system

Abstract

The system according to the embodiment aims to automatically create a diary based on emotional fluctuations. [Solution] A system according to an embodiment includes a monitoring unit, a detection unit, a summarization unit, and a recording unit. The monitoring unit monitors pulse or brain waves. The detection unit detects emotional fluctuations based on the data monitored by the monitoring unit. The summarization unit summarizes audio based on the emotional fluctuations detected by the detection unit. The recording unit records photos or videos based on the audio summarized by the summarization unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to automatically create a diary based on emotional fluctuations.

[0005] The system according to the embodiment aims to automatically create a diary based on emotional fluctuations. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a detection unit, a summarization unit, and a recording unit. The monitoring unit monitors pulse or brain waves. The detection unit detects emotional fluctuations based on the data monitored by the monitoring unit. The summarization unit summarizes the audio based on the emotional fluctuations detected by the detection unit. The recording unit records a photo or video based on the audio summarized by the summarization unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically create a diary based on emotional fluctuations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention reads a user's pulse and brain waves through a wearable item, detects emotional changes, and automatically records events before and after the change in a diary. This system constantly monitors the user's pulse and brain waves. If a significant emotional change is detected, the system captures the audio recordings before and after the change, which are summarized by a generation AI and recorded in the diary. Furthermore, when smart glasses are used, photos and videos can also be recorded simultaneously. For example, the system analyzes the user's pulse and brain waves in real time to detect emotional changes. Next, if a significant emotional change is detected, the system captures the audio recordings before and after the change, which are summarized by a generation AI. Furthermore, when smart glasses are used, photos and videos can also be recorded simultaneously. This allows the system to automatically provide detailed records of the user's emotional changes. For example, when reflecting on a moment of significant emotional change in daily life, the system can relive that moment through audio and video. Furthermore, accumulating data on emotional changes can help improve self-understanding and mental health.

[0029] An emotion recording system according to an embodiment includes a monitoring unit, a detection unit, a summarization unit, and a recording unit. The monitoring unit monitors the user's pulse or brain waves. For example, the monitoring unit acquires the user's biometric data using a pulse sensor or an brain wave sensor. The monitoring unit can also perform continuous monitoring and collect data in real time. Furthermore, the monitoring unit can transmit data to the cloud for remote monitoring. The detection unit detects emotional fluctuations based on the data acquired by the monitoring unit. For example, the detection unit can analyze a sudden increase in pulse rate or a specific pattern of brain waves to detect emotional fluctuations. The detection unit can also analyze emotional fluctuations in real time using an AI algorithm. Furthermore, the detection unit can detect abnormal emotional fluctuations by comparing the emotional fluctuations with past data. The summarization unit summarizes the audio based on the emotional fluctuations detected by the detection unit. For example, the summarization unit uses a generation AI to summarize audio before and after a significant emotional change. The summarization unit can also generate a summary by inputting a prompt to the generation AI, such as, "Please summarize the main points of this audio." Furthermore, the summarizing unit can convert audio data into text data and summarize it. The recording unit records photos or videos based on the audio summarized by the summarizing unit. For example, the recording unit uses smart glasses to take photos or videos of moments when emotions are greatly affected. The recording unit can also attach the taken photos or videos to a diary. Furthermore, the recording unit can store visual records of emotional fluctuations in the cloud for later reference. In this way, the emotion recording system according to the embodiment can automatically create a diary based on the user's emotional fluctuations and also provide visual records.

[0030] The monitoring unit can constantly monitor the user's pulse and brain waves. For example, a pulse sensor or an brain wave sensor is used for the constant monitoring. The monitoring unit measures the user's pulse in real time using, for example, a pulse sensor. The monitoring unit can also constantly monitor the user's brain waves using an brain wave sensor. Furthermore, the monitoring unit can transmit data to the cloud and perform remote monitoring. In this way, by constantly monitoring the user's pulse and brain waves, emotional fluctuations can be detected in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from the pulse sensor or brain wave sensor into a generation AI, which then analyzes the data.

[0031] The detection unit can analyze fluctuations in pulse rate and brain waves in real time to detect emotional fluctuations. The detection unit can detect emotional fluctuations, for example, by analyzing a sudden increase in pulse rate or a specific pattern of brain waves. For example, the detection unit can analyze pulse rate fluctuations in real time to detect emotional fluctuations. The detection unit can also analyze brain wave fluctuations in real time to detect emotional fluctuations. The detection unit can also analyze emotional fluctuations in real time using an AI algorithm. For example, the detection unit can analyze pulse rate and brain wave data using an AI algorithm to detect emotional fluctuations. The detection unit can also detect abnormal emotional fluctuations by comparing with past data. For example, the detection unit can detect abnormal fluctuations by comparing with past pulse rate data. The detection unit can also detect abnormal fluctuations by comparing with past brain wave data. In this way, emotional fluctuations can be quickly detected by analyzing pulse rate and brain wave fluctuations in real time. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or without using AI. For example, the detection unit can input pulse rate and brain wave data into the generation AI, which can then detect emotional fluctuations.

[0032] The summarization unit can pick up audio before and after a significant emotional change and summarize it using a generation AI. For example, the summarization unit can pick up audio before and after a significant emotional change and summarize it using a generation AI. For example, the summarization unit can input a prompt such as "Please summarize the main points of this audio" into the generation AI and generate a summary. The summarization unit can also convert audio data into text data using a generation AI to summarize. Furthermore, the summarization unit can automatically record audio before and after a significant emotional change and summarize it using a generation AI. For example, the summarization unit can record conversations before and after a significant emotional change and summarize them using a generation AI. This allows for the automatic creation of a detailed diary by summarizing the audio before and after a significant emotional change. Some or all of the above-mentioned processing in the summarization unit can be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input audio data into a generation AI and generate a summary using the generation AI.

[0033] The recording unit can record photos and videos using the smart glasses. For example, the recording unit can use the smart glasses to capture photos and videos of moments when emotions change significantly. For example, the recording unit can take photos using the camera of the smart glasses. The recording unit can also capture videos using the video function of the smart glasses. The recording unit can also attach the captured photos and videos to a diary. For example, the recording unit can attach the captured photos to the diary to provide a visual record of emotional fluctuations. The recording unit can also attach the captured videos to the diary to provide a detailed record of emotional fluctuations. In this way, by using the smart glasses, a visual record of emotional fluctuations can be attached to the diary. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input photos and videos captured with the smart glasses into a generation AI, which can then analyze the photos and videos.

[0034] The recording unit can attach a visual record of emotional fluctuations to the diary. The recording unit, for example, attaches a photo or video of a moment when an emotion significantly changes to the diary. For example, the recording unit attaches a photo taken with smart glasses to the diary. The recording unit can also attach a video taken with smart glasses to the diary. Furthermore, the recording unit can store the visual record of emotional fluctuations in the cloud and refer to it later. For example, the recording unit can store the captured photos and videos in the cloud and download them as needed. In this way, attaching a visual record of emotional fluctuations to the diary can provide a detailed record. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input photos and videos taken with smart glasses to a generation AI, which can analyze the photos and videos.

[0035] During monitoring, the monitoring unit can filter pulse and brain wave data according to the user's activity level. For example, when the user is stationary, the monitoring unit removes noise and filters the pulse and brain wave data. For example, the monitoring unit removes noise based on data in the stationary state to obtain accurate data. Furthermore, when the user is exercising, the monitoring unit can filter data taking into account noise caused by exercise. For example, the monitoring unit removes noise based on data from the user's exercise and obtains data that takes into account the influence of exercise. Furthermore, when the user is sleeping, the monitoring unit can filter pulse and brain wave data based on the user's sleep pattern. For example, the monitoring unit removes noise based on data from the user's sleep and obtains accurate data. In this way, accurate data can be obtained by filtering the data according to the user's activity level. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's activity level data to a generation AI, which then filters the data.

[0036] During monitoring, the monitoring unit can detect abnormal values ​​by referring to the user's past emotional fluctuation data. The monitoring unit, for example, compares the data with the user's past data to detect abnormal pulse fluctuations. For example, the monitoring unit detects abnormal fluctuations by comparing with past pulse data. The monitoring unit can also detect abnormal brain wave fluctuations by comparing with the user's past electroencephalogram patterns. For example, the monitoring unit detects abnormal fluctuations by comparing with past electroencephalogram data. The monitoring unit can also detect abnormal emotional fluctuations based on the user's past emotional fluctuation data. For example, the monitoring unit detects abnormal fluctuations by comparing with past emotional fluctuation data. In this way, abnormal emotional fluctuations can be quickly detected by referring to the past emotional fluctuation data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past emotional fluctuation data into a generation AI, which can detect abnormal values.

[0037] During monitoring, the monitoring unit can optimize the timing of monitoring based on the user's lifestyle rhythm. The monitoring unit, for example, starts monitoring in accordance with the user's wake-up time. For example, the monitoring unit starts collecting data in accordance with the user's wake-up time. The monitoring unit can also end monitoring in accordance with the user's bedtime. For example, the monitoring unit ends collecting data in accordance with the user's bedtime. The monitoring unit can also adjust the frequency of monitoring in accordance with the user's mealtimes. For example, the monitoring unit adjusts the frequency of data collection in accordance with the user's mealtimes. This enables efficient data collection by optimizing the timing of monitoring based on the user's lifestyle rhythm. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's lifestyle rhythm data into a generation AI, and the generation AI can optimize the timing of monitoring.

[0038] During monitoring, the monitoring unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, the monitoring unit prioritizes acquiring data on a relaxed state. For example, the monitoring unit prioritizes collecting data on a relaxed state at home. Furthermore, when the user is at work, the monitoring unit can also prioritize acquiring data on a stressed state. For example, the monitoring unit prioritizes collecting data on a stressed state at work. Furthermore, when the user is at an exercise facility, the monitoring unit can also prioritize acquiring data while exercising. For example, the monitoring unit prioritizes collecting data while exercising at an exercise facility. In this way, by taking the geographical location information into account, highly relevant data can be efficiently acquired. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize acquiring highly relevant data.

[0039] During monitoring, the monitoring unit can analyze the user's social media activity and acquire related data. For example, if the user is feeling stressed on social media, the monitoring unit prioritizes acquiring that data. For example, the monitoring unit prioritizes collecting data on the stress state on social media. The monitoring unit can also prioritize acquiring data on the user's relaxation on social media. For example, the monitoring unit prioritizes collecting data on the relaxation state on social media. The monitoring unit can also prioritize acquiring data on the user's exercise state on social media if the user posts about exercise on social media. For example, the monitoring unit prioritizes collecting data about exercise on social media. This allows for efficient acquisition of related data by analyzing social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media activity data into a generation AI, which can acquire related data.

[0040] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit adjusts the monitoring method, for example, based on situations in which the user felt stressed in the past. For example, the monitoring unit adjusts the monitoring method based on data on past stress states. The monitoring unit can also adjust the monitoring method based on situations in which the user was relaxed in the past. For example, the monitoring unit adjusts the monitoring method based on data on past relaxed states. The monitoring unit can also adjust the monitoring method based on situations in which the user exercised in the past. For example, the monitoring unit adjusts the monitoring method based on data on past exercise states. In this way, the monitoring method can be optimized by reflecting past feedback. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past feedback data into a generation AI and use the generation AI to customize the monitoring method.

[0041] During detection, the detection unit can detect emotional fluctuations by multidimensionally analyzing pulse and brain wave data. The detection unit, for example, simultaneously analyzes pulse and brain wave data to detect emotional fluctuations. For example, the detection unit simultaneously analyzes pulse data and brain wave data to detect emotional fluctuations. The detection unit can also detect emotional fluctuations by comparing pulse fluctuation patterns with brain wave fluctuation patterns. For example, the detection unit compares pulse fluctuation patterns with brain wave fluctuation patterns to detect emotional fluctuations. The detection unit can also integrate pulse and brain wave data to detect emotional fluctuations more accurately. For example, the detection unit integrates pulse data and brain wave data to detect emotional fluctuations. This enables more accurate detection of emotional fluctuations through multidimensional analysis. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input pulse data and brain wave data to a generation AI, which can then perform multidimensional analysis.

[0042] During detection, the detection unit can improve detection accuracy by referring to the user's past emotional fluctuation patterns. The detection unit improves detection accuracy, for example, based on the user's past emotional fluctuation patterns. For example, the detection unit improves detection accuracy by referring to the past emotional fluctuation patterns. The detection unit can also detect abnormal emotional fluctuations by referring to the user's past data. For example, the detection unit detects abnormal emotional fluctuations based on the past data. The detection unit can also improve detection accuracy by analyzing the user's past emotional fluctuation patterns. For example, the detection unit analyzes the past emotional fluctuation patterns and improves detection accuracy. In this way, detection accuracy is improved by referring to the past emotional fluctuation patterns. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input past emotional fluctuation patterns into a generation AI, which can improve detection accuracy.

[0043] During detection, the detection unit can optimize the detection of emotional fluctuations based on the user's current activity status. For example, when the user is exercising, the detection unit detects emotional fluctuations taking into account the effects of the exercise. For example, the detection unit detects emotional fluctuations based on data from the user's exercise. Furthermore, when the user is stationary, the detection unit can also detect emotional fluctuations by removing noise. For example, the detection unit can detect emotional fluctuations based on stationary data and then remove noise. Furthermore, when the user is sleeping, the detection unit can also detect emotional fluctuations based on sleep patterns. For example, the detection unit detects emotional fluctuations based on data from the user's sleep. This allows for accurate detection of emotional fluctuations by optimizing detection based on the current activity status. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's activity status data into a generation AI, and the generation AI can optimize the detection of emotional fluctuations.

[0044] The detection unit can detect emotional fluctuations taking the user's geographical distribution into consideration during detection. For example, when the user is at home, the detection unit prioritizes detecting emotional fluctuations at home. For example, the detection unit prioritizes detecting emotional fluctuations at home. Furthermore, when the user is at work, the detection unit can also prioritize detecting emotional fluctuations at work. For example, the detection unit prioritizes detecting emotional fluctuations at work. Furthermore, when the user is at an exercise facility, the detection unit can also prioritize detecting emotional fluctuations at the exercise facility. For example, the detection unit prioritizes detecting emotional fluctuations at the exercise facility. This improves the accuracy of detecting emotional fluctuations by taking the geographical distribution into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's geographical distribution data into a generation AI, and the generation AI can detect emotional fluctuations.

[0045] The detection unit can improve the accuracy of detecting emotional changes by referring to related literature during detection. The detection unit, for example, refers to related literature to improve the emotion change detection algorithm. For example, the detection unit improves the emotion change detection algorithm based on data from related literature. The detection unit can also improve the emotion change detection accuracy based on data from related literature. For example, the detection unit improves the emotion change detection accuracy by utilizing knowledge from related literature. The detection unit can also optimize the emotion change detection method by utilizing knowledge from related literature. For example, the detection unit optimizes the emotion change detection method based on knowledge from related literature. As a result, the emotion change detection accuracy is improved by referring to related literature. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data from related literature into a generation AI, which can improve the emotion change detection accuracy.

[0046] The detection unit can detect emotional fluctuations taking into account the user's market value during detection. For example, when the user's market value is high, the detection unit increases the detection accuracy of emotional fluctuations. For example, the detection unit increases the detection accuracy of emotional fluctuations based on data of users with high market value. The detection unit can also adjust the detection accuracy of emotional fluctuations when the user's market value is low. For example, the detection unit adjusts the detection accuracy of emotional fluctuations based on data of users with low market value. The detection unit can also optimize the detection method of emotional fluctuations based on the user's market value. For example, the detection unit optimizes the detection method of emotional fluctuations based on market value. This improves the detection accuracy of emotional fluctuations by taking market value into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's market value data into a generation AI, and the generation AI can detect emotional fluctuations.

[0047] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the audio data. For example, the summarization unit prioritizes summarization of important audio data to provide detailed content. For example, the summarization unit generates a detailed summary based on audio data of high importance. The summarization unit can also briefly summarize audio data of low importance. For example, the summarization unit generates a brief summary based on audio data of low importance. The summarization unit can also adjust the length of the summary based on the importance of the audio data. For example, the summarization unit generates a long summary based on audio data of high importance and a short summary based on audio data of low importance. In this way, by adjusting the level of detail of the summary based on the importance of the audio data, important information can be provided preferentially. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input audio data to a generation AI and have the generation AI adjust the level of detail of the summary.

[0048] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the audio data. For example, in the case of conversational audio, the summarization unit generates a dialogue-style summary. For example, the summarization unit generates a dialogue-style summary based on the conversational audio. In addition, in the case of lecture audio, the summarization unit can also generate a summary that summarizes the main points. For example, the summarization unit generates a summary that summarizes the main points based on the lecture audio. In addition, in the case of music audio, the summarization unit can also generate a summary that includes song titles and artist names. For example, the summarization unit generates a summary that includes song titles and artist names based on music audio. In this way, by applying a summarization algorithm depending on the category of the audio data, a more appropriate summary can be generated. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input the category of the audio data into the generation AI and apply different summarization algorithms using the generation AI.

[0049] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. The summarization unit, for example, adjusts the style of the summary based on the user's past summarization results. For example, the summarization unit adjusts the style of the summary based on the past summarization results. The summarization unit can also analyze the user's past summarization results to improve the accuracy of the summary. For example, the summarization unit analyzes the past summarization results and improves the accuracy of the summary. The summarization unit can also optimize the content of the summary by referring to the user's past summarization results. For example, the summarization unit optimizes the content of the summary by referring to the past summarization results. In this way, the accuracy of the summary is improved by referring to the past summarization results. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the summarization unit can input past summarization results into the generation AI and use the generation AI to improve the accuracy of the summary.

[0050] When generating summaries, the summarizing unit can determine the priority of summaries based on the submission date of the audio data. For example, the summarizing unit prioritizes summarizing recently submitted audio data. For example, the summarizing unit generates a summary based on recently submitted audio data. The summarizing unit can also postpone the generation of summaries based on older submitted audio data. For example, the summarizing unit postpones the generation of summaries based on older submitted audio data. The summarizing unit can also adjust the order of summaries based on the submission date. For example, the summarizing unit adjusts the order of summaries based on the submission date. In this way, by determining the priority of summaries based on the submission date, the latest information can be provided preferentially. Some or all of the above-mentioned processing in the summarizing unit is performed using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the summarizing unit can input the submission date of the audio data into the generation AI, and the generation AI can determine the priority of summaries.

[0051] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the audio data. The summarization unit, for example, prioritizes summarization of important audio data. For example, the summarization unit generates summaries based on highly relevant audio data. The summarization unit can also postpone summarization based on less relevant audio data. For example, the summarization unit postpones summarization based on less relevant audio data. The summarization unit can also adjust the order of summaries based on the relevance of the audio data. For example, the summarization unit adjusts the order of summaries based on highly relevant audio data. In this way, important information can be provided preferentially by adjusting the order of summaries based on relevance. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the summarization unit can input the relevance of the audio data into the generation AI and adjust the order of summaries using the generation AI.

[0052] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the summarization unit generates a summary that uses a lot of technical terms. For example, the summarization unit generates a summary that uses a lot of technical terms based on data of users with technical expertise. In addition, if the user does not have technical expertise, the summarization unit can generate a summary in simple language. For example, the summarization unit generates a summary in simple language based on data of users without technical expertise. In addition, the summarization unit can adjust the content of the summary based on the user's level of expertise. For example, the summarization unit adjusts the content of the summary based on the level of expertise. In this way, a summary suitable for the user is generated by adjusting the use of technical terms according to the level of expertise. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the summary.

[0053] The recording unit can select the optimal recording method by referring to the user's past recording data when recording. The recording unit, for example, selects the optimal photo or video recording method based on the user's past recording data. For example, the recording unit selects the optimal recording method based on the past recording data. The recording unit can also analyze the user's past recording data and suggest the optimal recording method. For example, the recording unit analyzes the past recording data and suggests the optimal recording method. The recording unit can also customize the recording method by referring to the user's past recording data. For example, the recording unit customizes the recording method by referring to the past recording data. In this way, the optimal recording method can be selected by referring to the past recording data. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past recording data to a generation AI, and the generation AI can select the optimal recording method.

[0054] The recording unit can customize the recording means based on the user's current living situation when recording. For example, if the user is traveling, the recording unit suggests a recording method suitable for the trip. For example, the recording unit suggests a recording method suitable for the trip based on data collected during the trip. Furthermore, if the user is at work, the recording unit can also suggest a recording method suitable for work. For example, the recording unit suggests a recording method suitable for work based on data collected during the work. Furthermore, if the user is at home, the recording unit can also suggest a recording method suitable for home use. For example, the recording unit suggests a recording method suitable for home use based on home data. This enables more appropriate recording by customizing the recording means based on the user's current living situation. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's living situation data into a generating AI, which can customize the recording means.

[0055] The recording unit can improve the recording method by reflecting the user's feedback during recording. The recording unit can improve the recording method, for example, based on feedback provided by the user in the past. For example, the recording unit improves the recording method based on past feedback. The recording unit can also analyze the user's feedback and suggest an optimal recording method. For example, the recording unit can analyze the feedback and suggest an optimal recording method. The recording unit can also customize the recording method by referring to the user's feedback. For example, the recording unit can customize the recording method by referring to the feedback. In this way, the recording method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's feedback data into a generation AI, which can improve the recording method.

[0056] The recording unit can select the optimal recording method by taking into account the user's geographical location information when recording. For example, if the user is at a tourist spot, the recording unit suggests a recording method suitable for sightseeing. For example, the recording unit suggests a recording method suitable for sightseeing based on data about the tourist spot. Furthermore, if the user is at home, the recording unit can also suggest a recording method suitable for the home. For example, the recording unit suggests a recording method suitable for the home based on data about the home. Furthermore, if the user is at work, the recording unit can also suggest a recording method suitable for the workplace. For example, the recording unit suggests a recording method suitable for the workplace based on data about the workplace. In this way, the optimal recording method can be selected by taking the geographical location information into account. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's geographical location information into a generation AI, which can select the optimal recording method.

[0057] The recording unit can analyze the user's social media activity at the time of recording and suggest a recording method. For example, if the user posts many photos on social media, the recording unit can suggest a photo recording method. For example, the recording unit can suggest a photo recording method based on social media data. Also, if the user posts many videos on social media, the recording unit can suggest a video recording method. For example, the recording unit can suggest a video recording method based on social media data. Also, the recording unit can analyze the user's social media activity and suggest an optimal recording method. For example, the recording unit can suggest an optimal recording method based on social media data. In this way, the optimal recording method can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's social media activity data into a generation AI, which can suggest a recording method.

[0058] The recording unit can customize the recording method by reflecting the user's past feedback when recording. The recording unit customizes the recording method, for example, based on feedback provided by the user in the past. For example, the recording unit customizes the recording method based on the past feedback. The recording unit can also analyze the user's feedback and suggest an optimal recording method. For example, the recording unit analyzes the feedback and suggests an optimal recording method. The recording unit can also improve the recording method by referring to the user's feedback. For example, the recording unit improves the recording method by referring to the feedback. In this way, the recording method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's feedback data into a generation AI and have the generation AI customize the recording method.

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

[0060] The monitoring unit can also monitor the user's body temperature and blood pressure. For example, the monitoring unit measures the user's body temperature in real time using a body temperature sensor. It can also constantly monitor the user's blood pressure using a blood pressure sensor. Furthermore, the monitoring unit can transmit this data to the cloud and perform remote monitoring. In this way, by constantly monitoring the user's body temperature and blood pressure, changes in health status can be detected in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data obtained from the body temperature sensor and blood pressure sensor into a generation AI, which then analyzes the data.

[0061] The summarization unit can summarize not only audio before and after major emotional changes, but also text messages and social media posts. For example, the summarization unit collects text messages before and after major emotional changes and summarizes them using a generation AI. It can also analyze social media posts and summarize content related to emotional changes. Furthermore, the summarization unit can automatically summarize the content of emails and chats before and after major emotional changes. This allows for the automatic creation of detailed diaries by summarizing not only audio but also text data. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input text data into a generation AI, which then generates a summary.

[0062] The monitoring unit can also monitor the user's diet and exercise data. For example, the monitoring unit records the contents of meals and calorie intake. It can also monitor the type of exercise and calorie expenditure. Furthermore, the monitoring unit can transmit this data to the cloud for remote monitoring. By monitoring the user's diet and exercise data, fluctuations in health status can be detected in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input diet and exercise data into a generation AI, which then analyzes the data.

[0063] The summarization unit can summarize not only the audio before and after the emotional event, but also the video call content. For example, the summarization unit collects the video call content before and after the emotional event and summarizes it using a generation AI. It can also analyze the video call footage and summarize the content related to the emotional change. Furthermore, the summarization unit can automatically record the video call content before and after the emotional event and summarize it using a generation AI. This allows a detailed diary to be automatically created by summarizing not only the audio but also the video call content. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input video call data into a generation AI, which then generates a summary.

[0064] The monitoring unit can also monitor the user's sleep patterns. For example, the monitoring unit can measure the user's sleep patterns in real time using a sleep sensor. It can also constantly monitor the quality and duration of sleep. Furthermore, the monitoring unit can transmit this data to the cloud for remote monitoring. This allows constant monitoring of the user's sleep patterns to detect fluctuations in health status in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from the sleep sensor into a generation AI, which then analyzes the data.

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

[0066] Step 1: The monitoring unit monitors the user's pulse or brain waves. For example, the monitoring unit acquires the user's biometric data using a pulse sensor or brain wave sensor. The monitoring unit can also perform constant monitoring and collect data in real time. Furthermore, the monitoring unit can also transmit data to the cloud for remote monitoring. Step 2: The detection unit detects emotional fluctuations based on the data acquired by the monitoring unit. For example, the detection unit may detect emotional fluctuations by analyzing a sudden increase in pulse rate or specific patterns in brain waves. The detection unit may also analyze emotional fluctuations in real time using an AI algorithm. Furthermore, the detection unit may detect abnormal emotional fluctuations by comparing data with past data. Step 3: The summarization unit summarizes the audio based on the emotional fluctuations detected by the detection unit. For example, the summarization unit uses the generation AI to summarize the audio before and after a significant emotional change. The summarization unit can also generate a summary by inputting a prompt to the generation AI such as "Please summarize the main points of this audio." Furthermore, the summarization unit can convert the audio data into text data and perform the summary. Step 4: The recorder records photos or videos based on the audio summarized by the summarizer. For example, the recorder may use smart glasses to capture photos or videos of moments of significant emotional change. The recorder may also attach the captured photos or videos to a diary. The recorder may also store a visual record of emotional changes in the cloud for later reference.

[0067] (Example 2) A system according to an embodiment of the present invention reads a user's pulse and brain waves through a wearable item, detects emotional changes, and automatically records events before and after the change in a diary. This system constantly monitors the user's pulse and brain waves. If a significant emotional change is detected, the system captures the audio recordings before and after the change, which are summarized by a generation AI and recorded in the diary. Furthermore, when smart glasses are used, photos and videos can also be recorded simultaneously. For example, the system analyzes the user's pulse and brain waves in real time to detect emotional changes. Next, if a significant emotional change is detected, the system captures the audio recordings before and after the change, which are summarized by a generation AI. Furthermore, when smart glasses are used, photos and videos can also be recorded simultaneously. This allows the system to automatically provide detailed records of the user's emotional changes. For example, when reflecting on a moment of significant emotional change in daily life, the system can relive that moment through audio and video. Furthermore, accumulating data on emotional changes can help improve self-understanding and mental health.

[0068] An emotion recording system according to an embodiment includes a monitoring unit, a detection unit, a summarization unit, and a recording unit. The monitoring unit monitors the user's pulse or brain waves. For example, the monitoring unit acquires the user's biometric data using a pulse sensor or an brain wave sensor. The monitoring unit can also perform continuous monitoring and collect data in real time. Furthermore, the monitoring unit can transmit data to the cloud for remote monitoring. The detection unit detects emotional fluctuations based on the data acquired by the monitoring unit. For example, the detection unit can analyze a sudden increase in pulse rate or a specific pattern of brain waves to detect emotional fluctuations. The detection unit can also analyze emotional fluctuations in real time using an AI algorithm. Furthermore, the detection unit can detect abnormal emotional fluctuations by comparing the emotional fluctuations with past data. The summarization unit summarizes the audio based on the emotional fluctuations detected by the detection unit. For example, the summarization unit uses a generation AI to summarize audio before and after a significant emotional change. The summarization unit can also generate a summary by inputting a prompt to the generation AI, such as, "Please summarize the main points of this audio." Furthermore, the summarizing unit can convert audio data into text data and summarize it. The recording unit records photos or videos based on the audio summarized by the summarizing unit. For example, the recording unit uses smart glasses to take photos or videos of moments when emotions are greatly affected. The recording unit can also attach the taken photos or videos to a diary. Furthermore, the recording unit can store visual records of emotional fluctuations in the cloud for later reference. In this way, the emotion recording system according to the embodiment can automatically create a diary based on the user's emotional fluctuations and also provide visual records.

[0069] The monitoring unit can constantly monitor the user's pulse and brain waves. For example, a pulse sensor or an brain wave sensor is used for the constant monitoring. The monitoring unit measures the user's pulse in real time using, for example, a pulse sensor. The monitoring unit can also constantly monitor the user's brain waves using an brain wave sensor. Furthermore, the monitoring unit can transmit data to the cloud and perform remote monitoring. In this way, by constantly monitoring the user's pulse and brain waves, emotional fluctuations can be detected in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from the pulse sensor or brain wave sensor into a generation AI, which then analyzes the data.

[0070] The detection unit can analyze fluctuations in pulse rate and brain waves in real time to detect emotional fluctuations. The detection unit can detect emotional fluctuations, for example, by analyzing a sudden increase in pulse rate or a specific pattern of brain waves. For example, the detection unit can analyze pulse rate fluctuations in real time to detect emotional fluctuations. The detection unit can also analyze brain wave fluctuations in real time to detect emotional fluctuations. The detection unit can also analyze emotional fluctuations in real time using an AI algorithm. For example, the detection unit can analyze pulse rate and brain wave data using an AI algorithm to detect emotional fluctuations. The detection unit can also detect abnormal emotional fluctuations by comparing with past data. For example, the detection unit can detect abnormal fluctuations by comparing with past pulse rate data. The detection unit can also detect abnormal fluctuations by comparing with past brain wave data. In this way, emotional fluctuations can be quickly detected by analyzing pulse rate and brain wave fluctuations in real time. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or without using AI. For example, the detection unit can input pulse rate and brain wave data into the generation AI, which can then detect emotional fluctuations.

[0071] The summarization unit can pick up audio before and after a significant emotional change and summarize it using a generation AI. For example, the summarization unit can pick up audio before and after a significant emotional change and summarize it using a generation AI. For example, the summarization unit can input a prompt such as "Please summarize the main points of this audio" into the generation AI and generate a summary. The summarization unit can also convert audio data into text data using a generation AI to summarize. Furthermore, the summarization unit can automatically record audio before and after a significant emotional change and summarize it using a generation AI. For example, the summarization unit can record conversations before and after a significant emotional change and summarize them using a generation AI. This allows for the automatic creation of a detailed diary by summarizing the audio before and after a significant emotional change. Some or all of the above-mentioned processing in the summarization unit can be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input audio data into a generation AI and generate a summary using the generation AI.

[0072] The recording unit can record photos and videos using the smart glasses. For example, the recording unit can use the smart glasses to capture photos and videos of moments when emotions change significantly. For example, the recording unit can take photos using the camera of the smart glasses. The recording unit can also capture videos using the video function of the smart glasses. The recording unit can also attach the captured photos and videos to a diary. For example, the recording unit can attach the captured photos to the diary to provide a visual record of emotional fluctuations. The recording unit can also attach the captured videos to the diary to provide a detailed record of emotional fluctuations. In this way, by using the smart glasses, a visual record of emotional fluctuations can be attached to the diary. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input photos and videos captured with the smart glasses into a generation AI, which can then analyze the photos and videos.

[0073] The recording unit can attach a visual record of emotional fluctuations to the diary. The recording unit, for example, attaches a photo or video of a moment when an emotion significantly changes to the diary. For example, the recording unit attaches a photo taken with smart glasses to the diary. The recording unit can also attach a video taken with smart glasses to the diary. Furthermore, the recording unit can store the visual record of emotional fluctuations in the cloud and refer to it later. For example, the recording unit can store the captured photos and videos in the cloud and download them as needed. In this way, attaching a visual record of emotional fluctuations to the diary can provide a detailed record. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input photos and videos taken with smart glasses to a generation AI, which can analyze the photos and videos.

[0074] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. For example, when the user is feeling stressed, the monitoring unit increases the monitoring frequency to collect detailed data. Furthermore, when the user is relaxed, the monitoring unit can also reduce the monitoring frequency and switch to a power-saving mode. Furthermore, when the user is exercising, the monitoring unit can appropriately adjust the monitoring frequency to collect data that takes into account the effects of the exercise. This enables efficient data collection by adjusting the monitoring frequency based on the user's emotions. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's emotion data into a generation AI, have the generation AI estimate the emotion, and adjust the monitoring frequency based on the result.

[0075] During monitoring, the monitoring unit can filter pulse and brain wave data according to the user's activity level. For example, when the user is stationary, the monitoring unit removes noise and filters the pulse and brain wave data. For example, the monitoring unit removes noise based on data in the stationary state to obtain accurate data. Furthermore, when the user is exercising, the monitoring unit can filter data taking into account noise caused by exercise. For example, the monitoring unit removes noise based on data from the user's exercise and obtains data that takes into account the influence of exercise. Furthermore, when the user is sleeping, the monitoring unit can filter pulse and brain wave data based on the user's sleep pattern. For example, the monitoring unit removes noise based on data from the user's sleep and obtains accurate data. In this way, accurate data can be obtained by filtering the data according to the user's activity level. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's activity level data to a generation AI, which then filters the data.

[0076] During monitoring, the monitoring unit can detect abnormal values ​​by referring to the user's past emotional fluctuation data. The monitoring unit, for example, compares the data with the user's past data to detect abnormal pulse fluctuations. For example, the monitoring unit detects abnormal fluctuations by comparing with past pulse data. The monitoring unit can also detect abnormal brain wave fluctuations by comparing with the user's past electroencephalogram patterns. For example, the monitoring unit detects abnormal fluctuations by comparing with past electroencephalogram data. The monitoring unit can also detect abnormal emotional fluctuations based on the user's past emotional fluctuation data. For example, the monitoring unit detects abnormal fluctuations by comparing with past emotional fluctuation data. In this way, abnormal emotional fluctuations can be quickly detected by referring to the past emotional fluctuation data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past emotional fluctuation data into a generation AI, which can detect abnormal values.

[0077] During monitoring, the monitoring unit can optimize the timing of monitoring based on the user's lifestyle rhythm. The monitoring unit, for example, starts monitoring in accordance with the user's wake-up time. For example, the monitoring unit starts collecting data in accordance with the user's wake-up time. The monitoring unit can also end monitoring in accordance with the user's bedtime. For example, the monitoring unit ends collecting data in accordance with the user's bedtime. The monitoring unit can also adjust the frequency of monitoring in accordance with the user's mealtimes. For example, the monitoring unit adjusts the frequency of data collection in accordance with the user's mealtimes. This enables efficient data collection by optimizing the timing of monitoring based on the user's lifestyle rhythm. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's lifestyle rhythm data into a generation AI, and the generation AI can optimize the timing of monitoring.

[0078] The monitoring unit can estimate the user's emotions and determine the priority of data to be monitored based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and determines the priority of data to be monitored based on the estimated emotions. For example, when the user is feeling stressed, the monitoring unit can prioritize monitoring pulse data. Also, when the user is relaxed, the monitoring unit can prioritize monitoring brain wave data. Furthermore, when the user is exercising, the monitoring unit can monitor both the pulse and brain waves in a balanced manner. This allows important data to be acquired preferentially by determining the priority of data based on the user's emotions. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's emotion data into a generation AI, and the generation AI can determine the priority of the data.

[0079] During monitoring, the monitoring unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, the monitoring unit prioritizes acquiring data on a relaxed state. For example, the monitoring unit prioritizes collecting data on a relaxed state at home. Furthermore, when the user is at work, the monitoring unit can also prioritize acquiring data on a stressed state. For example, the monitoring unit prioritizes collecting data on a stressed state at work. Furthermore, when the user is at an exercise facility, the monitoring unit can also prioritize acquiring data while exercising. For example, the monitoring unit prioritizes collecting data while exercising at an exercise facility. In this way, by taking the geographical location information into account, highly relevant data can be efficiently acquired. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into a generation AI, and the generation AI can prioritize acquiring highly relevant data.

[0080] During monitoring, the monitoring unit can analyze the user's social media activity and acquire related data. For example, if the user is feeling stressed on social media, the monitoring unit prioritizes acquiring that data. For example, the monitoring unit prioritizes collecting data on the stress state on social media. The monitoring unit can also prioritize acquiring data on the user's relaxation on social media. For example, the monitoring unit prioritizes collecting data on the relaxation state on social media. The monitoring unit can also prioritize acquiring data on the user's exercise state on social media if the user posts about exercise on social media. For example, the monitoring unit prioritizes collecting data about exercise on social media. This allows for efficient acquisition of related data by analyzing social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's social media activity data into a generation AI, which can acquire related data.

[0081] During monitoring, the monitoring unit can customize the monitoring method by reflecting the user's past feedback. The monitoring unit adjusts the monitoring method, for example, based on situations in which the user felt stressed in the past. For example, the monitoring unit adjusts the monitoring method based on data on past stress states. The monitoring unit can also adjust the monitoring method based on situations in which the user was relaxed in the past. For example, the monitoring unit adjusts the monitoring method based on data on past relaxed states. The monitoring unit can also adjust the monitoring method based on situations in which the user exercised in the past. For example, the monitoring unit adjusts the monitoring method based on data on past exercise states. In this way, the monitoring method can be optimized by reflecting past feedback. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's past feedback data into a generation AI and use the generation AI to customize the monitoring method.

[0082] The detection unit can estimate the user's emotions and adjust the detection criteria for emotional fluctuations based on the estimated user emotions. For example, the detection unit tightens the detection criteria for emotional fluctuations when the user is feeling stressed. For example, the detection unit tightens the detection criteria based on data on the user's stress state. The detection unit can also loosen the detection criteria for emotional fluctuations when the user is relaxed. For example, the detection unit loosens the detection criteria based on data on the user's relaxed state. The detection unit can also appropriately adjust the detection criteria for emotional fluctuations when the user is exercising. For example, the detection unit adjusts the detection criteria based on data on the user's exercise state. This improves the accuracy of detecting emotional fluctuations by adjusting the detection criteria based on the user's emotions. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's emotion data into a generation AI, and the generation AI can adjust the detection criteria for emotional fluctuations.

[0083] During detection, the detection unit can detect emotional fluctuations by multidimensionally analyzing pulse and brain wave data. The detection unit, for example, simultaneously analyzes pulse and brain wave data to detect emotional fluctuations. For example, the detection unit simultaneously analyzes pulse data and brain wave data to detect emotional fluctuations. The detection unit can also detect emotional fluctuations by comparing pulse fluctuation patterns with brain wave fluctuation patterns. For example, the detection unit compares pulse fluctuation patterns with brain wave fluctuation patterns to detect emotional fluctuations. The detection unit can also integrate pulse and brain wave data to detect emotional fluctuations more accurately. For example, the detection unit integrates pulse data and brain wave data to detect emotional fluctuations. This enables more accurate detection of emotional fluctuations through multidimensional analysis. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input pulse data and brain wave data to a generation AI, which can then perform multidimensional analysis.

[0084] During detection, the detection unit can improve detection accuracy by referring to the user's past emotional fluctuation patterns. The detection unit improves detection accuracy, for example, based on the user's past emotional fluctuation patterns. For example, the detection unit improves detection accuracy by referring to the past emotional fluctuation patterns. The detection unit can also detect abnormal emotional fluctuations by referring to the user's past data. For example, the detection unit detects abnormal emotional fluctuations based on the past data. The detection unit can also improve detection accuracy by analyzing the user's past emotional fluctuation patterns. For example, the detection unit analyzes the past emotional fluctuation patterns and improves detection accuracy. In this way, detection accuracy is improved by referring to the past emotional fluctuation patterns. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input past emotional fluctuation patterns into a generation AI, which can improve detection accuracy.

[0085] During detection, the detection unit can optimize the detection of emotional fluctuations based on the user's current activity status. For example, when the user is exercising, the detection unit detects emotional fluctuations taking into account the effects of the exercise. For example, the detection unit detects emotional fluctuations based on data from the user's exercise. Furthermore, when the user is stationary, the detection unit can also detect emotional fluctuations by removing noise. For example, the detection unit can detect emotional fluctuations based on stationary data and then remove noise. Furthermore, when the user is sleeping, the detection unit can also detect emotional fluctuations based on sleep patterns. For example, the detection unit detects emotional fluctuations based on data from the user's sleep. This allows for accurate detection of emotional fluctuations by optimizing detection based on the current activity status. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's activity status data into a generation AI, and the generation AI can optimize the detection of emotional fluctuations.

[0086] The detection unit can estimate the user's emotion and adjust the order in which the detection results of emotion fluctuations are displayed based on the estimated user's emotion. For example, when the user is feeling stressed, the detection unit can prioritize displaying emotion fluctuations related to stress. For example, the detection unit can prioritize displaying emotion fluctuations based on data on the user's stress state. Furthermore, when the user is relaxed, the detection unit can also prioritize displaying emotion fluctuations related to relaxation. For example, the detection unit can prioritize displaying emotion fluctuations based on data on the relaxed state. Furthermore, when the user is exercising, the detection unit can prioritize displaying emotion fluctuations related to exercise. For example, the detection unit can prioritize displaying emotion fluctuations based on data on the exercise state. In this way, by adjusting the display order based on the user's emotion, important information can be prioritized. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's emotion data into a generation AI, and the generation AI can adjust the order in which the detection results of emotion fluctuations are displayed.

[0087] The detection unit can detect emotional fluctuations taking the user's geographical distribution into consideration during detection. For example, when the user is at home, the detection unit prioritizes detecting emotional fluctuations at home. For example, the detection unit prioritizes detecting emotional fluctuations at home. Furthermore, when the user is at work, the detection unit can also prioritize detecting emotional fluctuations at work. For example, the detection unit prioritizes detecting emotional fluctuations at work. Furthermore, when the user is at an exercise facility, the detection unit can also prioritize detecting emotional fluctuations at the exercise facility. For example, the detection unit prioritizes detecting emotional fluctuations at the exercise facility. This improves the accuracy of detecting emotional fluctuations by taking the geographical distribution into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's geographical distribution data into a generation AI, and the generation AI can detect emotional fluctuations.

[0088] The detection unit can improve the accuracy of detecting emotional changes by referring to related literature during detection. The detection unit, for example, refers to related literature to improve the emotion change detection algorithm. For example, the detection unit improves the emotion change detection algorithm based on data from related literature. The detection unit can also improve the emotion change detection accuracy based on data from related literature. For example, the detection unit improves the emotion change detection accuracy by utilizing knowledge from related literature. The detection unit can also optimize the emotion change detection method by utilizing knowledge from related literature. For example, the detection unit optimizes the emotion change detection method based on knowledge from related literature. As a result, the emotion change detection accuracy is improved by referring to related literature. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data from related literature into a generation AI, which can improve the emotion change detection accuracy.

[0089] The detection unit can detect emotional fluctuations taking into account the user's market value during detection. For example, when the user's market value is high, the detection unit increases the detection accuracy of emotional fluctuations. For example, the detection unit increases the detection accuracy of emotional fluctuations based on data of users with high market value. The detection unit can also adjust the detection accuracy of emotional fluctuations when the user's market value is low. For example, the detection unit adjusts the detection accuracy of emotional fluctuations based on data of users with low market value. The detection unit can also optimize the detection method of emotional fluctuations based on the user's market value. For example, the detection unit optimizes the detection method of emotional fluctuations based on market value. This improves the detection accuracy of emotional fluctuations by taking market value into consideration. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's market value data into a generation AI, and the generation AI can detect emotional fluctuations.

[0090] The summarization unit can estimate the user's emotions and adjust the presentation method of the summary based on the estimated user emotions. For example, if the user is feeling stressed, the summarization unit generates a concise and easy-to-understand summary. For example, the summarization unit generates a concise summary based on data on the stress state. The summarization unit can also generate a detailed summary if the user is relaxed. For example, the summarization unit generates a detailed summary based on data on the relaxed state. The summarization unit can also generate a summary related to exercise if the user is exercising. For example, the summarization unit generates a summary related to exercise based on data on the exercise state. In this way, by adjusting the presentation method of the summary based on the user's emotions, a more appropriate summary is generated. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input the user's emotion data into the generation AI, and the generation AI can adjust the presentation method of the summary.

[0091] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the audio data. For example, the summarization unit prioritizes summarization of important audio data to provide detailed content. For example, the summarization unit generates a detailed summary based on audio data of high importance. The summarization unit can also briefly summarize audio data of low importance. For example, the summarization unit generates a brief summary based on audio data of low importance. The summarization unit can also adjust the length of the summary based on the importance of the audio data. For example, the summarization unit generates a long summary based on audio data of high importance and a short summary based on audio data of low importance. In this way, by adjusting the level of detail of the summary based on the importance of the audio data, important information can be provided preferentially. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input audio data to a generation AI and have the generation AI adjust the level of detail of the summary.

[0092] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the audio data. For example, in the case of conversational audio, the summarization unit generates a dialogue-style summary. For example, the summarization unit generates a dialogue-style summary based on the conversational audio. In addition, in the case of lecture audio, the summarization unit can also generate a summary that summarizes the main points. For example, the summarization unit generates a summary that summarizes the main points based on the lecture audio. In addition, in the case of music audio, the summarization unit can also generate a summary that includes song titles and artist names. For example, the summarization unit generates a summary that includes song titles and artist names based on music audio. In this way, by applying a summarization algorithm depending on the category of the audio data, a more appropriate summary can be generated. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input the category of the audio data into the generation AI and apply different summarization algorithms using the generation AI.

[0093] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. The summarization unit, for example, adjusts the style of the summary based on the user's past summarization results. For example, the summarization unit adjusts the style of the summary based on the past summarization results. The summarization unit can also analyze the user's past summarization results to improve the accuracy of the summary. For example, the summarization unit analyzes the past summarization results and improves the accuracy of the summary. The summarization unit can also optimize the content of the summary by referring to the user's past summarization results. For example, the summarization unit optimizes the content of the summary by referring to the past summarization results. In this way, the accuracy of the summary is improved by referring to the past summarization results. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the summarization unit can input past summarization results into the generation AI and use the generation AI to improve the accuracy of the summary.

[0094] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is feeling stressed, the summarization unit generates a short and to-the-point summary. For example, the summarization unit generates a short summary based on data on the user's stress state. The summarization unit can also generate a detailed summary if the user is relaxed. For example, the summarization unit generates a detailed summary based on data on the user's relaxed state. The summarization unit can also generate a concise summary if the user is exercising. For example, the summarization unit generates a concise summary based on data on the user's exercise state. In this way, by adjusting the length of the summary based on the user's emotions, a more appropriate summary is generated. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input the user's emotion data into the generation AI and adjust the length of the summary using the generation AI.

[0095] When generating summaries, the summarizing unit can determine the priority of summaries based on the submission date of the audio data. For example, the summarizing unit prioritizes summarizing recently submitted audio data. For example, the summarizing unit generates a summary based on recently submitted audio data. The summarizing unit can also postpone the generation of summaries based on older submitted audio data. For example, the summarizing unit postpones the generation of summaries based on older submitted audio data. The summarizing unit can also adjust the order of summaries based on the submission date. For example, the summarizing unit adjusts the order of summaries based on the submission date. In this way, by determining the priority of summaries based on the submission date, the latest information can be provided preferentially. Some or all of the above-mentioned processing in the summarizing unit is performed using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the summarizing unit can input the submission date of the audio data into the generation AI, and the generation AI can determine the priority of summaries.

[0096] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the audio data. The summarization unit, for example, prioritizes summarization of important audio data. For example, the summarization unit generates summaries based on highly relevant audio data. The summarization unit can also postpone summarization based on less relevant audio data. For example, the summarization unit postpones summarization based on less relevant audio data. The summarization unit can also adjust the order of summaries based on the relevance of the audio data. For example, the summarization unit adjusts the order of summaries based on highly relevant audio data. In this way, important information can be provided preferentially by adjusting the order of summaries based on relevance. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the summarization unit can input the relevance of the audio data into the generation AI and adjust the order of summaries using the generation AI.

[0097] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the summarization unit generates a summary that uses a lot of technical terms. For example, the summarization unit generates a summary that uses a lot of technical terms based on data of users with technical expertise. In addition, if the user does not have technical expertise, the summarization unit can generate a summary in simple language. For example, the summarization unit generates a summary in simple language based on data of users without technical expertise. In addition, the summarization unit can adjust the content of the summary based on the user's level of expertise. For example, the summarization unit adjusts the content of the summary based on the level of expertise. In this way, a summary suitable for the user is generated by adjusting the use of technical terms according to the level of expertise. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terms in the summary.

[0098] The recording unit can estimate the user's emotions and select photos and videos to record based on the estimated user emotions. For example, if the user is happy, the recording unit prioritizes recording photos and videos of that moment. For example, the recording unit selects photos and videos based on the emotion of joy. Also, if the user is surprised, the recording unit can prioritize recording photos and videos of that moment. For example, the recording unit selects photos and videos based on the emotion of surprise. Also, if the user is sad, the recording unit can prioritize recording photos and videos of that moment. For example, the recording unit selects photos and videos based on the emotion of sadness. This enables more appropriate recording by selecting photos and videos based on the user's emotions. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's emotion data into a generation AI, which then selects photos and videos.

[0099] The recording unit can select the optimal recording method by referring to the user's past recording data when recording. The recording unit, for example, selects the optimal photo or video recording method based on the user's past recording data. For example, the recording unit selects the optimal recording method based on the past recording data. The recording unit can also analyze the user's past recording data and suggest the optimal recording method. For example, the recording unit analyzes the past recording data and suggests the optimal recording method. The recording unit can also customize the recording method by referring to the user's past recording data. For example, the recording unit customizes the recording method by referring to the past recording data. In this way, the optimal recording method can be selected by referring to the past recording data. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past recording data to a generation AI, and the generation AI can select the optimal recording method.

[0100] The recording unit can customize the recording means based on the user's current living situation when recording. For example, if the user is traveling, the recording unit suggests a recording method suitable for the trip. For example, the recording unit suggests a recording method suitable for the trip based on data collected during the trip. Furthermore, if the user is at work, the recording unit can also suggest a recording method suitable for work. For example, the recording unit suggests a recording method suitable for work based on data collected during the work. Furthermore, if the user is at home, the recording unit can also suggest a recording method suitable for home use. For example, the recording unit suggests a recording method suitable for home use based on home data. This enables more appropriate recording by customizing the recording means based on the user's current living situation. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's living situation data into a generating AI, which can customize the recording means.

[0101] The recording unit can improve the recording method by reflecting the user's feedback during recording. The recording unit can improve the recording method, for example, based on feedback provided by the user in the past. For example, the recording unit improves the recording method based on past feedback. The recording unit can also analyze the user's feedback and suggest an optimal recording method. For example, the recording unit can analyze the feedback and suggest an optimal recording method. The recording unit can also customize the recording method by referring to the user's feedback. For example, the recording unit can customize the recording method by referring to the feedback. In this way, the recording method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's feedback data into a generation AI, which can improve the recording method.

[0102] The recording unit can estimate the user's emotions and determine the priority of data to be recorded based on the estimated user's emotions. For example, if the user is happy, the recording unit prioritizes recording of data at that moment. For example, the recording unit prioritizes data based on the emotion of joy. The recording unit can also prioritize recording of data at that moment when the user is surprised. For example, the recording unit prioritizes data based on the emotion of surprise. The recording unit can also prioritize recording of data at that moment when the user is sad. For example, the recording unit prioritizes data based on the emotion of sadness. In this way, by prioritizing data based on the user's emotions, important data can be preferentially recorded. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's emotion data to a generation AI, which can then prioritize the data.

[0103] The recording unit can select the optimal recording method by taking into account the user's geographical location information when recording. For example, if the user is at a tourist spot, the recording unit suggests a recording method suitable for sightseeing. For example, the recording unit suggests a recording method suitable for sightseeing based on data about the tourist spot. Furthermore, if the user is at home, the recording unit can also suggest a recording method suitable for the home. For example, the recording unit suggests a recording method suitable for the home based on data about the home. Furthermore, if the user is at work, the recording unit can also suggest a recording method suitable for the workplace. For example, the recording unit suggests a recording method suitable for the workplace based on data about the workplace. In this way, the optimal recording method can be selected by taking the geographical location information into account. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's geographical location information into a generation AI, which can select the optimal recording method.

[0104] The recording unit can analyze the user's social media activity at the time of recording and suggest a recording method. For example, if the user posts many photos on social media, the recording unit can suggest a photo recording method. For example, the recording unit can suggest a photo recording method based on social media data. Also, if the user posts many videos on social media, the recording unit can suggest a video recording method. For example, the recording unit can suggest a video recording method based on social media data. Also, the recording unit can analyze the user's social media activity and suggest an optimal recording method. For example, the recording unit can suggest an optimal recording method based on social media data. In this way, the optimal recording method can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's social media activity data into a generation AI, which can suggest a recording method.

[0105] The recording unit can customize the recording method by reflecting the user's past feedback when recording. The recording unit customizes the recording method, for example, based on feedback provided by the user in the past. For example, the recording unit customizes the recording method based on the past feedback. The recording unit can also analyze the user's feedback and suggest an optimal recording method. For example, the recording unit analyzes the feedback and suggests an optimal recording method. The recording unit can also improve the recording method by referring to the user's feedback. For example, the recording unit improves the recording method by referring to the feedback. In this way, the recording method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's feedback data into a generation AI and have the generation AI customize the recording method. === Hard Collateral 1-1 === Each of the multiple elements including the monitoring unit, detection unit, summarization unit, and recording unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit acquires biometric data of the user using a pulse sensor or an EEG sensor of the smart device 14. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes a sudden increase in pulse rate or specific EEG patterns to detect emotional fluctuations. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and summarizes audio before and after a significant emotional event using a generative AI. The recording unit is realized by the control unit 46A of the smart device 14 and takes a photo or video of the moment when the emotion significantly changes and attaches it to the diary. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, detection unit, summarization unit, and recording unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit acquires the user's biometric data using a pulse sensor or an EEG sensor in the smart glasses 214. The detection unit is realized by the specific processing unit 290 in the data processing device 12 and analyzes a sudden increase in pulse rate or specific EEG patterns to detect emotional fluctuations. The summarization unit is realized by the specific processing unit 290 in the data processing device 12 and summarizes audio before and after a significant emotional event using a generative AI. The recording unit is realized by the control unit 46A of the smart glasses 214 and takes a photo or video of the moment when the emotion significantly changes and attaches it to the diary. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, detection unit, summarization unit, and recording unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the monitoring unit acquires biometric data of the user using a pulse sensor or an electroencephalogram sensor of the headset-type terminal 314. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes a sudden increase in pulse rate or specific patterns of electroencephalograms to detect emotional fluctuations. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and uses a generation AI to summarize audio before and after a significant emotional event. The recording unit is realized by the control unit 46A of the headset-type terminal 314 and takes a photo or video of the moment when the emotion significantly changes and attaches it to the diary. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, detection unit, summarization unit, and recording unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit acquires biometric data of the user using a pulse sensor or an EEG sensor of the robot 414. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes sudden increases in pulse rate or specific EEG patterns to detect emotional fluctuations. The summarization unit is realized by the specific processing unit 290 of the data processing device 12 and summarizes audio before and after a significant emotional event using a generative AI. The recording unit is realized by the control unit 46A of the robot 414 and takes photos or videos of the moment when an emotional event occurs and attaches them to the diary.

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

[0107] The monitoring unit can also monitor the user's body temperature and blood pressure. For example, the monitoring unit measures the user's body temperature in real time using a body temperature sensor. It can also constantly monitor the user's blood pressure using a blood pressure sensor. Furthermore, the monitoring unit can transmit this data to the cloud and perform remote monitoring. In this way, by constantly monitoring the user's body temperature and blood pressure, changes in health status can be detected in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data obtained from the body temperature sensor and blood pressure sensor into a generation AI, which then analyzes the data.

[0108] The detection unit can detect emotional fluctuations by analyzing the user's voice tone and speech rate. For example, the detection unit can determine that a sudden increase in the user's voice tone indicates excitement or tension. Furthermore, the detection unit can also determine that a sudden increase in speech rate indicates stress or impatience. Furthermore, the detection unit can detect abnormal emotional fluctuations by comparing patterns of fluctuations in voice tone and speech rate with past data. This allows for rapid detection of emotional fluctuations by analyzing the voice tone and speech rate. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on voice tone and speech rate into a generation AI, which can then detect emotional fluctuations.

[0109] The summarization unit can summarize not only audio before and after major emotional changes, but also text messages and social media posts. For example, the summarization unit collects text messages before and after major emotional changes and summarizes them using a generation AI. It can also analyze social media posts and summarize content related to emotional changes. Furthermore, the summarization unit can automatically summarize the content of emails and chats before and after major emotional changes. This allows for the automatic creation of detailed diaries by summarizing not only audio but also text data. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input text data into a generation AI, which then generates a summary.

[0110] The recording unit can record the music and podcast playback history based on the user's emotional fluctuations. For example, the recording unit can record the music that was being played at the moment when the user's emotions were greatly affected. It can also record podcast episodes that were being played before and after the emotional fluctuation. Furthermore, the recording unit can store the music and podcast playback history related to the emotional fluctuations in the cloud and refer to it later. In this way, by recording the music and podcast playback history related to the emotional fluctuations, a detailed diary can be provided. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the music and podcast playback history into a generation AI, which can then perform analysis.

[0111] The monitoring unit can also monitor the user's diet and exercise data. For example, the monitoring unit records the contents of meals and calorie intake. It can also monitor the type of exercise and calorie expenditure. Furthermore, the monitoring unit can transmit this data to the cloud for remote monitoring. By monitoring the user's diet and exercise data, fluctuations in health status can be detected in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input diet and exercise data into a generation AI, which then analyzes the data.

[0112] The detection unit can estimate the user's emotions and notify the user of the detection result of the emotional fluctuation based on the estimated user's emotions. For example, if the user is feeling stressed, the detection unit can notify the user of emotional fluctuations related to stress. Also, if the user is relaxed, the detection unit can notify the user of emotional fluctuations related to relaxation. Furthermore, if the user is exercising, the detection unit can notify the user of emotional fluctuations related to exercise. In this way, important information can be provided promptly by providing notifications based on the user's emotions. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's emotional data into a generation AI, and notify the user of the detection result of the emotional fluctuation by the generation AI.

[0113] The summarization unit can summarize not only the audio before and after the emotional event, but also the video call content. For example, the summarization unit collects the video call content before and after the emotional event and summarizes it using a generation AI. It can also analyze the video call footage and summarize the content related to the emotional change. Furthermore, the summarization unit can automatically record the video call content before and after the emotional event and summarize it using a generation AI. This allows a detailed diary to be automatically created by summarizing not only the audio but also the video call content. Some or all of the above-mentioned processing in the summarization unit is performed using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the summarization unit can input video call data into a generation AI, which then generates a summary.

[0114] The recording unit can record related news articles and blog posts based on the user's emotional fluctuations. For example, the recording unit can record news articles related to the moment when the user's emotions significantly changed. It can also record blog posts related to the moments before and after the emotional fluctuations. Furthermore, the recording unit can store news articles and blog posts related to the emotional fluctuations in the cloud and refer to them later. This allows for the provision of a detailed diary by recording news articles and blog posts related to the emotional fluctuations. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input data of news articles and blog posts into a generation AI, which can then perform analysis.

[0115] The monitoring unit can also monitor the user's sleep patterns. For example, the monitoring unit can measure the user's sleep patterns in real time using a sleep sensor. It can also constantly monitor the quality and duration of sleep. Furthermore, the monitoring unit can transmit this data to the cloud for remote monitoring. This allows constant monitoring of the user's sleep patterns to detect fluctuations in health status in real time. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from the sleep sensor into a generation AI, which then analyzes the data.

[0116] The detection unit can estimate the user's emotions and visualize the detection results of emotional fluctuations based on the estimated user emotions. For example, if the user is feeling stressed, the detection unit can display stress-related emotional fluctuations in a graph or chart. Also, if the user is relaxed, the detection unit can visualize emotional fluctuations related to relaxation. Furthermore, if the user is exercising, the detection unit can visualize emotional fluctuations related to exercise. This allows important information to be intuitively understood by visualizing based on the user's emotions. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's emotional data into a generation AI, which can then visualize the detection results of emotional fluctuations.

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

[0118] Step 1: The monitoring unit monitors the user's pulse or brain waves. For example, the monitoring unit acquires the user's biometric data using a pulse sensor or brain wave sensor. The monitoring unit can also perform constant monitoring and collect data in real time. Furthermore, the monitoring unit can also transmit data to the cloud for remote monitoring. Step 2: The detection unit detects emotional fluctuations based on the data acquired by the monitoring unit. For example, the detection unit may detect emotional fluctuations by analyzing a sudden increase in pulse rate or specific patterns in brain waves. The detection unit may also analyze emotional fluctuations in real time using an AI algorithm. Furthermore, the detection unit may detect abnormal emotional fluctuations by comparing data with past data. Step 3: The summarization unit summarizes the audio based on the emotional fluctuations detected by the detection unit. For example, the summarization unit uses the generation AI to summarize the audio before and after a significant emotional change. The summarization unit can also generate a summary by inputting a prompt to the generation AI such as "Please summarize the main points of this audio." Furthermore, the summarization unit can convert the audio data into text data and perform the summary. Step 4: The recorder records photos or videos based on the audio summarized by the summarizer. For example, the recorder may use smart glasses to capture photos or videos of moments of significant emotional change. The recorder may also attach the captured photos or videos to a diary. The recorder may also store a visual record of emotional changes in the cloud for later reference.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

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

Claims

1. a monitoring unit that monitors a pulse or an electroencephalogram; a detection unit that detects changes in emotions based on the data monitored by the monitoring unit; a summarization unit that summarizes the speech based on the emotion fluctuations detected by the detection unit; a recording unit that records a photograph or a video based on the audio summarized by the summarizing unit; Equipped with A system characterized by:

2. The monitoring unit Constantly monitors the user's pulse and brain waves 2. The system of claim 1.

3. The detection unit Analyzing pulse and brain wave fluctuations in real time to detect emotional changes 2. The system of claim 1.

4. The summary section The audio before and after large emotional changes is picked up and summarized by the generative AI.

2. The system of claim 1.

5. The recording unit Record photos and videos using smart glasses 2. The system of claim 1.

6. The recording unit Keep a visual record of your emotional fluctuations in your journal 2. The system of claim 1.

7. The monitoring unit Estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions.

2. The system of claim 1.

8. The monitoring unit During monitoring, pulse and brainwave data is filtered according to the user's activity level.

2. The system of claim 1.

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