular area, the conversation unit can conduct the conversation based on topics related to that area. Furthermore, if the pet frequently visited a particular park, the conversation unit can conduct the conversation based on topics related to that park. Furthermore, if the deceased loved to travel, the conversation unit can conduct the conversation based on topics related to places the deceased visited. In this way, by taking into account the geographic location information of the deceased or pet, more relevant conversation can be provided. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the geographic location information of the deceased or pet into the generation AI and cause the generation AI to select the optimal conversation method.

[0057] The conversation unit can analyze the social media activity of the deceased or pet during a conversation to suggest a means of conversation. For example, the conversation unit can collect data from social media platforms to which the deceased frequently posted and conduct a conversation based on that data. Furthermore, if the deceased posted many photos of the pet, the conversation unit can conduct a conversation based on those photos. Furthermore, if the deceased used a specific hashtag, the conversation unit can conduct a conversation based on data related to that hashtag. By analyzing the social media activity of the deceased or pet, more appropriate means of conversation can be suggested. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without AI. For example, the conversation unit can input the social media activity of the deceased or pet into a generation AI and have the generation AI suggest a means of conversation.

[0058] The conversation unit can customize the conversation method by reflecting past feedback from the deceased or pet during a conversation. For example, the conversation unit customizes the conversation method by reflecting past feedback from the deceased or pet during a conversation. For example, if the deceased left specific feedback, the conversation unit can conduct a conversation that reflects that feedback. Furthermore, if the pet liked a specific behavior, the conversation unit can conduct a conversation that reflects that behavior. Furthermore, if the deceased talked a lot about a specific topic, the conversation unit can conduct a conversation related to that topic. In this way, by reflecting past feedback from the deceased or pet, a more appropriate conversation method can be selected. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input past feedback from the deceased or pet into the generation AI and cause the generation AI to customize the conversation method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can analyze the past behavioral patterns of the deceased or pet to improve the accuracy of the analysis. For example, if the deceased was frequently active during a specific time period, the analysis can focus on data from that time period. Also, if a pet liked a specific behavior, the analysis can prioritize data related to that behavior. Furthermore, if the deceased talked a lot about a specific topic, the analysis can focus on data related to that topic. This allows the accuracy of the analysis to be improved by referring to the past behavioral patterns of the deceased or pet.

[0061] The conversation unit can analyze the past conversation patterns of the deceased or pet and select the optimal conversation method. For example, conversations can be held based on the language frequently used by the deceased. Also, if the pet was active at a specific time of day, conversations can be held that replicate the behavior of that time of day. Furthermore, if the deceased talked a lot about a specific topic, conversations related to that topic can be held. In this way, by analyzing the past conversation patterns of the deceased or pet, the optimal conversation method can be selected.

[0062] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the deceased person or pet. For example, if the deceased lived in a specific area, data related to that area can be collected preferentially. Also, if the pet frequently visited a specific park, data related to that park can be collected preferentially. Furthermore, if the deceased loved to travel, data related to places the deceased visited can be collected preferentially. In this way, by collecting data by taking into account the geographical location information of the deceased person or pet, more relevant data can be obtained.

[0063] When generating an avatar, the generation unit can analyze the past behavioral patterns of the deceased person or pet and select an appropriate generation method. For example, an avatar can be generated based on the actions that the deceased frequently performed. Also, if a pet was active during a specific time period, an avatar that reproduces the actions of that time period can be generated. Furthermore, if the deceased talked a lot about a specific topic, an avatar related to that topic can be generated. In this way, by analyzing the past behavioral patterns of the deceased person or pet, the optimal avatar generation method can be selected.

[0064] When collecting data, the collection unit can analyze the social media activity of the deceased person or pet to collect relevant data. For example, data can be collected from social media platforms to which the deceased frequently posted. If many photos of pets were posted, those photos can also be collected. Furthermore, if the deceased person used a specific hashtag, data related to that hashtag can be collected. This allows for efficient collection of relevant data by analyzing the social media activity of the deceased person or pet.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects conversation history or SNS data. For example, the collection unit collects conversation history or SNS data of a deceased person or pet. The collection unit can collect data such as text messages, voice messages, and posted content. Step 2: The analysis unit analyzes the collected data and recreates the conversation patterns and personality of the deceased person or pet. The analysis unit analyzes the data using, for example, natural language processing technology. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit recreates the conversation patterns and personality based on the frequency of conversations, choice of words, etc. Step 3: The generator generates an avatar based on the analyzed data. The generator, for example, creates a 3D model. The generator can also perform voice synthesis. The generator generates an avatar that reproduces the speech patterns and personality of the deceased person or pet. Step 4: The conversation unit converses with the generated avatar. The conversation unit conducts the conversation based on, for example, the flow of dialogue and the timing of responses. The conversation unit can also reproduce the responses that the deceased person or pet used when they were alive. This allows the system according to the embodiment to make the presence of the deceased person or pet felt even after their death.

[0067] (Example 2) A system according to an embodiment of the present invention allows users to feel the presence of a deceased person or pet even after their death. This system collects conversation history and social media data, and a generation AI generates an avatar of the deceased person or pet based on this information. For example, the system registers personal information, such as the conversation history, conversation timing, personality, and social media accounts, of the deceased person or pet on a platform. The generation AI then analyzes this information and generates an avatar that reproduces the conversation patterns and personality of the deceased person or pet. The generated avatar can reproduce the same conversations the deceased person or pet had before their death. This allows surviving family and friends to alleviate loneliness and sadness by conversing with the deceased person or pet. This allows the system to feel the presence of the deceased person or pet even after their death. For example, by using an avatar to converse in the same way the deceased person had before their death, users can feel as if the deceased were still alive. Furthermore, by conversing with a pet avatar, users can feel as if the pet is still nearby. This allows family and friends to feel the presence of the deceased person or pet even after their death and alleviate loneliness and sadness.

[0068] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a conversation unit. The collection unit collects conversation history or SNS data. For example, the collection unit collects conversation history or SNS data of the deceased person or pet. The collection unit can collect data such as text messages, voice messages, and posted content. The analysis unit analyzes the collected data and recreates the conversation patterns and personality of the deceased person or pet. The analysis unit analyzes the data using, for example, natural language processing technology. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit recreates the conversation patterns and personality based on the frequency of conversations, the choice of words used, and the like. The generation unit generates an avatar based on the analyzed data. For example, the generation unit creates a 3D model. The generation unit can also perform voice synthesis. The generation unit generates an avatar that recreates the conversation patterns and personality of the deceased person or pet. The conversation unit converses with the generated avatar. The conversation unit conducts a conversation based on, for example, the flow of dialogue and the timing of responses. The conversation unit can also reproduce the same conversations that the deceased person or pet used to make in life. This allows the system according to the embodiment to make the presence of the deceased person or pet felt even after death. For example, when family or friends talk to the avatar, the avatar responds in the same way that the deceased person or pet used to make in life. This allows family and friends to ease their loneliness by talking to the deceased person or pet.

[0069] The collection unit can collect conversation history or social media data of the deceased person or pet. The collection unit collects, for example, conversation history or social media data of the deceased person or pet. The collection unit can collect data such as text messages, voice messages, and posted content. By collecting the conversation history or social media data of the deceased person or pet, information necessary for generating an avatar can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data of the deceased person or pet into the generation AI and have the generation AI collect the data.

[0070] The analysis unit can analyze the collected data and recreate the conversation patterns and personality of the deceased or pet. The analysis unit, for example, analyzes the collected data using natural language processing technology. For example, the analysis unit analyzes text data and recreates the conversation patterns of the deceased or pet. The analysis unit can also analyze data using machine learning algorithms. For example, the analysis unit analyzes audio data and recreates the personality of the deceased or pet. The analysis unit can also recreate the conversation patterns and personality based on the frequency of conversations and the choice of words used. In this way, the conversation patterns and personality of the deceased or pet can be recreated by analyzing the collected data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI recreate the conversation patterns and personality.

[0071] The generation unit can generate an avatar based on the analyzed data. The generation unit, for example, creates a 3D model based on the analyzed data. For example, the generation unit generates a 3D model that reproduces the appearance of the deceased person or pet. The generation unit can also perform voice synthesis. For example, the generation unit performs voice synthesis to reproduce the voice of the deceased person or pet. The generation unit can also generate an avatar that reproduces the conversation pattern and personality of the deceased person or pet. In this way, by generating an avatar based on the analyzed data, it is possible to reproduce the conversations that the deceased person or pet had when they were alive. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the analyzed data into a generation AI and cause the generation AI to generate an avatar.

[0072] The conversation unit can converse with the generated avatar. For example, the conversation unit converses with the generated avatar based on the flow of the dialogue and the timing of responses. For example, the conversation unit reproduces the responses that the deceased person or pet would have used when they were alive. Furthermore, when family or friends speak to the avatar, the avatar can respond in the same way that the deceased person or pet would have used when they were alive. This allows the user to feel the presence of the deceased person or pet by conversing with the generated avatar. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input a dialogue model for conversing with the generated avatar into the generation AI and have the generation AI execute the conversation.

[0073] The conversation unit enables the avatar to respond in the same way as the deceased or pet did when a family member or friend speaks to the avatar. For example, when a family member or friend speaks to the avatar, the avatar responds in the same way as the deceased or pet did when they were alive. For example, the conversation unit reproduces the language and timing of the deceased or pet's conversation. The conversation unit can also reproduce the emotional expressions of the deceased or pet. For example, the conversation unit reproduces responses that express joy or sadness of the deceased or pet. In this way, when family members or friends speak to the avatar, the avatar responds in the same way as the deceased or pet did when they were alive, thereby alleviating loneliness and sadness. Some or all of the above-described processing in the conversation unit may be performed using, or without, AI. For example, the conversation unit may input the content of what family members or friends say to the avatar into a generation AI and have the generation AI execute the avatar's responses.

[0074] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can reduce the frequency of data collection when the user is feeling sad and collect data when the user is calm. The collection unit can also increase the frequency of data collection when the user is relaxed and collect more information. The collection unit can also temporarily stop data collection when the user is busy and resume it when the user has time. This can reduce the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0075] The collection unit can analyze the past conversation history of the deceased or pet and select an appropriate collection method. For example, the collection unit analyzes the past conversation history of the deceased or pet and selects the optimal collection method. For example, the collection unit prioritizes collecting data from social media platforms frequently used by the deceased. Furthermore, if the pet was active during a particular time period, the collection unit can focus on collecting data from that time period. Furthermore, if the deceased talked a lot about a particular topic, the collection unit can prioritize collecting data related to that topic. This allows the optimal collection method to be selected by analyzing the past conversation history of the deceased or pet. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the past conversation history of the deceased or pet into the generation AI and have the generation AI select the optimal collection method.

[0076] The collection unit may filter data based on the living conditions or areas of interest of the deceased or pet when collecting data. For example, the collection unit may filter data based on the living conditions or areas of interest of the deceased or pet when collecting data. For example, the collection unit may preferentially collect data related to the hobbies of the deceased. If the pet was interested in a particular food, the collection unit may collect data related to that food. If the deceased frequently visited a particular place, the collection unit may collect data related to that place. By filtering data based on the living conditions or areas of interest of the deceased or pet, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may cause the generation AI to perform filtering based on the living conditions or areas of interest of the deceased or pet.

[0077] The collection unit can select the optimal collection means depending on the input method of the deceased or pet when collecting data. For example, the collection unit selects the optimal collection means depending on the input method of the deceased or pet (voice, text, image, etc.) when collecting data. For example, if the deceased used many voice messages, the collection unit can prioritize collecting voice data. Also, if many photos of the pet are left behind, the collection unit can also prioritize collecting image data. Also, if the deceased frequently used text messages, the collection unit can prioritize collecting text data. In this way, by selecting the optimal collection means depending on the input method of the deceased or pet, data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to perform data collection depending on the input method of the deceased or pet.

[0078] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling sad, the collection unit can prioritize collecting data related to emotions. Furthermore, if the user is relaxed, the collection unit can prioritize collecting daily conversation data. Furthermore, if the user is excited, the collection unit can prioritize collecting data related to specific events. This enables data collection that takes the user's emotions into consideration by determining the priority of data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0079] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the deceased person or pet when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the deceased person or pet when collecting data. For example, if the deceased lived in a particular area, the collection unit can prioritize collecting data related to that area. Furthermore, if the pet frequently visited a particular park, the collection unit can also collect data related to that park. Furthermore, if the deceased loved to travel, the collection unit can prioritize collecting data related to places the pet visited. In this way, by collecting data by taking into account the geographical location information of the deceased person or pet, more relevant data can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the deceased person or pet into the generation AI and cause the generation AI to collect highly relevant data.

[0080] The collection unit can analyze the social media activity of the deceased or pet to collect relevant data during data collection. For example, the collection unit can analyze the social media activity of the deceased or pet during data collection and collect relevant data. For example, the collection unit can collect data from social media platforms to which the deceased frequently posted. Furthermore, if many photos of the pet were posted, the collection unit can also collect those photos. Furthermore, if the deceased used a specific hashtag, the collection unit can collect data related to that hashtag. This allows for efficient collection of relevant data by analyzing the social media activity of the deceased or pet. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the social media activity of the deceased or pet into the generation AI and cause the generation AI to collect relevant data.

[0081] The collection unit can customize the collection method by reflecting the past feedback of the deceased person or pet when collecting data. For example, the collection unit customizes the collection method by reflecting the past feedback of the deceased person or pet when collecting data. For example, if the deceased person preferred a particular data collection method, the collection unit can preferentially use that method. Also, if the pet was active during a particular time period, the collection unit can focus on collecting data during that time period. Also, if the deceased person talked a lot about a particular topic, the collection unit can preferentially collect data related to that topic. In this way, by reflecting the past feedback of the deceased person or pet, a more appropriate collection method can be selected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past feedback of the deceased person or pet into the generation AI and cause the generation AI to customize the collection method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling sad, the analysis unit can display the analysis results in gentle language. If the user is relaxed, the analysis unit can display detailed analysis results. If the user is excited, the analysis unit can display analysis results with visually stimulating effects. This allows the analysis results to be easily understood by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis of important data. The analysis unit can also perform a simplified analysis of general data. The analysis unit can also perform a focused analysis of data related to a specific topic. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] The analysis unit can apply an appropriate analysis algorithm depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0085] The analysis unit can improve the accuracy of the analysis by referring to past analysis results of the deceased or pet during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the deceased or pet during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the deceased's past conversation patterns. The analysis unit can also improve the accuracy of the analysis by referring to the pet's past behavioral patterns. The analysis unit can also improve the accuracy of the analysis by referring to the deceased's past social media posts. In this way, the accuracy of the analysis can be improved by referring to the past analysis results of the deceased or pet. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results of the deceased or pet into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, if the user is feeling sad, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotion, it is possible to provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also prioritize analyzing data submitted within a specific period. The analysis unit can also prioritize analyzing data from a period specified by the user. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0088] The analysis unit can appropriately adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also prioritize analysis of data related to a specific topic. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the level of expertise of the deceased or pet during the analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the level of expertise of the deceased or pet during the analysis. For example, if the deceased had specialized knowledge, the analysis unit can use a lot of technical terms. Alternatively, if the deceased had general knowledge, the analysis unit can refrain from using technical terms. The analysis unit can also use technical terms related to the behavior of the pet. This allows for appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the level of expertise of the deceased or pet. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the level of expertise of the deceased or pet into the generation AI and cause the generation AI to adjust the use of technical terms.

[0090] The generation unit can estimate the user's emotion and adjust the avatar generation method based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the avatar generation method based on the estimated user's emotion. For example, if the user is feeling sad, the generation unit can generate an avatar with a gentle expression. If the user is relaxed, the generation unit can generate an avatar with a natural expression. If the user is excited, the generation unit can generate an avatar with a lively expression. This allows the avatar optimal for the user to be generated by adjusting the avatar generation method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the avatar generation method.

[0091] The generation unit can analyze the past behavioral patterns of the deceased person or pet and select an appropriate generation method when generating an avatar. For example, when generating an avatar, the generation unit analyzes the past behavioral patterns of the deceased person or pet and selects the optimal generation method. For example, the generation unit generates an avatar based on the behaviors frequently performed by the deceased person. Furthermore, if the pet was active during a specific time period, the generation unit can generate an avatar that reproduces the behavior of that time period. Furthermore, if the deceased talked a lot about a specific topic, the generation unit can generate an avatar related to that topic. In this way, the optimal avatar generation method can be selected by analyzing the past behavioral patterns of the deceased person or pet. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the past behavioral patterns of the deceased person or pet into the generation AI and have the generation AI select the optimal generation method.

[0092] The generation unit can customize the generation means based on the current living situation of the deceased person or pet when generating an avatar. For example, when generating an avatar, the generation unit customizes the generation means based on the current living situation of the deceased person or pet. For example, if the deceased had a particular lifestyle, the generation unit can generate an avatar that reflects that lifestyle. Furthermore, if the pet lived in a particular environment, the generation unit can generate an avatar that recreates that environment. Furthermore, if the deceased had a particular hobby, the generation unit can generate an avatar related to that hobby. By customizing the generation means based on the current living situation of the deceased person or pet, more realistic avatars can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the current living situation of the deceased person or pet into the generation AI and have the generation AI customize the generation means.

[0093] The generation unit can improve the generation method when generating an avatar by reflecting feedback from the deceased or pet. For example, when generating an avatar, the generation unit can improve the generation method by reflecting feedback from the deceased or pet. For example, if the deceased left specific feedback, the generation unit can generate an avatar that reflects that feedback. Also, if the pet liked a specific behavior, the generation unit can generate an avatar that reflects that behavior. Also, if the deceased talked a lot about a specific topic, the generation unit can generate an avatar related to that topic. In this way, by reflecting feedback from the deceased or pet, a more appropriate avatar generation method can be selected. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input feedback from the deceased or pet into the generation AI and cause the generation AI to improve the generation method.

[0094] The generation unit can estimate the user's emotions and determine the priority of avatars to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and determines the priority of avatars to be generated based on the estimated user emotions. For example, if the user is feeling sad, the generation unit may prioritize generating an avatar of the deceased person who was closest to the user. Furthermore, if the user is relaxed, the generation unit may simultaneously generate avatars of multiple deceased people or pets. Furthermore, if the user is excited, the generation unit may prioritize generating avatars related to a specific event. Thus, by determining the priority of avatars to be generated based on the user's emotions, it is possible to prioritize generating the most suitable avatar for the user. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the priority of avatars.

[0095] The generation unit can select an appropriate generation method when generating an avatar, taking into account the geographical location information of the deceased person or pet. For example, when generating an avatar, the generation unit selects the optimal generation method by taking into account the geographical location information of the deceased person or pet. For example, if the deceased lived in a specific area, the generation unit can generate an avatar with a background related to that area. Furthermore, if the pet frequently visited a specific park, the generation unit can generate an avatar with the park as its background. Furthermore, if the deceased loved traveling, the generation unit can generate an avatar with a background related to places the deceased visited. In this way, by taking into account the geographical location information of the deceased person or pet, a more relevant avatar can be generated. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the geographical location information of the deceased person or pet into the generation AI and have the generation AI select the optimal generation method.

[0096] The generation unit can analyze the social media activity of the deceased person or pet and suggest a generation method when generating an avatar. For example, the generation unit can analyze the social media activity of the deceased person or pet and suggest a generation method when generating an avatar. For example, the generation unit can collect data from social media platforms to which the deceased frequently posted and generate an avatar based on that data. Furthermore, if the deceased posted many photos of their pet, the generation unit can generate an avatar based on those photos. Furthermore, if the deceased used a specific hashtag, the generation unit can generate an avatar based on data related to that hashtag. By analyzing the social media activity of the deceased person or pet, more appropriate avatar generation methods can be suggested. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the social media activity of the deceased person or pet into the generation AI and have the generation AI execute the suggested generation method.

[0097] The generation unit can customize the generation method when generating an avatar by reflecting past feedback from the deceased or pet. For example, when generating an avatar, the generation unit customizes the generation method by reflecting past feedback from the deceased or pet. For example, if the deceased left specific feedback, the generation unit can generate an avatar that reflects that feedback. Also, if the pet liked a specific behavior, the generation unit can generate an avatar that reflects that behavior. Also, if the deceased talked a lot about a specific topic, the generation unit can generate an avatar related to that topic. In this way, by reflecting past feedback from the deceased or pet, a more appropriate avatar generation method can be selected. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past feedback from the deceased or pet into the generation AI and cause the generation AI to customize the generation method.

[0098] The conversation unit can estimate the user's emotions and adjust the conversation method based on the estimated user emotions. For example, the conversation unit can estimate the user's emotions and adjust the conversation method based on the estimated user emotions. For example, if the user is feeling sad, the conversation unit can use gentle language when speaking. Furthermore, if the user is relaxed, the conversation unit can use natural language when speaking. Furthermore, if the user is excited, the conversation unit can use lively language when speaking. This allows the optimal conversation to be provided by adjusting the conversation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversation unit can be performed using AI, for example, or without AI. For example, the conversation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the conversation method.

[0099] The conversation unit can select the optimal conversation method by analyzing the past conversation patterns of the deceased or pet during a conversation. For example, the conversation unit can select the optimal conversation method by analyzing the past conversation patterns of the deceased or pet during a conversation. For example, the conversation unit can conduct a conversation based on the language frequently used by the deceased. If the pet was active during a specific time period, the conversation unit can also conduct a conversation that reproduces the behavior of that time period. If the deceased talked a lot about a specific topic, the conversation unit can also conduct a conversation related to that topic. This makes it possible to select the optimal conversation method by analyzing the past conversation patterns of the deceased or pet. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without AI. For example, the conversation unit can input the past conversation patterns of the deceased or pet into a generation AI and have the generation AI select the optimal conversation method.

[0100] The conversation unit can customize the conversation means based on the current living situation of the deceased or pet during a conversation. For example, the conversation unit customizes the conversation means based on the current living situation of the deceased or pet during a conversation. For example, if the deceased had a particular lifestyle, the conversation unit can hold a conversation that reflects that lifestyle. Furthermore, if the pet lived in a particular environment, the conversation unit can hold a conversation that recreates that environment. Furthermore, if the deceased had a particular hobby, the conversation unit can hold a conversation related to that hobby. By customizing the conversation means based on the current living situation of the deceased or pet, more realistic conversations can be provided. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without AI. For example, the conversation unit can input the current living situation of the deceased or pet into the generation AI and cause the generation AI to customize the conversation means.

[0101] The conversation unit can improve the conversation method by reflecting feedback from the deceased or pet during a conversation. For example, the conversation unit can improve the conversation method by reflecting feedback from the deceased or pet during a conversation. For example, if the deceased left specific feedback, the conversation unit can conduct a conversation that reflects that feedback. Furthermore, if the pet liked a specific behavior, the conversation unit can conduct a conversation that reflects that behavior. Furthermore, if the deceased talked a lot about a specific topic, the conversation unit can conduct a conversation related to that topic. In this way, by reflecting feedback from the deceased or pet, a more appropriate conversation method can be selected. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input feedback from the deceased or pet into the generation AI and cause the generation AI to improve the conversation method.

[0102] The conversation unit can estimate the user's emotions and determine the priority of conversations based on the estimated user emotions. For example, the conversation unit can estimate the user's emotions and determine the priority of conversations based on the estimated user emotions. For example, if the user is feeling sad, the conversation unit can prioritize conversations related to emotions. Furthermore, if the user is relaxed, the conversation unit can prioritize everyday conversations. Furthermore, if the user is excited, the conversation unit can prioritize conversations related to specific events. This allows the optimal conversation for the user to be provided preferentially by determining the priority of conversations based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversation unit can be performed using, for example, an AI. For example, the conversation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of conversations.

[0103] The conversation unit can select the optimal conversation method during a conversation by taking into account the geographic location information of the deceased or pet. For example, the conversation unit selects the optimal conversation method by taking into account the geographic location information of the deceased or pet during a conversation. For example, if the deceased lived in a particular area, the conversation unit can conduct the conversation based on topics related to that area. Furthermore, if the pet frequently visited a particular park, the conversation unit can conduct the conversation based on topics related to that park. Furthermore, if the deceased loved to travel, the conversation unit can conduct the conversation based on topics related to places the deceased visited. In this way, by taking into account the geographic location information of the deceased or pet, more relevant conversation can be provided. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the geographic location information of the deceased or pet into the generation AI and cause the generation AI to select the optimal conversation method.

[0104] The conversation unit can analyze the social media activity of the deceased or pet during a conversation to suggest a means of conversation. For example, the conversation unit can collect data from social media platforms to which the deceased frequently posted and conduct a conversation based on that data. Furthermore, if the deceased posted many photos of the pet, the conversation unit can conduct a conversation based on those photos. Furthermore, if the deceased used a specific hashtag, the conversation unit can conduct a conversation based on data related to that hashtag. By analyzing the social media activity of the deceased or pet, more appropriate means of conversation can be suggested. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without AI. For example, the conversation unit can input the social media activity of the deceased or pet into a generation AI and have the generation AI suggest a means of conversation.

[0105] The conversation unit can customize the conversation method by reflecting past feedback from the deceased or pet during a conversation. For example, the conversation unit customizes the conversation method by reflecting past feedback from the deceased or pet during a conversation. For example, if the deceased left specific feedback, the conversation unit can conduct a conversation that reflects that feedback. Furthermore, if the pet liked a specific behavior, the conversation unit can conduct a conversation that reflects that behavior. Furthermore, if the deceased talked a lot about a specific topic, the conversation unit can conduct a conversation related to that topic. In this way, by reflecting past feedback from the deceased or pet, a more appropriate conversation method can be selected. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input past feedback from the deceased or pet into the generation AI and cause the generation AI to customize the conversation method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and conversation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect conversation history and SNS data using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect data such as text messages, voice messages, and posted content. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using natural language processing technology and machine learning algorithms. The generation unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a 3D model and performs voice synthesis. The conversation unit can be realized, for example, by the control unit 46A of the smart device 14 and converses with the generated avatar. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and conversation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect conversation history and SNS data using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect data such as text messages, voice messages, and posted content. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using natural language processing technology and machine learning algorithms. The generation unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a 3D model and performs voice synthesis. The conversation unit can be realized, for example, by the control unit 46A of the smart glasses 214 and converses with the generated avatar. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and conversation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect conversation history and SNS data using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit is also realized by the specific processing unit 290 of the data processing device 12 and collects data such as text messages, voice messages, and posted content. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using natural language processing technology and machine learning algorithms. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a 3D model and performs voice synthesis. The conversation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and converses with the generated avatar. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and conversation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect conversation history and SNS data using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect data such as text messages, voice messages, and posted content. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes data using natural language processing technology and machine learning algorithms. The generation unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a 3D model and performs voice synthesis. The conversation unit can be realized, for example, by the control unit 46A of the robot 414 and converses with the generated avatar.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can also estimate the user's emotions and adjust the priority of data collection based on the estimated emotions. For example, if the user is feeling sad, the collection unit can prioritize collecting data related to emotions. Also, if the user is relaxed, the collection unit can prioritize collecting daily conversation data. Furthermore, if the user is excited, the collection unit can prioritize collecting data related to a specific event. In this way, by adjusting the priority of data to be collected according to the user's emotions, data collection that takes the user's emotions into consideration is possible.

[0108] The analysis unit can analyze the past behavioral patterns of the deceased or pet to improve the accuracy of the analysis. For example, if the deceased was frequently active during a specific time period, the analysis can focus on data from that time period. Also, if a pet liked a specific behavior, the analysis can prioritize data related to that behavior. Furthermore, if the deceased talked a lot about a specific topic, the analysis can focus on data related to that topic. This allows the accuracy of the analysis to be improved by referring to the past behavioral patterns of the deceased or pet.

[0109] The generation unit can estimate the user's emotions and adjust the avatar's facial expressions and movements based on the estimated emotions. For example, if the user is feeling sad, the avatar will have a gentle facial expression and gentle movements. If the user is relaxed, the avatar can have a natural facial expression and movements. Furthermore, if the user is excited, the avatar can have a lively facial expression and movements. This allows the avatar's facial expressions and movements to be adjusted according to the user's emotions, providing a more realistic interaction experience.

[0110] The conversation unit can analyze the past conversation patterns of the deceased or pet and select the optimal conversation method. For example, conversations can be held based on the language frequently used by the deceased. Also, if the pet was active at a specific time of day, conversations can be held that replicate the behavior of that time of day. Furthermore, if the deceased talked a lot about a specific topic, conversations related to that topic can be held. In this way, by analyzing the past conversation patterns of the deceased or pet, the optimal conversation method can be selected.

[0111] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the deceased person or pet. For example, if the deceased lived in a specific area, data related to that area can be collected preferentially. Also, if the pet frequently visited a specific park, data related to that park can be collected preferentially. Furthermore, if the deceased loved to travel, data related to places the deceased visited can be collected preferentially. In this way, by collecting data by taking into account the geographical location information of the deceased person or pet, more relevant data can be obtained.

[0112] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling sad, the analysis results can be displayed in gentle language. If the user is relaxed, detailed analysis results can be displayed. Furthermore, if the user is excited, analysis results can be displayed with visually stimulating effects. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0113] When generating an avatar, the generation unit can analyze the past behavioral patterns of the deceased person or pet and select an appropriate generation method. For example, an avatar can be generated based on the actions that the deceased frequently performed. Also, if a pet was active during a specific time period, an avatar that reproduces the actions of that time period can be generated. Furthermore, if the deceased talked a lot about a specific topic, an avatar related to that topic can be generated. In this way, by analyzing the past behavioral patterns of the deceased person or pet, the optimal avatar generation method can be selected.

[0114] The conversation unit can estimate the user's emotions and adjust the conversation style based on the estimated emotions. For example, if the user is feeling sad, the conversation can be conducted in gentle language. If the user is relaxed, the conversation can be conducted in natural language. Furthermore, if the user is excited, the conversation can be conducted in lively language. In this way, by adjusting the conversation style according to the user's emotions, it is possible to provide the user with the most suitable conversation.

[0115] When collecting data, the collection unit can analyze the social media activity of the deceased person or pet to collect relevant data. For example, data can be collected from social media platforms to which the deceased frequently posted. If many photos of pets were posted, those photos can also be collected. Furthermore, if the deceased person used a specific hashtag, data related to that hashtag can be collected. This allows for efficient collection of relevant data by analyzing the social media activity of the deceased person or pet.

[0116] The generation unit can estimate the user's emotions and determine the priority of the avatars to be generated based on the estimated emotions. For example, if the user is feeling sad, the avatar of the deceased person closest to the user can be generated with priority. Also, if the user is relaxed, avatars of multiple deceased people and pets can be generated simultaneously. Furthermore, if the user is excited, avatars related to a specific event can be generated with priority. In this way, by determining the priority of the avatars to be generated according to the user's emotions, it is possible to generate the avatar that is most suitable for the user with priority.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects conversation history or SNS data. For example, the collection unit collects conversation history or SNS data of a deceased person or pet. The collection unit can collect data such as text messages, voice messages, and posted content. Step 2: The analysis unit analyzes the collected data and recreates the conversation patterns and personality of the deceased person or pet. The analysis unit analyzes the data using, for example, natural language processing technology. The analysis unit can also analyze the data using machine learning algorithms. The analysis unit recreates the conversation patterns and personality based on the frequency of conversations, choice of words, etc. Step 3: The generator generates an avatar based on the analyzed data. The generator, for example, creates a 3D model. The generator can also perform voice synthesis. The generator generates an avatar that reproduces the speech patterns and personality of the deceased person or pet. Step 4: The conversation unit converses with the generated avatar. The conversation unit conducts the conversation based on, for example, the flow of dialogue and the timing of responses. The conversation unit can also reproduce the responses that the deceased person or pet used when they were alive. This allows the system according to the embodiment to make the presence of the deceased person or pet felt even after their death.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects conversation history or SNS data; an analysis unit that analyzes the data collected by the collection unit and recreates the speech patterns and personality of the deceased person or pet; a generation unit that generates an avatar based on the data analyzed by the analysis unit; a conversation unit that converses with the avatar generated by the generation unit. A system characterized by:

2. The collecting unit Collecting conversation history or social media data of deceased people or pets 2. The system of claim 1.

3. The analysis unit Analyzing the collected data to recreate the speech patterns and personalities of deceased people and pets 2. The system of claim 1.

4. The generation unit Generate an avatar based on the analyzed data 2. The system of claim 1.

5. The conversation unit is Talk to the generated avatar 2. The system of claim 1.

6. The conversation unit is When a family member or friend talks to the avatar, the avatar responds in the same way that the deceased person or pet did when they were alive.

2. The system of claim 1.

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

8. The collecting unit Analyze the past conversation history of the deceased person or pet and select the appropriate collection method 2. The system of claim 1.

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