System

Abstract

To provide a system capable of analyzing a voice tone and laughter of a user, providing appropriate video, and monitoring the health status of the parent.SOLUTION: A system according to an embodiment comprises a voice analysis unit, a video provision unit, and a health monitoring unit. The voice analysis unit analyzes a voice tone or laughter of a user. The video provision unit provides a video on the basis of the analysis results of the voice analysis unit. The health monitoring unit monitors the health status of the parent on the basis of the analysis results of the voice analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Previous technology had the problem that it was not sufficient to analyze the tone of a user's voice or laughter to provide appropriate images, or to monitor the health of parents.

[0005] The system according to the embodiment aims to analyze the tone of a user's voice and laughter, provide appropriate images, and monitor the health of the parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an audio analysis unit, a video providing unit, and a health monitoring unit. The audio analysis unit analyzes the tone of a user's voice or laughter. The video providing unit provides a video based on the result of the analysis by the audio analysis unit. The health monitoring unit monitors the health of the parents based on the result of the analysis by the audio analysis unit. Effect of the Invention

[0007] The system according to the embodiment can analyze the tone of a user's voice and laughter, provide appropriate images, and monitor the health of parents. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The laughter providing system according to the embodiment of the present invention is a system that analyzes the tone of a user's voice and laughter, provides optimal images, and monitors the health of the parents. This laughter providing system learns where the user laughs, the tone of voice, and the laughter through conversations and images with the user's My character, and provides optimal laughter. In addition, a monitoring mode is also provided, and the health of the parents can be grasped remotely. For example, the user starts a conversation with My character. My character analyzes the tone of the user's voice and laughter to learn what makes the user laugh. For example, if the user laughs at a joke, it learns the content and timing of the joke, and provides the joke at the optimal timing in the next conversation. Next, My character provides a video to the user. The video is composed of content that makes the user laugh easily, and the user's reaction is analyzed in real time. For example, if the user laughs at a video of an animal, it learns the content and timing of the video, and reflects it in the video provided next time. Furthermore, in the monitoring mode, the health of the user's parents can be grasped remotely. My character analyzes the tone of the user's voice and laughter to monitor the health of the parents. For example, if the frequency of a parent's laughter decreases, the system will notify the user that there may be something wrong with their health. This service provides laughter to users anytime and anywhere, which helps with stress relief, cognitive prevention, and disease prevention. In addition, the monitoring mode allows users to remotely monitor their parents' health conditions, allowing them to live with peace of mind. This allows the laughter providing system to analyze the user's tone of voice and laughter, provide optimal images, and monitor the health of their parents.

[0029] The laughter providing system according to the embodiment includes a voice analysis unit, a video providing unit, and a health monitoring unit. The voice analysis unit analyzes the tone of the user's voice or laughter. For example, the voice analysis unit can analyze the pitch, strength, rhythm, etc. of the user's voice to estimate the user's emotional state. The voice analysis unit can also analyze the type of laughter and sound characteristics of the user's laughter and learn what makes the user laugh. For example, when the user laughs at a joke, the voice analysis unit can learn the content and timing of the joke and provide the joke at the optimal timing in the next and subsequent conversations. The video providing unit provides video based on the results of the analysis by the voice analysis unit. For example, the video providing unit can provide video with content that is easy for the user to laugh at. The video providing unit can analyze the user's reaction in real time and adjust the content and timing of the video. For example, when the user laughs at a video of an animal, the video providing unit can learn the content and timing of the video and reflect it in the video provided next and subsequent conversations. The health monitoring unit monitors the health of the parents based on the results of the analysis by the voice analysis unit. For example, the health monitoring unit can analyze the tone of the parents' voices and laughter to monitor their health conditions. If the frequency of the parents' laughter decreases, the health monitoring unit can notify the user that there may be something wrong with their health. In this way, the laughter providing system according to the embodiment can analyze the user's tone of voice and laughter, provide optimal images, and monitor the health conditions of the parents.

[0030] The voice analysis unit analyzes the tone of the user's voice or laughter. Specifically, the voice analysis unit uses advanced voice recognition technology to analyze voice features such as the pitch, strength, and rhythm of the user's voice in detail. This makes it possible to estimate the user's emotional state. For example, if the user's voice is high and the rhythm is fast, it may indicate emotions of excitement or joy. On the other hand, if the user's voice is low and the rhythm is slow, it may indicate emotions of calmness or sadness. In addition, the voice analysis unit can analyze the type and sound characteristics of the user's laughter and learn what makes the user laugh. For example, if the user laughs at a joke, it can learn the content and timing of the joke and provide the joke at the optimal timing in the next or subsequent conversation. Furthermore, the voice analysis unit can analyze the pattern of the user's laughter and evaluate the strength and duration of the laughter. This makes it possible to quantitatively evaluate how much the user is enjoying themselves. The voice analysis unit accumulates these data and learns the laughter tendencies and preferences of each user, making it possible to provide more personalized laughter.

[0031] The video providing unit provides the video based on the results of the analysis by the audio analysis unit. Specifically, the video providing unit can select and provide a video with content that is likely to make the user laugh. For example, if the user has a tendency to laugh at videos of animals, the video providing unit preferentially provides funny videos of animals. The video providing unit can also analyze the user's reaction in real time and adjust the content and timing of the video. For example, if the user laughs at a particular scene, the video providing unit can learn the characteristics of that scene and reflect them in the video provided from the next time onwards. Furthermore, the video providing unit can analyze the user's laughing pattern and optimize the playback speed of the video and the timing of scene switching. This makes it possible to provide a video experience that is more enjoyable for the user. The video providing unit can continue to learn the user's preferences and reactions, and can always provide optimal videos based on the latest information. For example, if the user likes videos of a particular genre, the video providing unit can preferentially provide videos of that genre, thereby improving the user's satisfaction.

[0032] The health monitoring unit monitors the health of the parents based on the results of the analysis by the voice analysis unit. Specifically, the health monitoring unit can analyze the tone of the parents' voices and laughter to monitor their health. For example, if the tone of the parents' voices is lower than usual and they laugh less, there may be an abnormality in their health, and the user can be notified. The health monitoring unit can periodically record the frequency and intensity of the parents' laughter to track changes in their long-term health. This allows for early detection of abnormalities in their health and appropriate measures to be taken. Furthermore, the health monitoring unit can analyze the patterns of the parents' laughter to evaluate their stress levels and mental health. For example, if the frequency of laughter decreases and the laughter becomes weaker, this may indicate signs of stress or depression. The health monitoring unit can provide specific advice and support to the user based on these data. For example, if the health of the parents is deteriorating, a notification recommending that they visit a medical institution can be sent. This allows the health monitoring unit to continuously monitor the health of the parents, detect abnormalities early, and support appropriate responses.

[0033] The voice analysis unit may include a learning unit that analyzes the user's tone of voice or laughter and learns the user's laughter pattern. The voice analysis unit may, for example, analyze the user's tone of voice or laughter and learn the user's laughter pattern. For example, the voice analysis unit may analyze the frequency, duration, sound characteristics, etc. of the user's laughter and learn what makes the user laugh. In this way, the voice analysis unit can provide more appropriate laughter by learning the user's laughter pattern. A part or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit may input data of the user's tone of voice or laughter to the generation AI and cause the generation AI to learn the user's laughter pattern.

[0034] The video providing unit can provide a video with content that is easy for the user to laugh at. The video providing unit can provide a video with content that is easy for the user to laugh at, for example. For example, the video providing unit can provide a video of comedy, humor, jokes, etc. The video providing unit can analyze the user's reaction in real time and adjust the content and timing of the video. For example, when the user laughs at a video of an animal, the video providing unit can learn the content and timing of the video and reflect it in the video provided next time and thereafter. In this way, the video providing unit can promote laughter by providing a video with content that is easy for the user to laugh at. A part or all of the above-mentioned processing in the video providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the video providing unit can input the user's reaction data to the generation AI and cause the generation AI to adjust the content and timing of the video.

[0035] The health monitoring unit can analyze the tone of the voices or laughter of the parents and monitor the health condition. The health monitoring unit can, for example, analyze the tone of the voices or laughter of the parents and monitor the health condition. For example, the health monitoring unit can analyze the pitch, strength, rhythm, etc. of the parents' voices to estimate their health condition. The health monitoring unit can also analyze the type and sound characteristics of the parents' laughter and monitor their health condition. For example, if the frequency at which the parents laugh has decreased, the health monitoring unit can notify the user that there may be an abnormality in their health condition. In this way, the health monitoring unit can support health management by monitoring the health condition of the parents. A part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the health monitoring unit can input data on the tone of the voices and laughter of the parents to the generation AI and cause the generation AI to monitor the health condition.

[0036] The health monitoring unit may include a notification unit that notifies the user when there is an abnormality in the health state. The health monitoring unit may include, for example, a notification unit that notifies the user when there is an abnormality in the health state. For example, the health monitoring unit may analyze the tone of voice and laughter of the parents and notify the user when there is an abnormality in the health state. The notification unit may notify the user of, for example, the specific content of the abnormality and a method of dealing with it. As a result, the health monitoring unit notifies the user when there is an abnormality in the health state, thereby enabling early response. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the health monitoring unit may input data of the tone of voice and laughter of the parents to the generation AI and cause the generation AI to execute monitoring of the health state.

[0037] The voice analysis unit can analyze the user's past voice data and select the optimal analysis algorithm. The voice analysis unit can, for example, analyze the user's past voice data and select the optimal analysis algorithm. For example, the voice analysis unit can analyze the voice data of the user laughing in the past and select the optimal analysis algorithm. In addition, the voice analysis unit can analyze the reaction to a specific emotion from the user's past voice data and adjust the algorithm. Furthermore, the voice analysis unit can analyze the emotion fluctuation pattern based on the user's past voice data and select the optimal algorithm. Thereby, the voice analysis unit can select the optimal analysis algorithm by analyzing the past voice data. Part or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's past voice data to the generation AI and cause the generation AI to select the optimal analysis algorithm.

[0038] The voice analysis unit can improve the analysis accuracy by filtering the user's current environmental sound during voice analysis. The voice analysis unit can improve the analysis accuracy by filtering the user's current environmental sound during voice analysis. For example, the voice analysis unit can improve the analysis accuracy by filtering the environmental sound when the user is in a noisy environment. In addition, the voice analysis unit can filter the environmental sound to perform detailed analysis when the user is in a quiet environment. Furthermore, the voice analysis unit can filter the environmental sound to maintain the analysis accuracy when the user is moving. Thereby, the voice analysis unit can improve the analysis accuracy by filtering the environmental sound. Part or all of the above-mentioned processing in the voice analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the voice analysis unit can input the user's environmental sound data to the generation AI and cause the generation AI to execute filtering of the environmental sound.

[0039] The voice analysis unit can customize the analysis result by considering the geographical location information of the user during the voice analysis. The voice analysis unit can customize the analysis result by considering the geographical location information of the user during the voice analysis. For example, when the user is in a specific area, the voice analysis unit can customize the analysis result by considering the characteristics of the area. Furthermore, when the user is traveling, the voice analysis unit can customize the analysis result by considering the characteristics of the travel destination. Furthermore, when the user is at home, the voice analysis unit can customize the analysis result by considering the home environment. Thereby, the voice analysis unit can customize the analysis result by considering the geographical location information. Part or all of the above-mentioned processing in the voice analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice analysis unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute customization of the analysis result.

[0040] The voice analysis unit can analyze the social media activity of the user during the voice analysis and obtain related voice data. The voice analysis unit can, for example, analyze the social media activity of the user during the voice analysis and obtain related voice data. For example, the voice analysis unit can analyze the voice data shared by the user on social media and obtain related information. In addition, the voice analysis unit can obtain voice data related to a specific emotion from the social media activity of the user. Furthermore, the voice analysis unit can analyze the voice data of the account that the user follows on social media and obtain related information. In this way, the voice analysis unit can obtain related voice data by analyzing the social media activity. A part or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute the acquisition of related voice data.

[0041] The video providing unit can select the optimal video by referring to the user's past viewing history when providing the video. The video providing unit can select the optimal video by referring to the user's past viewing history, for example, when providing the video. For example, the video providing unit can select the optimal video by referring to the history of videos that the user has viewed in the past. In addition, the video providing unit can select a video of a specific genre from the user's past viewing history. Furthermore, the video providing unit can select a video that matches the viewing tendency based on the user's past viewing history. Thereby, the video providing unit can select the optimal video by referring to the past viewing history. A part or all of the above-mentioned processing in the video providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the video providing unit can input the user's viewing history data to the generation AI and cause the generation AI to select the optimal video.

[0042] The video providing unit can adjust the timing of the video based on the user's current activity status when providing the video. The video providing unit can adjust the timing of the video based on the user's current activity status when providing the video. For example, the video providing unit can adjust the playback timing of the video when the user is relaxing. In addition, the video providing unit can provide a short video when the user is busy. Furthermore, the video providing unit can provide a longer video when the user is taking a break. Thereby, the video providing unit can provide the video at a more appropriate timing by adjusting the timing of the video based on the current activity status. A part or all of the above-mentioned processing in the video providing unit may be performed, for example, using AI or may be performed without using AI. For example, the video providing unit can input the user's activity status data to the generation AI and cause the generation AI to adjust the timing of the video.

[0043] The video providing unit can provide highly relevant video by considering the geographical location information of the user when providing the video. The video providing unit can provide highly relevant video by considering the geographical location information of the user when providing the video. For example, when the user is in a specific area, the video providing unit can provide video related to the area. Furthermore, when the user is traveling, the video providing unit can provide video related to the travel destination. Furthermore, when the user is at home, the video providing unit can provide video related to the home environment. Thereby, the video providing unit can provide highly relevant video by considering the geographical location information. A part or all of the above-mentioned processing in the video providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the video providing unit can input the geographical location information data of the user to the generation AI and cause the generation AI to provide highly relevant video.

[0044] The video providing unit can analyze the social media activity of the user and provide related videos when providing the video. The video providing unit can, for example, analyze the social media activity of the user and provide related videos when providing the video. For example, the video providing unit can refer to the video shared by the user on social media and provide related videos. In addition, the video providing unit can provide videos of a specific genre from the user's social media activity. Furthermore, the video providing unit can refer to the video of the account the user follows on social media and provide related videos. In this way, the video providing unit can provide related videos by analyzing social media activities. A part or all of the above-mentioned processing in the video providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the video providing unit can input the user's social media activity data to the generation AI and cause the generation AI to provide related videos.

[0045] The health monitoring unit can improve the accuracy of abnormality detection by referring to past health data during health monitoring. The health monitoring unit can improve the accuracy of abnormality detection by referring to past health data during health monitoring, for example. For example, the health monitoring unit can improve the accuracy of abnormality detection by referring to the user's past health data. In addition, the health monitoring unit can analyze a specific pattern from the user's past health data and improve the accuracy of abnormality detection. Furthermore, the health monitoring unit can perform early detection of abnormalities based on the user's past health data. Thereby, the health monitoring unit can improve the accuracy of abnormality detection by referring to the past health data. A part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the health monitoring unit can input the user's past health data to the generation AI and cause the generation AI to execute improvement of the accuracy of abnormality detection.

[0046] The health monitoring unit can perform monitoring in consideration of the lifestyle data of the user during health monitoring. The health monitoring unit can perform monitoring in consideration of the lifestyle data of the user during health monitoring, for example. For example, the health monitoring unit can improve the accuracy of monitoring by referring to the lifestyle data of the user. In addition, the health monitoring unit can analyze a specific pattern from the lifestyle data of the user and perform monitoring. Furthermore, the health monitoring unit can perform early detection of abnormalities based on the lifestyle data of the user. As a result, the health monitoring unit can perform more accurate monitoring by taking the lifestyle data into consideration. A part or all of the above-mentioned processing in the health monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the health monitoring unit can input the lifestyle data of the user to the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0047] The health monitoring unit can perform monitoring while taking into consideration the geographical location information of the user during health monitoring. The health monitoring unit can perform monitoring while taking into consideration the geographical location information of the user during health monitoring, for example. For example, when the user is in a specific area, the health monitoring unit can perform monitoring while taking into consideration the characteristics of the area. In addition, when the user is traveling, the health monitoring unit can perform monitoring while taking into consideration the characteristics of the travel destination. Furthermore, when the user is at home, the health monitoring unit can perform monitoring while taking into consideration the home environment. This allows the health monitoring unit to perform more accurate monitoring by taking into consideration the geographical location information. Part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the health monitoring unit can input the geographical location information data of the user to the generation AI and cause the generation AI to perform monitoring.

[0048] The health monitoring unit can analyze the social media activity of the user during health monitoring and acquire related health data. The health monitoring unit can, for example, analyze the social media activity of the user during health monitoring and acquire related health data. For example, the health monitoring unit can refer to health data shared by the user on social media and acquire related information. The health monitoring unit can also acquire data related to a specific health condition from the user's social media activity. Furthermore, the health monitoring unit can refer to health data of accounts the user follows on social media and acquire related information. In this way, the health monitoring unit can acquire related health data by analyzing social media activity. A part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the health monitoring unit can input the user's social media activity data to the generation AI and cause the generation AI to execute acquisition of related health data.

[0049] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit can optimize the learning algorithm by referring to past learning data, for example, during learning. For example, the learning unit can select an optimal learning algorithm by referring to the user's past learning data. In addition, the learning unit can analyze a specific pattern from the user's past learning data and adjust the algorithm. Furthermore, the learning unit can select an algorithm that improves the efficiency of learning based on the user's past learning data. Thereby, the learning unit can optimize the learning algorithm by referring to the past learning data. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input the user's past learning data to the generation AI and cause the generation AI to execute optimization of the learning algorithm.

[0050] The learning unit can weight the learning data based on the time of submission of the voice data during learning. The learning unit can weight the learning data based on the time of submission of the voice data during learning. For example, the learning unit can weight the learning data based on the time of the voice data submitted by the user. Also, the learning unit can preferentially learn the voice data submitted by the user in a specific time period. Furthermore, the learning unit can weight the learning data based on the frequency of the voice data submitted by the user. Thereby, the learning unit can weight the learning data based on the time of submission of the voice data, thereby enabling more appropriate learning. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of submission of the voice data of the user to the generation AI and cause the generation AI to execute weighting of the learning data.

[0051] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit can select the optimal notification method by referring to the user's past notification history, for example, at the time of notification. For example, the notification unit can select the optimal notification method by referring to the user's past notification history. In addition, the notification unit can analyze a specific pattern from the user's past notification history and adjust the notification method. Furthermore, the notification unit can select a method for improving the efficiency of notifications based on the user's past notification history. Thereby, the notification unit can select the optimal notification method by referring to the past notification history. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's notification history data to the generation AI and cause the generation AI to select the optimal notification method.

[0052] The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification. The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification, for example. For example, when the user is in a specific area, the notification unit can select the notification method in consideration of the characteristics of the area. Furthermore, when the user is traveling, the notification unit can select the notification method in consideration of the characteristics of the travel destination. Furthermore, when the user is at home, the notification unit can select the notification method in consideration of the home environment. Thereby, the notification unit can select the optimal notification method by considering the geographical location information. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the optimal notification method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The laughter providing system may further include an activity analysis unit that analyzes the user's activity history. The activity analysis unit may collect the user's daily activity data and learn the user's activity patterns. For example, the activity analysis unit may analyze the time periods when the user exercises and takes rests, and provide optimal timing for laughter based on the analysis. The activity analysis unit may also adjust the intensity and frequency of laughter according to the user's activity level. Furthermore, the activity analysis unit may identify time periods when stress is high based on the user's activity history, and provide relaxing laughter during those time periods. This allows the laughter providing system to provide optimal laughter according to the user's activity status.

[0055] The voice analysis unit can take into account the user's physical condition data when analyzing the user's tone of voice or laughter. For example, the voice analysis unit can acquire physical condition data such as the user's heart rate and blood pressure, and adjust the accuracy of the analysis based on the data. If the user is in poor health, the accuracy of the analysis can be improved to perform a detailed emotion analysis. Also, if the user is in good health, the accuracy of the analysis can be adjusted to provide results quickly. Furthermore, the voice analysis unit can quickly capture emotional fluctuations based on the user's physical condition data. This allows the voice analysis unit to perform a more accurate analysis by adjusting the accuracy of the analysis based on the user's physical condition.

[0056] The health monitoring unit can perform health monitoring by taking into account the lifestyle habit data of the user. For example, the health monitoring unit can improve the accuracy of monitoring by referring to the lifestyle habit data of the user. The health monitoring unit can also analyze specific patterns from the lifestyle habit data of the user and perform monitoring. Furthermore, the health monitoring unit can perform early detection of abnormalities based on the lifestyle habit data of the user. This allows the health monitoring unit to perform more accurate monitoring by taking into account the lifestyle habit data.

[0057] The voice analysis unit can improve the analysis accuracy by filtering the user's current environmental sound during voice analysis. For example, when the user is in a noisy environment, the voice analysis unit can improve the analysis accuracy by filtering the environmental sound. Also, when the user is in a quiet environment, the voice analysis unit can filter the environmental sound to perform a detailed analysis. Furthermore, when the user is moving, the voice analysis unit can filter the environmental sound to maintain the analysis accuracy. As a result, the voice analysis unit can improve the analysis accuracy by filtering the environmental sound.

[0058] When providing a video, the video providing unit can select the optimal video by referring to the user's past viewing history. For example, the video providing unit can select the optimal video by referring to the history of videos the user has viewed in the past. The video providing unit can also select a video of a specific genre from the user's past viewing history. Furthermore, the video providing unit can select a video that matches the viewing tendency based on the user's past viewing history. This allows the video providing unit to select the optimal video by referring to the past viewing history.

[0059] At the time of notification, the notification unit can select the optimal notification method by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, the notification unit can select the notification method by taking into consideration the characteristics of the area. Furthermore, when the user is traveling, the notification unit can select the notification method by taking into consideration the characteristics of the travel destination. Furthermore, when the user is at home, the notification unit can select the notification method by taking into consideration the home environment. In this way, the notification unit can select the optimal notification method by taking into consideration the geographical location information.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The voice analysis unit analyzes the tone of the user's voice or laughter. For example, the voice analysis unit can analyze the pitch, strength, rhythm, etc. of the user's voice to estimate the user's emotional state. The voice analysis unit can also analyze the type and sound characteristics of the user's laughter to learn what makes the user laugh. For example, if the user laughs at a joke, the voice analysis unit can learn the content and timing of that joke and provide a joke at the optimal timing in the next and subsequent conversations. Step 2: The video providing unit provides video based on the results of the analysis by the audio analysis unit. For example, the video providing unit can provide video with content that is likely to make the user laugh. The video providing unit can analyze the user's reaction in real time and adjust the content and timing of the video. For example, if the user laughs at a video of an animal, the video providing unit can learn the content and timing of that video and reflect it in subsequent video provision. Step 3: The health monitoring unit monitors the health of the parents based on the results of the analysis by the voice analysis unit. For example, the health monitoring unit can analyze the tone of the parents' voices and laughter to monitor their health. If the frequency at which the parents laugh has decreased, the health monitoring unit can notify the user that there may be something wrong with their health.

[0062] (Example 2) The laughter providing system according to the embodiment of the present invention is a system that analyzes the tone of a user's voice and laughter, provides optimal images, and monitors the health of the parents. This laughter providing system learns where the user laughs, the tone of voice, and the laughter through conversations and images with the user's My character, and provides optimal laughter. In addition, a monitoring mode is also provided, and the health of the parents can be grasped remotely. For example, the user starts a conversation with My character. My character analyzes the tone of the user's voice and laughter to learn what makes the user laugh. For example, if the user laughs at a joke, it learns the content and timing of the joke, and provides the joke at the optimal timing in the next conversation. Next, My character provides a video to the user. The video is composed of content that makes the user laugh easily, and the user's reaction is analyzed in real time. For example, if the user laughs at a video of an animal, it learns the content and timing of the video, and reflects it in the video provided next time. Furthermore, in the monitoring mode, the health of the user's parents can be grasped remotely. My character analyzes the tone of the user's voice and laughter to monitor the health of the parents. For example, if the frequency of a parent's laughter decreases, the system will notify the user that there may be something wrong with their health. This service provides laughter to users anytime and anywhere, which helps with stress relief, cognitive prevention, and disease prevention. In addition, the monitoring mode allows users to remotely monitor their parents' health conditions, allowing them to live with peace of mind. This allows the laughter providing system to analyze the user's tone of voice and laughter, provide optimal images, and monitor the health of their parents.

[0063] The laughter providing system according to the embodiment includes a voice analysis unit, a video providing unit, and a health monitoring unit. The voice analysis unit analyzes the tone of the user's voice or laughter. For example, the voice analysis unit can analyze the pitch, strength, rhythm, etc. of the user's voice to estimate the user's emotional state. The voice analysis unit can also analyze the type of laughter and sound characteristics of the user's laughter and learn what makes the user laugh. For example, when the user laughs at a joke, the voice analysis unit can learn the content and timing of the joke and provide the joke at the optimal timing in the next and subsequent conversations. The video providing unit provides video based on the results of the analysis by the voice analysis unit. For example, the video providing unit can provide video with content that is easy for the user to laugh at. The video providing unit can analyze the user's reaction in real time and adjust the content and timing of the video. For example, when the user laughs at a video of an animal, the video providing unit can learn the content and timing of the video and reflect it in the video provided next and subsequent conversations. The health monitoring unit monitors the health of the parents based on the results of the analysis by the voice analysis unit. For example, the health monitoring unit can analyze the tone of the parents' voices and laughter to monitor their health conditions. If the frequency of the parents' laughter decreases, the health monitoring unit can notify the user that there may be something wrong with their health. In this way, the laughter providing system according to the embodiment can analyze the user's tone of voice and laughter, provide optimal images, and monitor the health conditions of the parents.

[0064] The voice analysis unit analyzes the tone of the user's voice or laughter. Specifically, the voice analysis unit uses advanced voice recognition technology to analyze voice features such as the pitch, strength, and rhythm of the user's voice in detail. This makes it possible to estimate the user's emotional state. For example, if the user's voice is high and the rhythm is fast, it may indicate emotions of excitement or joy. On the other hand, if the user's voice is low and the rhythm is slow, it may indicate emotions of calmness or sadness. In addition, the voice analysis unit can analyze the type and sound characteristics of the user's laughter and learn what makes the user laugh. For example, if the user laughs at a joke, it can learn the content and timing of the joke and provide the joke at the optimal timing in the next or subsequent conversation. Furthermore, the voice analysis unit can analyze the pattern of the user's laughter and evaluate the strength and duration of the laughter. This makes it possible to quantitatively evaluate how much the user is enjoying themselves. The voice analysis unit accumulates these data and learns the laughter tendencies and preferences of each user, making it possible to provide more personalized laughter.

[0065] The video providing unit provides the video based on the results of the analysis by the audio analysis unit. Specifically, the video providing unit can select and provide a video with content that is likely to make the user laugh. For example, if the user has a tendency to laugh at videos of animals, the video providing unit preferentially provides funny videos of animals. The video providing unit can also analyze the user's reaction in real time and adjust the content and timing of the video. For example, if the user laughs at a particular scene, the video providing unit can learn the characteristics of that scene and reflect them in the video provided from the next time onwards. Furthermore, the video providing unit can analyze the user's laughing pattern and optimize the playback speed of the video and the timing of scene switching. This makes it possible to provide a video experience that is more enjoyable for the user. The video providing unit can continue to learn the user's preferences and reactions, and can always provide optimal videos based on the latest information. For example, if the user likes videos of a particular genre, the video providing unit can preferentially provide videos of that genre, thereby improving the user's satisfaction.

[0066] The health monitoring unit monitors the health of the parents based on the results of the analysis by the voice analysis unit. Specifically, the health monitoring unit can analyze the tone of the parents' voices and laughter to monitor their health. For example, if the tone of the parents' voices is lower than usual and they laugh less, there may be an abnormality in their health, and the user can be notified. The health monitoring unit can periodically record the frequency and intensity of the parents' laughter to track changes in their long-term health. This allows for early detection of abnormalities in their health and appropriate measures to be taken. Furthermore, the health monitoring unit can analyze the patterns of the parents' laughter to evaluate their stress levels and mental health. For example, if the frequency of laughter decreases and the laughter becomes weaker, this may indicate signs of stress or depression. The health monitoring unit can provide specific advice and support to the user based on these data. For example, if the health of the parents is deteriorating, a notification recommending that they visit a medical institution can be sent. This allows the health monitoring unit to continuously monitor the health of the parents, detect abnormalities early, and support appropriate responses.

[0067] The voice analysis unit may include a learning unit that analyzes the user's tone of voice or laughter and learns the user's laughter pattern. The voice analysis unit may, for example, analyze the user's tone of voice or laughter and learn the user's laughter pattern. For example, the voice analysis unit may analyze the frequency, duration, sound characteristics, etc. of the user's laughter and learn what makes the user laugh. In this way, the voice analysis unit can provide more appropriate laughter by learning the user's laughter pattern. A part or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit may input data of the user's tone of voice or laughter to the generation AI and cause the generation AI to learn the user's laughter pattern.

[0068] The video providing unit can provide a video with content that is easy for the user to laugh at. The video providing unit can provide a video with content that is easy for the user to laugh at, for example. For example, the video providing unit can provide a video of comedy, humor, jokes, etc. The video providing unit can analyze the user's reaction in real time and adjust the content and timing of the video. For example, when the user laughs at a video of an animal, the video providing unit can learn the content and timing of the video and reflect it in the video provided next time and thereafter. In this way, the video providing unit can promote laughter by providing a video with content that is easy for the user to laugh at. A part or all of the above-mentioned processing in the video providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the video providing unit can input the user's reaction data to the generation AI and cause the generation AI to adjust the content and timing of the video.

[0069] The health monitoring unit can analyze the tone of the voices or laughter of the parents and monitor the health condition. The health monitoring unit can, for example, analyze the tone of the voices or laughter of the parents and monitor the health condition. For example, the health monitoring unit can analyze the pitch, strength, rhythm, etc. of the parents' voices to estimate their health condition. The health monitoring unit can also analyze the type and sound characteristics of the parents' laughter and monitor their health condition. For example, if the frequency at which the parents laugh has decreased, the health monitoring unit can notify the user that there may be an abnormality in their health condition. In this way, the health monitoring unit can support health management by monitoring the health condition of the parents. A part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the health monitoring unit can input data on the tone of the voices and laughter of the parents to the generation AI and cause the generation AI to monitor the health condition.

[0070] The health monitoring unit may include a notification unit that notifies the user when there is an abnormality in the health state. The health monitoring unit may include, for example, a notification unit that notifies the user when there is an abnormality in the health state. For example, the health monitoring unit may analyze the tone of voice and laughter of the parents and notify the user when there is an abnormality in the health state. The notification unit may notify the user of, for example, the specific content of the abnormality and a method of dealing with it. As a result, the health monitoring unit notifies the user when there is an abnormality in the health state, thereby enabling early response. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the health monitoring unit may input data of the tone of voice and laughter of the parents to the generation AI and cause the generation AI to execute monitoring of the health state.

[0071] The voice analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. The voice analysis unit can, for example, estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the voice analysis unit can increase the accuracy of the analysis to perform detailed emotion analysis. In addition, when the user is stressed, the voice analysis unit can adjust the accuracy of the analysis to quickly provide a result. Furthermore, when the user is excited, the voice analysis unit can adjust the accuracy of the analysis to quickly capture the fluctuation of the emotion. Thereby, the voice analysis unit can adjust the accuracy of the analysis based on the user's emotion to perform a more accurate analysis. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice analysis unit can input data on the user's tone of voice or laughter into the generation AI, allowing the generation AI to estimate emotions.

[0072] The voice analysis unit can analyze the user's past voice data and select the optimal analysis algorithm. The voice analysis unit can, for example, analyze the user's past voice data and select the optimal analysis algorithm. For example, the voice analysis unit can analyze the voice data of the user laughing in the past and select the optimal analysis algorithm. In addition, the voice analysis unit can analyze the reaction to a specific emotion from the user's past voice data and adjust the algorithm. Furthermore, the voice analysis unit can analyze the emotion fluctuation pattern based on the user's past voice data and select the optimal algorithm. Thereby, the voice analysis unit can select the optimal analysis algorithm by analyzing the past voice data. Part or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the user's past voice data to the generation AI and cause the generation AI to select the optimal analysis algorithm.

[0073] The voice analysis unit can improve the analysis accuracy by filtering the user's current environmental sound during voice analysis. The voice analysis unit can improve the analysis accuracy by filtering the user's current environmental sound during voice analysis. For example, the voice analysis unit can improve the analysis accuracy by filtering the environmental sound when the user is in a noisy environment. In addition, the voice analysis unit can filter the environmental sound to perform detailed analysis when the user is in a quiet environment. Furthermore, the voice analysis unit can filter the environmental sound to maintain the analysis accuracy when the user is moving. Thereby, the voice analysis unit can improve the analysis accuracy by filtering the environmental sound. Part or all of the above-mentioned processing in the voice analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the voice analysis unit can input the user's environmental sound data to the generation AI and cause the generation AI to execute filtering of the environmental sound.

[0074] The voice analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The voice analysis unit can, for example, estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. For example, the voice analysis unit can provide detailed analysis results preferentially when the user is relaxed. In addition, the voice analysis unit can provide quick analysis results preferentially when the user is stressed. Furthermore, the voice analysis unit can provide analysis results related to emotional fluctuations preferentially when the user is excited. Thereby, the voice analysis unit can provide more appropriate results by determining the priority of the analysis results based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice analysis unit can input data on the user's tone of voice or laughter into the generation AI, allowing the generation AI to estimate emotions.

[0075] The voice analysis unit can customize the analysis result by considering the geographical location information of the user during the voice analysis. The voice analysis unit can customize the analysis result by considering the geographical location information of the user during the voice analysis. For example, when the user is in a specific area, the voice analysis unit can customize the analysis result by considering the characteristics of the area. Furthermore, when the user is traveling, the voice analysis unit can customize the analysis result by considering the characteristics of the travel destination. Furthermore, when the user is at home, the voice analysis unit can customize the analysis result by considering the home environment. Thereby, the voice analysis unit can customize the analysis result by considering the geographical location information. Part or all of the above-mentioned processing in the voice analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the voice analysis unit can input the geographical location information data of the user to the generation AI and cause the generation AI to execute customization of the analysis result.

[0076] The voice analysis unit can analyze the social media activity of the user during the voice analysis and obtain related voice data. The voice analysis unit can, for example, analyze the social media activity of the user during the voice analysis and obtain related voice data. For example, the voice analysis unit can analyze the voice data shared by the user on social media and obtain related information. In addition, the voice analysis unit can obtain voice data related to a specific emotion from the social media activity of the user. Furthermore, the voice analysis unit can analyze the voice data of the account that the user follows on social media and obtain related information. In this way, the voice analysis unit can obtain related voice data by analyzing the social media activity. A part or all of the above-mentioned processing in the voice analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice analysis unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute the acquisition of related voice data.

[0077] The video providing unit can estimate the user's emotion and adjust the content of the video based on the estimated user's emotion. The video providing unit can, for example, estimate the user's emotion and adjust the content of the video based on the estimated user's emotion. For example, the video providing unit can provide a calm video when the user is relaxed. Furthermore, the video providing unit can provide a relaxing video when the user is stressed. Furthermore, the video providing unit can provide an energetic video when the user is excited. Thereby, the video providing unit can provide a more appropriate video by adjusting the content of the video based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the video providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the video providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the video.

[0078] The video providing unit can select the optimal video by referring to the user's past viewing history when providing the video. The video providing unit can select the optimal video by referring to the user's past viewing history, for example, when providing the video. For example, the video providing unit can select the optimal video by referring to the history of videos that the user has viewed in the past. In addition, the video providing unit can select a video of a specific genre from the user's past viewing history. Furthermore, the video providing unit can select a video that matches the viewing tendency based on the user's past viewing history. Thereby, the video providing unit can select the optimal video by referring to the past viewing history. A part or all of the above-mentioned processing in the video providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the video providing unit can input the user's viewing history data to the generation AI and cause the generation AI to select the optimal video.

[0079] The video providing unit can adjust the timing of the video based on the user's current activity status when providing the video. The video providing unit can adjust the timing of the video based on the user's current activity status when providing the video. For example, the video providing unit can adjust the playback timing of the video when the user is relaxing. In addition, the video providing unit can provide a short video when the user is busy. Furthermore, the video providing unit can provide a longer video when the user is taking a break. Thereby, the video providing unit can provide the video at a more appropriate timing by adjusting the timing of the video based on the current activity status. A part or all of the above-mentioned processing in the video providing unit may be performed, for example, using AI or may be performed without using AI. For example, the video providing unit can input the user's activity status data to the generation AI and cause the generation AI to adjust the timing of the video.

[0080] The video providing unit can estimate the user's emotion and adjust the length of the video based on the estimated user's emotion. The video providing unit can, for example, estimate the user's emotion and adjust the length of the video based on the estimated user's emotion. For example, the video providing unit can provide a longer video when the user is relaxed. Furthermore, the video providing unit can provide a shorter video when the user is stressed. Furthermore, the video providing unit can provide an energetic video when the user is excited. Thereby, the video providing unit can provide a more appropriate video by adjusting the length of the video based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the video providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the video providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the video.

[0081] The video providing unit can provide highly relevant video by considering the geographical location information of the user when providing the video. The video providing unit can provide highly relevant video by considering the geographical location information of the user when providing the video. For example, when the user is in a specific area, the video providing unit can provide video related to the area. Furthermore, when the user is traveling, the video providing unit can provide video related to the travel destination. Furthermore, when the user is at home, the video providing unit can provide video related to the home environment. Thereby, the video providing unit can provide highly relevant video by considering the geographical location information. A part or all of the above-mentioned processing in the video providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the video providing unit can input the geographical location information data of the user to the generation AI and cause the generation AI to provide highly relevant video.

[0082] The video providing unit can analyze the social media activity of the user and provide related videos when providing the video. The video providing unit can, for example, analyze the social media activity of the user and provide related videos when providing the video. For example, the video providing unit can refer to the video shared by the user on social media and provide related videos. In addition, the video providing unit can provide videos of a specific genre from the user's social media activity. Furthermore, the video providing unit can refer to the video of the account the user follows on social media and provide related videos. In this way, the video providing unit can provide related videos by analyzing social media activities. A part or all of the above-mentioned processing in the video providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the video providing unit can input the user's social media activity data to the generation AI and cause the generation AI to provide related videos.

[0083] The health monitoring unit can estimate the user's emotion and adjust the accuracy of monitoring based on the estimated user's emotion. The health monitoring unit can, for example, estimate the user's emotion and adjust the accuracy of monitoring based on the estimated user's emotion. For example, the health monitoring unit can perform detailed monitoring when the user is relaxed. In addition, the health monitoring unit can perform rapid monitoring when the user is stressed. Furthermore, the health monitoring unit can perform monitoring that quickly captures emotional fluctuations when the user is excited. This allows the health monitoring unit to adjust the accuracy of monitoring based on the user's emotion, thereby enabling more accurate monitoring. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the health monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the health monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the accuracy of monitoring.

[0084] The health monitoring unit can improve the accuracy of abnormality detection by referring to past health data during health monitoring. The health monitoring unit can improve the accuracy of abnormality detection by referring to past health data during health monitoring, for example. For example, the health monitoring unit can improve the accuracy of abnormality detection by referring to the user's past health data. In addition, the health monitoring unit can analyze a specific pattern from the user's past health data and improve the accuracy of abnormality detection. Furthermore, the health monitoring unit can perform early detection of abnormalities based on the user's past health data. Thereby, the health monitoring unit can improve the accuracy of abnormality detection by referring to the past health data. A part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the health monitoring unit can input the user's past health data to the generation AI and cause the generation AI to execute improvement of the accuracy of abnormality detection.

[0085] The health monitoring unit can perform monitoring in consideration of the lifestyle data of the user during health monitoring. The health monitoring unit can perform monitoring in consideration of the lifestyle data of the user during health monitoring, for example. For example, the health monitoring unit can improve the accuracy of monitoring by referring to the lifestyle data of the user. In addition, the health monitoring unit can analyze a specific pattern from the lifestyle data of the user and perform monitoring. Furthermore, the health monitoring unit can perform early detection of abnormalities based on the lifestyle data of the user. As a result, the health monitoring unit can perform more accurate monitoring by taking the lifestyle data into consideration. A part or all of the above-mentioned processing in the health monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the health monitoring unit can input the lifestyle data of the user to the generation AI and cause the generation AI to improve the accuracy of monitoring.

[0086] The health monitoring unit can estimate the user's emotion and determine the priority of the monitoring results based on the estimated user's emotion. The health monitoring unit can, for example, estimate the user's emotion and determine the priority of the monitoring results based on the estimated user's emotion. For example, the health monitoring unit can provide detailed monitoring results preferentially when the user is relaxed. Furthermore, the health monitoring unit can provide quick monitoring results preferentially when the user is stressed. Furthermore, the health monitoring unit can provide monitoring results related to emotional fluctuations preferentially when the user is excited. Thereby, the health monitoring unit can provide more appropriate results by determining the priority of the monitoring results based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the health monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the health monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the monitoring results.

[0087] The health monitoring unit can perform monitoring while taking into consideration the geographical location information of the user during health monitoring. The health monitoring unit can perform monitoring while taking into consideration the geographical location information of the user during health monitoring, for example. For example, when the user is in a specific area, the health monitoring unit can perform monitoring while taking into consideration the characteristics of the area. In addition, when the user is traveling, the health monitoring unit can perform monitoring while taking into consideration the characteristics of the travel destination. Furthermore, when the user is at home, the health monitoring unit can perform monitoring while taking into consideration the home environment. This allows the health monitoring unit to perform more accurate monitoring by taking into consideration the geographical location information. Part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the health monitoring unit can input the geographical location information data of the user to the generation AI and cause the generation AI to perform monitoring.

[0088] The health monitoring unit can analyze the social media activity of the user during health monitoring and acquire related health data. The health monitoring unit can, for example, analyze the social media activity of the user during health monitoring and acquire related health data. For example, the health monitoring unit can refer to health data shared by the user on social media and acquire related information. The health monitoring unit can also acquire data related to a specific health condition from the user's social media activity. Furthermore, the health monitoring unit can refer to health data of accounts the user follows on social media and acquire related information. In this way, the health monitoring unit can acquire related health data by analyzing social media activity. A part or all of the above-mentioned processing in the health monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the health monitoring unit can input the user's social media activity data to the generation AI and cause the generation AI to execute acquisition of related health data.

[0089] The learning unit can estimate the user's emotion and select learning data based on the estimated user's emotion. The learning unit can, for example, estimate the user's emotion and select learning data based on the estimated user's emotion. For example, when the user is relaxed, the learning unit can select learning data that can relax the user. Furthermore, when the user is stressed, the learning unit can select learning data that helps relieve stress. Furthermore, when the user is excited, the learning unit can select energetic learning data. This allows the learning unit to select learning data based on the user's emotion, thereby enabling more appropriate learning. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data to the generation AI and cause the generation AI to select the learning data.

[0090] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit can optimize the learning algorithm by referring to past learning data, for example, during learning. For example, the learning unit can select an optimal learning algorithm by referring to the user's past learning data. In addition, the learning unit can analyze a specific pattern from the user's past learning data and adjust the algorithm. Furthermore, the learning unit can select an algorithm that improves the efficiency of learning based on the user's past learning data. Thereby, the learning unit can optimize the learning algorithm by referring to the past learning data. A part or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input the user's past learning data to the generation AI and cause the generation AI to execute optimization of the learning algorithm.

[0091] The learning unit can estimate the user's emotion and adjust the frequency of learning based on the estimated user's emotion. The learning unit can, for example, estimate the user's emotion and adjust the frequency of learning based on the estimated user's emotion. For example, the learning unit can increase the frequency of learning when the user is relaxed. Also, the learning unit can reduce the frequency of learning when the user is stressed. Furthermore, the learning unit can adjust the frequency of learning and perform learning at an optimal timing when the user is excited. As a result, the learning unit can adjust the frequency of learning based on the user's emotion, thereby enabling more appropriate learning. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the frequency of learning.

[0092] The learning unit can weight the learning data based on the time of submission of the voice data during learning. The learning unit can weight the learning data based on the time of submission of the voice data during learning. For example, the learning unit can weight the learning data based on the time of the voice data submitted by the user. Also, the learning unit can preferentially learn the voice data submitted by the user in a specific time period. Furthermore, the learning unit can weight the learning data based on the frequency of the voice data submitted by the user. Thereby, the learning unit can weight the learning data based on the time of submission of the voice data, thereby enabling more appropriate learning. Part or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of submission of the voice data of the user to the generation AI and cause the generation AI to execute weighting of the learning data.

[0093] The notification unit can estimate the user's emotion and adjust the content of the notification based on the estimated user's emotion. The notification unit can, for example, estimate the user's emotion and adjust the content of the notification based on the estimated user's emotion. For example, the notification unit can provide detailed notification content when the user is relaxed. Furthermore, the notification unit can provide concise notification content when the user is stressed. Furthermore, the notification unit can provide notification content that matches the emotion when the user is excited. Thereby, the notification unit can adjust the content of the notification based on the user's emotion, thereby enabling more appropriate notification. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the notification content.

[0094] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit can select the optimal notification method by referring to the user's past notification history, for example, at the time of notification. For example, the notification unit can select the optimal notification method by referring to the user's past notification history. In addition, the notification unit can analyze a specific pattern from the user's past notification history and adjust the notification method. Furthermore, the notification unit can select a method for improving the efficiency of notifications based on the user's past notification history. Thereby, the notification unit can select the optimal notification method by referring to the past notification history. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's notification history data to the generation AI and cause the generation AI to select the optimal notification method.

[0095] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit can, for example, estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. For example, the notification unit can provide detailed notifications preferentially when the user is relaxed. Furthermore, the notification unit can provide quick notifications preferentially when the user is stressed. Furthermore, the notification unit can provide notifications regarding emotional fluctuations preferentially when the user is excited. This allows the notification unit to determine the priority of notifications based on the user's emotion, thereby enabling more appropriate notifications. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of notifications.

[0096] The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification. The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification, for example. For example, when the user is in a specific area, the notification unit can select the notification method in consideration of the characteristics of the area. Furthermore, when the user is traveling, the notification unit can select the notification method in consideration of the characteristics of the travel destination. Furthermore, when the user is at home, the notification unit can select the notification method in consideration of the home environment. Thereby, the notification unit can select the optimal notification method by considering the geographical location information. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select the optimal notification method.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The laughter providing system may further include an activity analysis unit that analyzes the user's activity history. The activity analysis unit may collect the user's daily activity data and learn the user's activity patterns. For example, the activity analysis unit may analyze the time periods when the user exercises and takes rests, and provide optimal timing for laughter based on the analysis. The activity analysis unit may also adjust the intensity and frequency of laughter according to the user's activity level. Furthermore, the activity analysis unit may identify time periods when stress is high based on the user's activity history, and provide relaxing laughter during those time periods. This allows the laughter providing system to provide optimal laughter according to the user's activity status.

[0099] The voice analysis unit can take into account the user's physical condition data when analyzing the user's tone of voice or laughter. For example, the voice analysis unit can acquire physical condition data such as the user's heart rate and blood pressure, and adjust the accuracy of the analysis based on the data. If the user is in poor health, the accuracy of the analysis can be improved to perform a detailed emotion analysis. Also, if the user is in good health, the accuracy of the analysis can be adjusted to provide results quickly. Furthermore, the voice analysis unit can quickly capture emotional fluctuations based on the user's physical condition data. This allows the voice analysis unit to perform a more accurate analysis by adjusting the accuracy of the analysis based on the user's physical condition.

[0100] The video providing unit can estimate the user's emotion and adjust the content of the video based on the estimated user's emotion. For example, when the user is relaxed, the video providing unit can provide a calm video. When the user is stressed, the video providing unit can provide a relaxing video. When the user is excited, the video providing unit can provide an energetic video. In this way, the video providing unit can provide a more appropriate video by adjusting the content of the video based on the user's emotion.

[0101] The health monitoring unit can estimate the user's emotions and adjust the accuracy of monitoring based on the estimated user's emotions. For example, the health monitoring unit can perform detailed monitoring when the user is relaxed. Furthermore, the health monitoring unit can perform rapid monitoring when the user is stressed. Furthermore, the health monitoring unit can perform monitoring that quickly captures emotional fluctuations when the user is excited. This allows the health monitoring unit to adjust the accuracy of monitoring based on the user's emotions, enabling more accurate monitoring.

[0102] The health monitoring unit can perform health monitoring by taking into account the lifestyle habit data of the user. For example, the health monitoring unit can improve the accuracy of monitoring by referring to the lifestyle habit data of the user. The health monitoring unit can also analyze specific patterns from the lifestyle habit data of the user and perform monitoring. Furthermore, the health monitoring unit can perform early detection of abnormalities based on the lifestyle habit data of the user. This allows the health monitoring unit to perform more accurate monitoring by taking into account the lifestyle habit data.

[0103] The voice analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user's emotions. For example, the voice analysis unit can provide detailed analysis results preferentially when the user is relaxed. Furthermore, the voice analysis unit can provide quick analysis results preferentially when the user is stressed. Furthermore, the voice analysis unit can provide analysis results related to emotional fluctuations preferentially when the user is excited. In this way, the voice analysis unit can provide more appropriate results by determining the priority of analysis results based on the user's emotions.

[0104] The voice analysis unit can improve the analysis accuracy by filtering the user's current environmental sound during voice analysis. For example, when the user is in a noisy environment, the voice analysis unit can improve the analysis accuracy by filtering the environmental sound. Also, when the user is in a quiet environment, the voice analysis unit can filter the environmental sound to perform a detailed analysis. Furthermore, when the user is moving, the voice analysis unit can filter the environmental sound to maintain the analysis accuracy. As a result, the voice analysis unit can improve the analysis accuracy by filtering the environmental sound.

[0105] When providing a video, the video providing unit can select the optimal video by referring to the user's past viewing history. For example, the video providing unit can select the optimal video by referring to the history of videos the user has viewed in the past. The video providing unit can also select a video of a specific genre from the user's past viewing history. Furthermore, the video providing unit can select a video that matches the viewing tendency based on the user's past viewing history. This allows the video providing unit to select the optimal video by referring to the past viewing history.

[0106] The notification unit can estimate the user's emotion and adjust the content of the notification based on the estimated user's emotion. For example, the notification unit can provide detailed notification content when the user is relaxed. Furthermore, the notification unit can provide concise notification content when the user is stressed. Furthermore, the notification unit can provide notification content that matches the emotion when the user is excited. This allows the notification unit to adjust the content of the notification based on the user's emotion, thereby enabling more appropriate notification.

[0107] At the time of notification, the notification unit can select the optimal notification method by taking into consideration the geographical location information of the user. For example, when the user is in a specific area, the notification unit can select the notification method by taking into consideration the characteristics of the area. Furthermore, when the user is traveling, the notification unit can select the notification method by taking into consideration the characteristics of the travel destination. Furthermore, when the user is at home, the notification unit can select the notification method by taking into consideration the home environment. In this way, the notification unit can select the optimal notification method by taking into consideration the geographical location information.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The voice analysis unit analyzes the tone of the user's voice or laughter. For example, the voice analysis unit can analyze the pitch, strength, rhythm, etc. of the user's voice to estimate the user's emotional state. The voice analysis unit can also analyze the type and sound characteristics of the user's laughter to learn what makes the user laugh. For example, if the user laughs at a joke, the voice analysis unit can learn the content and timing of that joke and provide a joke at the optimal timing in the next and subsequent conversations. Step 2: The video providing unit provides video based on the results of the analysis by the audio analysis unit. For example, the video providing unit can provide video with content that is likely to make the user laugh. The video providing unit can analyze the user's reaction in real time and adjust the content and timing of the video. For example, if the user laughs at a video of an animal, the video providing unit can learn the content and timing of that video and reflect it in subsequent video provision. Step 3: The health monitoring unit monitors the health of the parents based on the results of the analysis by the voice analysis unit. For example, the health monitoring unit can analyze the tone of the parents' voices and laughter to monitor their health. If the frequency at which the parents laugh has decreased, the health monitoring unit can notify the user that there may be something wrong with their health.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned voice analysis unit, video providing unit, and health monitoring unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the voice analysis unit detects the tone of the user's voice and laughter using the microphone 38B of the smart device 14, and the detected voice and laughter are analyzed by the control unit 46A. The video providing unit provides the user with a video using, for example, the display 40A of the smart device 14, and the content and timing of the video are adjusted by the control unit 46A. The health monitoring unit analyzes the tone of the parents' voices and laughter using, for example, the specific processing unit 290 of the data processing device 12, and monitors the health condition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned voice analysis unit, video providing unit, and health monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice analysis unit detects the tone of the user's voice or laughter using the microphone 238 of the smart glasses 214, and is analyzed by the control unit 46A. The video providing unit provides the user with an image using, for example, the display of the smart glasses 214, and the content and timing of the image are adjusted by the control unit 46A. The health monitoring unit analyzes the tone of the parents' voices and laughter using, for example, the specific processing unit 290 of the data processing device 12, and monitors the health condition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] 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.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned voice analysis unit, video providing unit, and health monitoring unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the voice analysis unit detects the tone of the user's voice and laughter using the microphone 238 of the headset type terminal 314, and is analyzed by the control unit 46A. The video providing unit provides the user with an image using, for example, the display 343 of the headset type terminal 314, and the content and timing of the image are adjusted by the control unit 46A. The health monitoring unit analyzes the tone of the parents' voices and laughter using, for example, the specific processing unit 290 of the data processing device 12, and monitors the health condition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] 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.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned voice analysis unit, video providing unit, and health monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice analysis unit detects the tone of the user's voice and laughter using the microphone 238 of the robot 414, and the detected voice and laughter are analyzed by the control unit 46A. The video providing unit provides the user with a video using, for example, the display of the robot 414, and the content and timing of the video are adjusted by the control unit 46A. The health monitoring unit analyzes the tone of the parents' voices and laughter using, for example, the specific processing unit 290 of the data processing device 12, and monitors their health conditions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) a voice analysis unit that analyzes the tone of a user's voice or laughter; a video providing unit that provides a video based on the result of the analysis by the audio analysis unit; A health monitoring unit that monitors the health conditions of the parents based on the results of the analysis by the voice analysis unit. A system characterized by: (Appendix 2) The voice analysis unit A learning unit is provided that analyzes the user's tone of voice or laughter and learns the user's laughter pattern. 2. The system of claim 1, (Appendix 3) The video providing unit is Provide videos that users find funny 2. The system of claim 1, (Appendix 4) The health monitoring unit includes: Analyzing the tone of parents' voices or laughter to monitor their health 2. The system of claim 1, (Appendix 5) The health monitoring unit includes: Equipped with a notification unit that notifies the user when there is an abnormality in the health condition 2. The system of claim 1, (Appendix 6) The voice analysis unit Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The voice analysis unit Analyze the user's past voice data and select the optimal analysis algorithm 2. The system of claim 1, (Appendix 8) The voice analysis unit When analyzing voice, the user's current environmental sounds are filtered to improve analysis accuracy. 2. The system of claim 1, (Appendix 9) The voice analysis unit Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The voice analysis unit When analyzing voice, customize the analysis results by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 11) The voice analysis unit During voice analysis, the user's social media activity is analyzed to obtain relevant voice data. 2. The system of claim 1, (Appendix 12) The video providing unit is Estimating user emotions and adjusting video content based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The video providing unit is When providing video, the most suitable video is selected by referring to the user's past viewing history. 2. The system of claim 1, (Appendix 14) The video providing unit is When providing video, adjust the timing of the video based on the user's current activity 2. The system of claim 1, (Appendix 15) The video providing unit is Estimate the user's emotion and adjust the video length based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The video providing unit is When providing video, the system takes into account the user's geographic location information to provide highly relevant video. 2. The system of claim 1, (Appendix 17) The video providing unit is When providing video, analyze the user's social media activity and provide related video 2. The system of claim 1, (Appendix 18) The health monitoring unit includes: Estimate the user's emotions and adjust the accuracy of monitoring based on the estimated user emotions. 2. The system of claim 1, (Appendix 19) The health monitoring unit includes: Improve the accuracy of anomaly detection by referencing past health data during health monitoring 2. The system of claim 1, (Appendix 20) The health monitoring unit includes: When monitoring health, monitor taking into account the user's lifestyle data. 2. The system of claim 1, (Appendix 21) The health monitoring unit includes: Estimate user emotions and prioritize monitoring results based on the estimated user emotions. 2. The system of claim 1, (Appendix 22) The health monitoring unit includes: When monitoring health, the geographic location of the user is taken into consideration. 2. The system of claim 1, (Appendix 23) The health monitoring unit includes: During health monitoring, users' social media activities are analyzed to obtain relevant health data 2. The system of claim 1, (Appendix 24) The learning unit is Estimate the user's emotions and select learning data based on the estimated user emotions. 3. The system of claim 2, (Appendix 25) The learning unit is During training, the learning algorithm is optimized by referencing past training data. 3. The system of claim 2, (Appendix 26) The learning unit is Infer user emotions and adjust the frequency of learning based on the estimated user emotions. 3. The system of claim 2, (Appendix 27) The learning unit is During training, the training data is weighted based on when the audio data was submitted. 3. The system of claim 2, (Appendix 28) The notification unit is Infer user emotions and adjust notification content based on the estimated user emotions 6. The system of claim 5, (Appendix 29) The notification unit is When notifying users, the system selects the optimal notification method by referring to the user's past notification history. 6. The system of claim 5, (Appendix 30) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 6. The system of claim 5, (Appendix 31) The notification unit is When notifying users, the optimal notification method is selected based on the user's geographic location. 6. The system of claim 5, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice analysis unit that analyzes the tone of a user's voice or laughter; a video providing unit that provides a video based on the result of the analysis by the audio analysis unit; A health monitoring unit that monitors the health conditions of the parents based on the results of the analysis by the voice analysis unit. A system characterized by:

2. The voice analysis unit A learning unit is provided that analyzes the user's tone of voice or laughter and learns the user's laughter pattern.

2. The system of claim 1 .

3. The video providing unit is Provide videos that users find funny 2. The system of claim 1 .

4. The health monitoring unit includes: Analyzing the tone of parents' voices or laughter to monitor their health 2. The system of claim 1 .

5. The health monitoring unit includes: Equipped with a notification unit that notifies the user when there is an abnormality in the health condition 2. The system of claim 1 .

6. The voice analysis unit Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1 .

7. The voice analysis unit Analyze the user's past voice data and select the optimal analysis algorithm 2. The system of claim 1 .

8. The voice analysis unit When analyzing voice, the user's current environmental sounds are filtered to improve analysis accuracy.

2. The system of claim 1 .

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