System

Abstract

An object of the system according to the embodiment is to collect messages and memories that elderly people want to send in various formats and naturally reproduce the messages and the memories as emotional messages.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a reproduction unit. The collection unit collects messages, life lessons, and episodes of memories that the elderly person wants to send in the form of text, images, audio, and video. The analysis unit analyzes the information collected by the collection unit and generates an emotional message. The generation unit reproduces the message generated by the analysis unit in natural voice. The reproduction unit stores the message generated by the generation unit as a message to be transmitted to a relative or a friend or as a message to the user after a lapse of time.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 have not adequately collected messages and memories that elderly people want to send in various formats and reproduced them naturally as emotional messages, so there is room for improvement.

[0005] The system according to the embodiment aims to collect messages and memories that elderly people want to send in various formats and play them back naturally as emotional messages. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a playback unit. The collection unit collects messages, life lessons, and memorable episodes that the elderly person wants to send in the form of text, images, audio, and video. The analysis unit analyzes the information collected by the collection unit and generates emotional messages. The generation unit plays back the messages generated by the analysis unit in a natural voice. The playback unit saves the messages generated by the generation unit as messages to send to relatives and friends, or as messages to the elderly person after some time has passed. [Effects of the Invention]

[0007] The system according to the embodiment can collect messages and memories that the elderly want to send in various formats and play them back naturally as emotional messages. [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) An AI virtual time capsule system according to an embodiment of the present invention collects messages, life lessons, and memorable episodes that seniors want to share and stores them as messages to send to relatives and friends or to themselves later. This system utilizes multimodal capabilities to store messages in various formats, including text, images, audio, and video. AI combines these modal information to generate emotive messages. For example, it analyzes old photos and videos of seniors and generates text and audio based on them. Furthermore, AI can learn the seniors' voices and speech patterns to reproduce messages in a natural voice. This creates an emotional connection and vivid memories. Leveraging such multimodal capabilities, the AI ​​virtual time capsule system provides a mechanism for seniors to more vividly relive their memories and deepen their bonds with family and friends, thereby creating new value. This AI virtual time capsule system allows seniors to store messages, life lessons, and memorable episodes that seniors want to share in various formats, creating emotional connections and vivid memories. It also provides a mechanism for seniors to more vividly relive their memories and deepen their bonds with family and friends, thereby creating new value.

[0029] An AI virtual time capsule system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a playback unit. The collection unit collects messages, life lessons, and memorable episodes that the elderly want to share in the form of text, images, audio, and video. For example, the collection unit can record the elderly talking about their experiences while looking at old photos and save the audio data. The collection unit can also accept input in text format. The collection unit can also collect images and videos. For example, the collection unit can scan old photos owned by the elderly and save them as digital data. The analysis unit analyzes the information collected by the collection unit and generates an emotional message. For example, the analysis unit can analyze old photos of the elderly and generate text based on the photos. The analysis unit can learn the elderly's voice and tone of voice and generate a message in a natural voice. The generation unit plays back the message generated by the analysis unit in a natural voice. For example, the generation unit can learn the elderly's voice and tone of voice and play back the message in a natural voice. The playback unit saves the message generated by the generation unit as a message to send to relatives or friends, or as a message to the elderly person after some time has passed. For example, the playback unit can record a message for an elderly person's grandchild and play that message when the grandchild reaches adulthood. As a result, the AI ​​virtual time capsule system according to the embodiment can collect messages, life lessons, and memorable episodes that the elderly want to send in various formats, generate emotional messages, play them in a natural voice, and save them.

[0030] The collection unit can analyze the elderly person's past message collection history and select an appropriate collection method. For example, the collection unit prioritizes collection of formats (audio, text, etc.) that the elderly person has frequently used in the past. The collection unit can also suggest collection methods (interview format, free writing, etc.) that the elderly person has preferred in the past. The collection unit can also select a format related to a specific theme from the elderly person's past collection history. This makes it possible to select the optimal collection method based on the elderly person's past collection history.

[0031] The collection unit can filter messages based on the elderly person's current living situation (e.g., health condition or hobbies) when collecting messages. For example, the collection unit prioritizes collection of episodes related to hobbies or activities in which the elderly person is currently interested. The collection unit can also select a collection method that places less strain on the elderly person, taking into consideration the elderly person's current health condition. The collection unit can also provide an appropriate collection method depending on the elderly person's living environment (home, facility, etc.). This makes it possible to collect appropriate messages based on the elderly person's current living situation and areas of interest.

[0032] The collection unit can select the optimal collection means depending on the input method (voice, text, image, etc.) of the elderly person when collecting data. For example, if the elderly person prefers voice input, the collection unit can provide a voice recording function with priority. Also, if the elderly person prefers text input, the collection unit can provide a simple keyboard input interface. Also, if the elderly person prefers images, the collection unit can provide a photo taking or scanning function. In this way, the optimal collection means can be provided depending on the input method of the elderly person.

[0033] The collection unit can prioritize collecting highly relevant messages by taking into consideration the geographical location information of the elderly person when collecting messages. For example, the collection unit can prioritize collecting memories related to the area where the elderly person currently lives. The collection unit can also prioritize collecting episodes related to places where the elderly person has lived in the past. The collection unit can also prioritize collecting memories related to places where the elderly person has traveled. This makes it possible to collect highly relevant messages based on the geographical location information of the elderly person.

[0034] The collection unit can analyze the social media activities of the elderly person at the time of collection and collect related messages. The collection unit can collect, for example, memorable photos and posts shared by the elderly person on social media. The collection unit can also analyze the elderly person's interactions with friends on social media and collect related episodes. The collection unit can also collect memories related to topics the elderly person follows on social media. This makes it possible to collect related messages based on the elderly person's social media activities.

[0035] The collection unit can customize the collection method by reflecting the elderly person's past feedback at the time of collection. For example, the collection unit can provide collection methods that the elderly person has previously preferred with priority. The collection unit can also improve the collection interface based on the elderly person's past feedback. The collection unit can also adjust the theme of the collection content by reflecting the elderly person's past feedback. This makes it possible to customize the collection method based on the elderly person's past feedback.

[0036] The analysis unit can adjust the level of detail of the analysis based on the importance of the information collected during the analysis. For example, the analysis unit performs a detailed analysis on important episodes. The analysis unit can also perform a simplified analysis on general episodes. The analysis unit can also perform a focused analysis on information that the elderly person particularly wants to emphasize. This makes it possible to adjust the level of detail of the analysis based on the importance of the collected information.

[0037] The analysis unit can apply an appropriate analysis algorithm depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. The analysis unit can also apply an image analysis algorithm to image information. The analysis unit can also apply a voice analysis algorithm to voice information. This makes it possible to apply the optimal analysis algorithm depending on the category of information.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the past analysis results of the elderly person during the analysis. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results of the elderly person. The analysis unit can also extract specific patterns from the past analysis results of the elderly person and reflect them in the analysis. The analysis unit can also build a feedback loop to improve the accuracy of the analysis by referring to the past analysis results of the elderly person. This makes it possible to improve the accuracy of the analysis by referring to the past analysis results of the elderly person.

[0039] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit prioritizes the analysis of the most recent information and quickly generates a message. The analysis unit can also postpone analysis of information that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission of information.

[0040] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information and generates an integrated message. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also optimize the order of analysis based on the relevance of information. This makes it possible to adjust the order of analysis based on the relevance of information.

[0041] The analysis unit can adjust the use of technical terms during analysis according to the elderly person's level of expertise. For example, if the elderly person has technical knowledge, the analysis unit can perform the analysis using technical terms. Also, if the elderly person does not have technical knowledge, the analysis unit can perform the analysis using simple language. Also, the analysis unit can select appropriate terms according to the elderly person's level of expertise to perform the analysis. This makes it possible to select appropriate terms according to the elderly person's level of expertise to perform the analysis.

[0042] The generation unit can adjust the level of detail of the generation based on the importance of the information analyzed during generation. For example, the generation unit generates detailed information for important episodes. The generation unit can also generate simplified information for general episodes. The generation unit can also generate information that the elderly person particularly wants to emphasize, with a focus on that information. This allows the level of detail of the generation to be adjusted based on the importance of the analyzed information.

[0043] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit applies a natural language generation algorithm to text information. The generation unit can also apply an image generation algorithm to image information. The generation unit can also apply a voice generation algorithm to voice information. This makes it possible to apply the optimal generation algorithm depending on the category of information.

[0044] The generation unit can improve the accuracy of generation by referring to the elderly person's past generation results during generation. The generation unit can adjust the generation algorithm based on the elderly person's past generation results, for example. The generation unit can also extract specific patterns from the elderly person's past generation results and reflect them in the generation. The generation unit can also build a feedback loop to improve the accuracy of generation by referring to the elderly person's past generation results. This makes it possible to improve the accuracy of generation by referring to the elderly person's past generation results.

[0045] The generation unit can determine the priority of generation based on the time of submission of information at the time of generation. For example, the generation unit generates the latest information with priority and provides the message quickly. The generation unit can also postpone the generation of information that was submitted earlier. The generation unit can also adjust the generation schedule based on the time of submission. This makes it possible to determine the priority of generation based on the time of submission of information.

[0046] The generation unit can adjust the order of generation based on the relevance of the information at the time of generation. For example, the generation unit generates highly relevant information with priority and provides an integrated message. The generation unit can also postpone generation of less relevant information. The generation unit can also optimize the order of generation based on the relevance of the information. This makes it possible to adjust the order of generation based on the relevance of the information.

[0047] The generation unit can adjust the use of technical terms during generation according to the expert knowledge level of the elderly person. For example, if the elderly person has expert knowledge, the generation unit generates the text using technical terms. Also, if the elderly person does not have expert knowledge, the generation unit can generate the text using simple language. Also, the generation unit can select appropriate terms according to the expert knowledge level of the elderly person to generate the text. In this way, appropriate terms can be selected according to the expert knowledge level of the elderly person to generate the text.

[0048] The playback unit can adjust the level of detail of the playback based on the importance of the message generated during playback. For example, the playback unit performs detailed playback for important episodes. The playback unit can also perform simplified playback for general episodes. The playback unit can also perform focused playback for information that the elderly person particularly wants to emphasize. In this way, the level of detail of the playback can be adjusted based on the importance of the generated message.

[0049] The playback unit can apply different playback algorithms depending on the message category during playback. For example, the playback unit applies a natural language generation algorithm to text information for playback. The playback unit can also apply an image generation algorithm to image information for playback. The playback unit can also apply a voice generation algorithm to voice information for playback. This makes it possible to apply the optimal playback algorithm depending on the message category.

[0050] The playback unit can improve the accuracy of playback by referring to the elderly person's past playback results during playback. For example, the playback unit adjusts the playback algorithm based on the elderly person's past playback results. The playback unit can also extract specific patterns from the elderly person's past playback results and reflect them in the playback. The playback unit can also build a feedback loop to improve the accuracy of playback by referring to the elderly person's past playback results. This makes it possible to improve the accuracy of playback by referring to the elderly person's past playback results.

[0051] The playback unit can determine the priority of playback based on the submission time of the message during playback. For example, the playback unit prioritizes playback of the most recent message and provides it quickly. The playback unit can also postpone playback of messages that were submitted earlier. The playback unit can also adjust the playback schedule based on the submission time. This makes it possible to determine the priority of playback based on the submission time of the message.

[0052] The playback unit can adjust the playback order based on the relevance of the messages during playback. For example, the playback unit prioritizes playback of highly relevant messages to provide an integrated message. The playback unit can also postpone playback of less relevant messages. The playback unit can also optimize the playback order based on the relevance of the messages. This makes it possible to adjust the playback order based on the relevance of the messages.

[0053] The playback unit can adjust the use of technical terms during playback depending on the level of expertise of the elderly person. For example, if the elderly person has technical knowledge, the playback unit can play back using technical terms. Also, if the elderly person does not have technical knowledge, the playback unit can play back using simple language. Also, the playback unit can select appropriate terms depending on the level of expertise of the elderly person and play back. In this way, appropriate terms can be selected depending on the level of expertise of the elderly person and played back.

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

[0055] The collection unit can filter messages based on the elderly person's current living situation (e.g., health status or hobbies) when collecting them. For example, it can prioritize collecting episodes related to hobbies and activities that the elderly person is currently interested in. The collection unit can also select a collection method that places less strain on the elderly person, taking into consideration the elderly person's current health status. Furthermore, the collection unit can provide an appropriate collection method depending on the elderly person's living environment (home, facility, etc.). This makes it possible to collect appropriate messages based on the elderly person's current living situation and areas of interest.

[0056] The analysis unit can apply an appropriate analysis algorithm depending on the category of information during analysis. For example, a natural language processing algorithm is applied to text information. The analysis unit can also apply an image analysis algorithm to image information. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice information. This makes it possible to apply the optimal analysis algorithm depending on the category of information.

[0057] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, a natural language generation algorithm is applied to text information. The generation unit can also apply an image generation algorithm to image information. Furthermore, the generation unit can also apply a voice generation algorithm to voice information. This makes it possible to apply the optimal generation algorithm depending on the category of information.

[0058] The playback unit can apply different playback algorithms depending on the message category during playback. For example, a natural language generation algorithm is applied to text information for playback. The playback unit can also apply an image generation algorithm to image information for playback. Furthermore, the playback unit can also apply a voice generation algorithm to voice information for playback. This makes it possible to apply the optimal playback algorithm depending on the message category.

[0059] The collection unit can prioritize collecting highly relevant messages by taking into account the geographical location information of the elderly person when collecting messages. For example, the collection unit can prioritize collecting memories related to the area where the elderly person currently lives. The collection unit can also prioritize collecting episodes related to places where the elderly person has lived in the past. Furthermore, the collection unit can also prioritize collecting memories related to places where the elderly person has traveled. In this way, highly relevant messages can be collected based on the geographical location information of the elderly person.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects messages, life lessons, and memorable episodes that the elderly want to share in the form of text, images, audio, and video. For example, the collection unit can record the elderly talking about their experiences while looking at old photos and save the audio data. The collection unit can also accept input in text format. Furthermore, the collection unit can collect images and videos. For example, the collection unit can scan old photos that the elderly have and save them as digital data. Step 2: The analysis unit analyzes the information collected by the collection unit and generates an emotional message. For example, the analysis unit analyzes old photos of elderly people and generates sentences based on the photos. The analysis unit can also learn the voice and tone of voice of elderly people and generate messages in a natural voice. Step 3: The generator reproduces the message generated by the analyzer in a natural voice. For example, the generator can learn the voice and tone of an elderly person and reproduce the message in a natural voice. Step 4: The playback unit saves the message generated by the generation unit as a message to send to relatives or friends or as a message to the user after a certain time has passed. For example, the playback unit can record a message for an elderly person to send to their grandchild and play the message when the grandchild becomes an adult.

[0062] (Example 2) An AI virtual time capsule system according to an embodiment of the present invention collects messages, life lessons, and memorable episodes that seniors want to share and stores them as messages to send to relatives and friends or to themselves later. This system utilizes multimodal capabilities to store messages in various formats, including text, images, audio, and video. AI combines these modal information to generate emotive messages. For example, it analyzes old photos and videos of seniors and generates text and audio based on them. Furthermore, AI can learn the seniors' voices and speech patterns to reproduce messages in a natural voice. This creates an emotional connection and vivid memories. Leveraging such multimodal capabilities, the AI ​​virtual time capsule system provides a mechanism for seniors to more vividly relive their memories and deepen their bonds with family and friends, thereby creating new value. This AI virtual time capsule system allows seniors to store messages, life lessons, and memorable episodes that seniors want to share in various formats, creating emotional connections and vivid memories. It also provides a mechanism for seniors to more vividly relive their memories and deepen their bonds with family and friends, thereby creating new value.

[0063] An AI virtual time capsule system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a playback unit. The collection unit collects messages, life lessons, and memorable episodes that the elderly want to share in the form of text, images, audio, and video. For example, the collection unit can record the elderly talking about their experiences while looking at old photos and save the audio data. The collection unit can also accept input in text format. The collection unit can also collect images and videos. For example, the collection unit can scan old photos owned by the elderly and save them as digital data. The analysis unit analyzes the information collected by the collection unit and generates an emotional message. For example, the analysis unit can analyze old photos of the elderly and generate text based on the photos. The analysis unit can learn the elderly's voice and tone of voice and generate a message in a natural voice. The generation unit plays back the message generated by the analysis unit in a natural voice. For example, the generation unit can learn the elderly's voice and tone of voice and play back the message in a natural voice. The playback unit saves the message generated by the generation unit as a message to send to relatives or friends, or as a message to the elderly person after some time has passed. For example, the playback unit can record a message for an elderly person's grandchild and play that message when the grandchild reaches adulthood. As a result, the AI ​​virtual time capsule system according to the embodiment can collect messages, life lessons, and memorable episodes that the elderly want to send in various formats, generate emotional messages, play them in a natural voice, and save them.

[0064] The collection unit can estimate the elderly person's emotions and select an appropriate format for the message to be collected based on the estimated emotions. For example, if the elderly person is emotional, the collection unit can prioritize collecting audio and video to enrich the expression of the emotion. In addition, if the elderly person is feeling nostalgic, the collection unit can collect a combination of old photos and text to vividly recall the memories. In addition, if the elderly person is sad, the collection unit can mainly collect audio and text to carefully express the emotion. This makes it possible to collect messages in the optimal format depending on the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The collection unit can analyze the elderly person's past message collection history and select an appropriate collection method. For example, the collection unit prioritizes collection of formats (audio, text, etc.) that the elderly person has frequently used in the past. The collection unit can also suggest collection methods (interview format, free writing, etc.) that the elderly person has preferred in the past. The collection unit can also select a format related to a specific theme from the elderly person's past collection history. This makes it possible to select the optimal collection method based on the elderly person's past collection history.

[0066] The collection unit can filter messages based on the elderly person's current living situation (e.g., health condition or hobbies) when collecting messages. For example, the collection unit prioritizes collection of episodes related to hobbies or activities in which the elderly person is currently interested. The collection unit can also select a collection method that places less strain on the elderly person, taking into consideration the elderly person's current health condition. The collection unit can also provide an appropriate collection method depending on the elderly person's living environment (home, facility, etc.). This makes it possible to collect appropriate messages based on the elderly person's current living situation and areas of interest.

[0067] The collection unit can select the optimal collection means depending on the input method (voice, text, image, etc.) of the elderly person when collecting data. For example, if the elderly person prefers voice input, the collection unit can provide a voice recording function with priority. Also, if the elderly person prefers text input, the collection unit can provide a simple keyboard input interface. Also, if the elderly person prefers images, the collection unit can provide a photo taking or scanning function. In this way, the optimal collection means can be provided depending on the input method of the elderly person.

[0068] The collection unit can estimate the emotions of the elderly person and determine the priority of messages to be collected based on the estimated emotions. For example, if the elderly person is emotional, the collection unit can prioritize collecting that emotion and capture the peak of the emotion. Furthermore, if the elderly person is feeling nostalgic, the collection unit can prioritize collecting memorable episodes. Furthermore, if the elderly person is sad, the collection unit can prioritize collecting that emotion to deepen the emotional expression. This makes it possible to determine the priority of messages to be collected according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0069] The collection unit can prioritize collecting highly relevant messages by taking into consideration the geographical location information of the elderly person when collecting messages. For example, the collection unit can prioritize collecting memories related to the area where the elderly person currently lives. The collection unit can also prioritize collecting episodes related to places where the elderly person has lived in the past. The collection unit can also prioritize collecting memories related to places where the elderly person has traveled. This makes it possible to collect highly relevant messages based on the geographical location information of the elderly person.

[0070] The collection unit can analyze the social media activities of the elderly person at the time of collection and collect related messages. The collection unit can collect, for example, memorable photos and posts shared by the elderly person on social media. The collection unit can also analyze the elderly person's interactions with friends on social media and collect related episodes. The collection unit can also collect memories related to topics the elderly person follows on social media. This makes it possible to collect related messages based on the elderly person's social media activities.

[0071] The collection unit can customize the collection method by reflecting the elderly person's past feedback at the time of collection. For example, the collection unit can provide collection methods that the elderly person has previously preferred with priority. The collection unit can also improve the collection interface based on the elderly person's past feedback. The collection unit can also adjust the theme of the collection content by reflecting the elderly person's past feedback. This makes it possible to customize the collection method based on the elderly person's past feedback.

[0072] The analysis unit can estimate the emotion of the elderly person and adjust the expression method of the analysis based on the estimated emotion. For example, if the elderly person is emotional, the analysis unit uses an expression method that emphasizes the emotion. Furthermore, if the elderly person is feeling nostalgic, the analysis unit can also use an expression method that vividly brings back memories. Furthermore, if the elderly person is sad, the analysis unit can also use a method that politely expresses the emotion. This makes it possible to adjust the expression method of the analysis according to the emotion of the elderly person. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the information collected during the analysis. For example, the analysis unit performs a detailed analysis on important episodes. The analysis unit can also perform a simplified analysis on general episodes. The analysis unit can also perform a focused analysis on information that the elderly person particularly wants to emphasize. This makes it possible to adjust the level of detail of the analysis based on the importance of the collected information.

[0074] The analysis unit can apply an appropriate analysis algorithm depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. The analysis unit can also apply an image analysis algorithm to image information. The analysis unit can also apply a voice analysis algorithm to voice information. This makes it possible to apply the optimal analysis algorithm depending on the category of information.

[0075] The analysis unit can improve the accuracy of the analysis by referring to the past analysis results of the elderly person during the analysis. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results of the elderly person. The analysis unit can also extract specific patterns from the past analysis results of the elderly person and reflect them in the analysis. The analysis unit can also build a feedback loop to improve the accuracy of the analysis by referring to the past analysis results of the elderly person. This makes it possible to improve the accuracy of the analysis by referring to the past analysis results of the elderly person.

[0076] The analysis unit can estimate the elderly person's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the elderly person is emotional, the analysis unit can perform a detailed analysis and generate a longer message. If the elderly person is in a hurry, the analysis unit can also perform a concise analysis and generate a shorter message. If the elderly person is relaxed, the analysis unit can also perform an analysis of appropriate length and generate a balanced message. This makes it possible to adjust the length of the analysis according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0077] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit prioritizes the analysis of the most recent information and quickly generates a message. The analysis unit can also postpone analysis of information that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission of information.

[0078] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information and generates an integrated message. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also optimize the order of analysis based on the relevance of information. This makes it possible to adjust the order of analysis based on the relevance of information.

[0079] The analysis unit can adjust the use of technical terms during analysis according to the elderly person's level of expertise. For example, if the elderly person has technical knowledge, the analysis unit can perform the analysis using technical terms. Also, if the elderly person does not have technical knowledge, the analysis unit can perform the analysis using simple language. Also, the analysis unit can select appropriate terms according to the elderly person's level of expertise to perform the analysis. This makes it possible to select appropriate terms according to the elderly person's level of expertise to perform the analysis.

[0080] The generation unit can estimate the emotion of the elderly person and adjust the expression method of the generated message based on the estimated emotion. For example, if the elderly person is emotional, the generation unit uses an expression method that emphasizes the emotion. Furthermore, if the elderly person is feeling nostalgic, the generation unit can also use an expression method that vividly brings back memories. Furthermore, if the elderly person is sad, the generation unit can also use a method that politely expresses the emotion. This makes it possible to adjust the expression method of the generated message depending on the emotion of the elderly. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0081] The generation unit can adjust the level of detail of the generation based on the importance of the information analyzed during generation. For example, the generation unit generates detailed information for important episodes. The generation unit can also generate simplified information for general episodes. The generation unit can also generate information that the elderly person particularly wants to emphasize, with a focus on that information. This allows the level of detail of the generation to be adjusted based on the importance of the analyzed information.

[0082] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit applies a natural language generation algorithm to text information. The generation unit can also apply an image generation algorithm to image information. The generation unit can also apply a voice generation algorithm to voice information. This makes it possible to apply the optimal generation algorithm depending on the category of information.

[0083] The generation unit can improve the accuracy of generation by referring to the elderly person's past generation results during generation. The generation unit can adjust the generation algorithm based on the elderly person's past generation results, for example. The generation unit can also extract specific patterns from the elderly person's past generation results and reflect them in the generation. The generation unit can also build a feedback loop to improve the accuracy of generation by referring to the elderly person's past generation results. This makes it possible to improve the accuracy of generation by referring to the elderly person's past generation results.

[0084] The generation unit can estimate the emotion of the elderly person and adjust the length of the message to be generated based on the estimated emotion. For example, if the elderly person is emotional, the generation unit can generate a detailed message and provide a longer message. Furthermore, if the elderly person is in a hurry, the generation unit can generate a concise message and provide a shorter message. Furthermore, if the elderly person is relaxed, the generation unit can generate a message of appropriate length and provide a balanced message. This makes it possible to adjust the length of the message to be generated according to the emotion of the elderly person. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0085] The generation unit can determine the priority of generation based on the time of submission of information at the time of generation. For example, the generation unit generates the latest information with priority and provides the message quickly. The generation unit can also postpone the generation of information that was submitted earlier. The generation unit can also adjust the generation schedule based on the time of submission. This makes it possible to determine the priority of generation based on the time of submission of information.

[0086] The generation unit can adjust the order of generation based on the relevance of the information at the time of generation. For example, the generation unit generates highly relevant information with priority and provides an integrated message. The generation unit can also postpone generation of less relevant information. The generation unit can also optimize the order of generation based on the relevance of the information. This makes it possible to adjust the order of generation based on the relevance of the information.

[0087] The generation unit can adjust the use of technical terms during generation according to the expert knowledge level of the elderly person. For example, if the elderly person has expert knowledge, the generation unit generates the text using technical terms. Also, if the elderly person does not have expert knowledge, the generation unit can generate the text using simple language. Also, the generation unit can select appropriate terms according to the expert knowledge level of the elderly person to generate the text. In this way, appropriate terms can be selected according to the expert knowledge level of the elderly person to generate the text.

[0088] The playback unit can estimate the emotion of the elderly person and adjust the expression method of the message to be played back based on the estimated emotion. For example, if the elderly person is moved, the playback unit plays back the message in an expression method that emphasizes the emotion. Furthermore, if the elderly person is feeling nostalgic, the playback unit can also play back the message in an expression method that vividly brings back memories. Furthermore, if the elderly person is sad, the playback unit can also play back the message in a way that politely expresses the emotion. This makes it possible to adjust the expression method of the message to be played back depending on the emotion of the elderly person. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0089] The playback unit can adjust the level of detail of the playback based on the importance of the message generated during playback. For example, the playback unit performs detailed playback for important episodes. The playback unit can also perform simplified playback for general episodes. The playback unit can also perform focused playback for information that the elderly person particularly wants to emphasize. In this way, the level of detail of the playback can be adjusted based on the importance of the generated message.

[0090] The playback unit can apply different playback algorithms depending on the message category during playback. For example, the playback unit applies a natural language generation algorithm to text information for playback. The playback unit can also apply an image generation algorithm to image information for playback. The playback unit can also apply a voice generation algorithm to voice information for playback. This makes it possible to apply the optimal playback algorithm depending on the message category.

[0091] The playback unit can improve the accuracy of playback by referring to the elderly person's past playback results during playback. For example, the playback unit adjusts the playback algorithm based on the elderly person's past playback results. The playback unit can also extract specific patterns from the elderly person's past playback results and reflect them in the playback. The playback unit can also build a feedback loop to improve the accuracy of playback by referring to the elderly person's past playback results. This makes it possible to improve the accuracy of playback by referring to the elderly person's past playback results.

[0092] The playback unit can estimate the emotion of the elderly person and adjust the length of the message to be played back based on the estimated emotion. For example, if the elderly person is emotional, the playback unit can play a detailed message and provide a longer message. Furthermore, if the elderly person is in a hurry, the playback unit can play a concise message and provide a shorter message. Furthermore, if the elderly person is relaxed, the playback unit can play a message of appropriate length and provide a balanced message. This makes it possible to adjust the length of the message to be played back depending on the emotion of the elderly person. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The playback unit can determine the priority of playback based on the submission time of the message during playback. For example, the playback unit prioritizes playback of the most recent message and provides it quickly. The playback unit can also postpone playback of messages that were submitted earlier. The playback unit can also adjust the playback schedule based on the submission time. This makes it possible to determine the priority of playback based on the submission time of the message.

[0094] The playback unit can adjust the playback order based on the relevance of the messages during playback. For example, the playback unit prioritizes playback of highly relevant messages to provide an integrated message. The playback unit can also postpone playback of less relevant messages. The playback unit can also optimize the playback order based on the relevance of the messages. This makes it possible to adjust the playback order based on the relevance of the messages.

[0095] The playback unit can adjust the use of technical terms during playback depending on the level of expertise of the elderly person. For example, if the elderly person has technical knowledge, the playback unit can play back using technical terms. Also, if the elderly person does not have technical knowledge, the playback unit can play back using simple language. Also, the playback unit can select appropriate terms depending on the level of expertise of the elderly person and play back. In this way, appropriate terms can be selected depending on the level of expertise of the elderly person and played back. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and playback unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect messages and episodes of the elderly person using the camera 42 and microphone 38B of the smart device 14. For example, the collection unit can also be implemented by the specific processing unit 290 of the data processing device 12 and collect information in the form of text, images, audio, and video. For example, the analysis unit can be implemented by the specific processing unit 290 of the data processing device 12 and analyze the collected information to generate an emotional message. For example, the generation unit can be implemented by the control unit 46A of the smart device 14 and play back messages in a natural voice. For example, the playback unit can be implemented by the specific processing unit 290 of the data processing device 12 and save the generated message and play it back as a message to send to relatives or friends or to oneself after a certain period of time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and playback unit, described above, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect messages and episodes of the elderly person using the camera 42 and microphone 238 of the smart glasses 214. For example, the collection unit can also be implemented by the specific processing unit 290 of the data processing device 12 and collect information in the form of text, images, audio, and video. For example, the analysis unit can be implemented by the specific processing unit 290 of the data processing device 12 and analyze the collected information to generate an emotional message. For example, the generation unit can be implemented by the control unit 46A of the smart glasses 214 and play back messages in natural voice. For example, the playback unit can be implemented by the specific processing unit 290 of the data processing device 12 and save the generated message and play it back as a message to send to relatives or friends or to oneself after a certain time has passed. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and playback unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect messages and episodes from the elderly person using the camera 42 and microphone 238 of the headset terminal 314. For example, the collection unit can also be implemented by the specific processing unit 290 of the data processing device 12 and collect information in the form of text, images, audio, and video. For example, the analysis unit can be implemented by the specific processing unit 290 of the data processing device 12 and analyze the collected information to generate an emotional message. For example, the generation unit can be implemented by the control unit 46A of the headset terminal 314 and play back messages in a natural voice. For example, the playback unit can be implemented by the specific processing unit 290 of the data processing device 12 and save the generated message and play it back as a message to send to relatives or friends or to oneself after a certain period of time. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and playback 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 messages and episodes from the elderly person using the camera 42 and microphone 238 of the robot 414. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect information in the form of text, images, audio, and video. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the collected information to generate an emotional message. For example, the generation unit can be realized by the control unit 46A of the robot 414 and play back a message in a natural voice. For example, the playback unit can be realized by the specific processing unit 290 of the data processing device 12 and save the generated message and play it back as a message to send to relatives or friends or to oneself after a certain period of time.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The collection unit can filter messages based on the elderly person's current living situation (e.g., health status or hobbies) when collecting them. For example, it can prioritize collecting episodes related to hobbies and activities that the elderly person is currently interested in. The collection unit can also select a collection method that places less strain on the elderly person, taking into consideration the elderly person's current health status. Furthermore, the collection unit can provide an appropriate collection method depending on the elderly person's living environment (home, facility, etc.). This makes it possible to collect appropriate messages based on the elderly person's current living situation and areas of interest.

[0098] The analysis unit can apply an appropriate analysis algorithm depending on the category of information during analysis. For example, a natural language processing algorithm is applied to text information. The analysis unit can also apply an image analysis algorithm to image information. Furthermore, the analysis unit can also apply a voice analysis algorithm to voice information. This makes it possible to apply the optimal analysis algorithm depending on the category of information.

[0099] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, a natural language generation algorithm is applied to text information. The generation unit can also apply an image generation algorithm to image information. Furthermore, the generation unit can also apply a voice generation algorithm to voice information. This makes it possible to apply the optimal generation algorithm depending on the category of information.

[0100] The playback unit can apply different playback algorithms depending on the message category during playback. For example, a natural language generation algorithm is applied to text information for playback. The playback unit can also apply an image generation algorithm to image information for playback. Furthermore, the playback unit can also apply a voice generation algorithm to voice information for playback. This makes it possible to apply the optimal playback algorithm depending on the message category.

[0101] The collection unit can prioritize collecting highly relevant messages by taking into account the geographical location information of the elderly person when collecting messages. For example, the collection unit can prioritize collecting memories related to the area where the elderly person currently lives. The collection unit can also prioritize collecting episodes related to places where the elderly person has lived in the past. Furthermore, the collection unit can also prioritize collecting memories related to places where the elderly person has traveled. In this way, highly relevant messages can be collected based on the geographical location information of the elderly person.

[0102] The collection unit can estimate the emotions of the elderly person and determine the priority of messages to be collected based on the estimated emotions. For example, if the elderly person is emotional, that emotion is collected first to capture the peak of the emotion. In addition, if the elderly person is feeling nostalgic, the collection unit can also collect memorable episodes first. Furthermore, if the elderly person is sad, the collection unit can also collect that emotion first to deepen the emotional expression. This makes it possible to determine the priority of messages to be collected according to the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0103] The analysis unit can estimate the emotion of the elderly person and adjust the expression method of the analysis based on the estimated emotion. For example, if the elderly person is emotional, an expression method that emphasizes the emotion is used. Furthermore, if the elderly person is feeling nostalgic, the analysis unit can also use an expression method that vividly brings back memories. Furthermore, if the elderly person is sad, the analysis unit can also use a method that politely expresses the emotion. This makes it possible to adjust the expression method of the analysis according to the emotion of the elderly person. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The generation unit can estimate the emotion of the elderly person and adjust the expression method of the generated message based on the estimated emotion. For example, if the elderly person is moved, an expression method that emphasizes the emotion is used. Furthermore, if the elderly person is feeling nostalgic, the generation unit can also use an expression method that vividly brings back memories. Furthermore, if the elderly person is sad, the generation unit can also use a method that politely expresses the emotion. This makes it possible to adjust the expression method of the generated message depending on the emotion of the elderly person. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0105] The playback unit can estimate the emotion of the elderly person and adjust the expression of the message to be played back based on the estimated emotion. For example, if the elderly person is moved, the playback unit can play back the message in an expression that emphasizes the emotion. Furthermore, if the elderly person is feeling nostalgic, the playback unit can play back the message in an expression that vividly brings back memories. Furthermore, if the elderly person is sad, the playback unit can play back the message in a way that politely expresses the emotion. This makes it possible to adjust the expression of the message to be played back depending on the emotion of the elderly person. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0106] The collection unit can estimate the elderly person's emotions and select an appropriate format for the message to be collected based on the estimated emotions. For example, if the elderly person is emotional, audio and video can be collected preferentially to enrich the expression of the emotion. In addition, if the elderly person is feeling nostalgic, the collection unit can collect a combination of old photos and text to vividly recall the memories. Furthermore, if the elderly person is sad, the collection unit can mainly collect audio and text to carefully express the emotion. This makes it possible to collect messages in the optimal format depending on the elderly person's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects messages, life lessons, and memorable episodes that the elderly want to share in the form of text, images, audio, and video. For example, the collection unit can record the elderly talking about their experiences while looking at old photos and save the audio data. The collection unit can also accept input in text format. Furthermore, the collection unit can collect images and videos. For example, the collection unit can scan old photos that the elderly have and save them as digital data. Step 2: The analysis unit analyzes the information collected by the collection unit and generates an emotional message. For example, the analysis unit analyzes old photos of elderly people and generates sentences based on the photos. The analysis unit can also learn the voice and tone of voice of elderly people and generate messages in a natural voice. Step 3: The generator reproduces the message generated by the analyzer in a natural voice. For example, the generator can learn the voice and tone of an elderly person and reproduce the message in a natural voice. Step 4: The playback unit saves the message generated by the generation unit as a message to send to relatives or friends or as a message to the user after a certain time has passed. For example, the playback unit can record a message for an elderly person to send to their grandchild and play the message when the grandchild becomes an adult.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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. The collection department collects messages, life lessons, and memorable stories that seniors want to share in the form of text, images, audio, and video. an analysis unit that analyzes the information collected by the collection unit and generates an emotional message; a generating unit that reproduces the message generated by the analyzing unit in a natural voice; a playback unit that saves the message generated by the generation unit as a message to send to relatives or friends or as a message to oneself after a certain time has passed. A system characterized by:

2. The collecting unit Estimate the emotions of elderly people and select the appropriate format of messages to collect based on the estimated emotions.

2. The system of claim 1.

3. The collecting unit Analyze the elderly's past message collection history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit At the time of collection, filtering is performed based on the elderly person's current living situation (e.g., health status, hobbies, etc.).

2. The system of claim 1.

5. The collecting unit Select appropriate collection methods according to the elderly's input method at the time of collection.

2. The system of claim 1.

6. The collecting unit Estimate the emotions of elderly people and prioritize messages to be collected based on the estimated emotions.

2. The system of claim 1.

7. The collecting unit Taking into account the elderly person's geographic location information at the time of collection, messages that are highly relevant (e.g., information about nearby events) are collected preferentially.

2. The system of claim 1.

8. The collecting unit Analyze seniors' social media activity at the time of collection and collect relevant messages 2. The system of claim 1.

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