System

Abstract

An object of a system according to an embodiment is to analyze a daily behavior and a health condition of a user and to warn of a risk in advance.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a question-and-answer unit, and a warning unit. The collection unit collects daily behavior and health condition of the user. The analysis unit analyzes the data collected by the collection unit. The question and answer unit performs a question and answer at random timing based on the data analyzed by the analysis unit. The warning unit determines the risk based on the analysis result obtained by the question-and-answer unit, and issues a warning in advance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately assess risk based on a user's daily behavior and health status, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's daily behavior and health condition and to warn of danger in advance. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a question and answer unit, and a warning unit. The collection unit collects information on the user's daily behavior and health status. The analysis unit analyzes the data collected by the collection unit. The question and answer unit randomly conducts question and answer sessions based on the data analyzed by the analysis unit. The warning unit determines risk based on the analysis results obtained by the question and answer unit and issues a warning in advance. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's daily behavior and health condition and warn of danger in advance. [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 collects a user's daily behavior and health status, analyzes them with a generation AI, analyzes their mental state through question and answer sessions, determines whether there is a risk, and issues a warning. For example, the system collects the user's daily behavior and health status. For example, it collects data such as the user's number of steps, heart rate, and sleep time. It also collects the user's daily voice, social media posts, and text messages. Next, the system uses a generation AI to analyze the collected data. The generation AI analyzes the user's mental state based on the collected data. For example, it detects changes in the user's emotions from the content of the user's social media posts and text messages. It also detects signs of stress or anxiety from the user's daily voice. Furthermore, the system uses the generation AI to further analyze the user's mental state through irregular, unstructured question and answer sessions. For example, the generation AI asks the user questions such as, "Is there anything that's been bothering you lately?" and analyzes the user's answers. Finally, the system uses the generation AI to determine whether there is a risk based on the analysis results and issues a warning in advance. For example, if a user is feeling stressed or anxious, the generating AI will issue a warning such as, "You seem to be feeling stressed recently. We recommend that you take some time to relax." This allows the system to comprehensively analyze the user's daily behavior and health status and understand their mental state. This allows the system to comprehensively analyze the user's daily behavior and health status and understand their mental state. For example, the user can understand their mental state and take appropriate measures to maintain their mental health.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, a question and answer unit, and a warning unit. The collection unit collects a user's daily behavior and health status. For example, the collection unit collects health data such as the user's step count, heart rate, and sleep time. The collection unit can also collect the user's daily voice, social media posts, and text messages. For example, the collection unit collects the user's conversational voice and social media text posts. The analysis unit uses a generation AI to analyze the data collected by the collection unit. For example, the analysis unit detects changes in the user's emotions based on the collected data. The generation AI detects changes in emotions from the content of the user's social media posts and text messages using a text generation AI (e.g., LLM) or a multimodal generation AI. The question and answer unit uses the generation AI to randomly answer questions based on the data analyzed by the analysis unit. For example, the question and answer unit uses the generation AI to ask the user a question such as, "Is there anything that's been bothering you lately?" and analyzes the answer. The warning unit determines the risk based on the analysis results obtained by the question and answer unit and issues a warning in advance. For example, if the user is feeling stressed or anxious, the generating AI will issue a warning such as, "You seem to be feeling stressed recently. I recommend you take some time to relax." This allows the system according to the embodiment to comprehensively analyze the user's daily behavior and health condition and understand their mental state.

[0030] The collection unit can collect health data including the user's number of steps, heart rate, and sleep time. Examples of health data include, but are not limited to, the number of steps, heart rate, and sleep time. For example, the collection unit collects the user's number of steps using a smartwatch or a fitness tracker. The collection unit can also collect the user's heart rate using a heart rate sensor. For example, the heart rate sensor measures the user's heart rate in real time and collects the data. The collection unit can also collect the user's sleep time using a sleep tracker. For example, the sleep tracker analyzes the user's sleep patterns and records the sleep time. By collecting the user's health data, the health condition can be understood. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartwatch or fitness tracker into a generation AI and have the generation AI analyze the data.

[0031] The collection unit can collect the user's everyday voice, SNS posts, and text messages. The collection unit, for example, collects the user's everyday voice using a microphone. For example, the collection unit records the user's conversation voice and saves it as data. The collection unit can also collect the user's SNS posts. For example, the collection unit collects the user's text posts and image posts on SNS. The collection unit can also collect the user's text messages. For example, the collection unit collects the user's chat messages and emails. By collecting the user's everyday voice, SNS posts, and text messages, it is possible to understand changes in behavior and emotions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data acquired by a microphone to a generation AI and have the generation AI analyze the voice data.

[0032] The analysis unit can detect changes in the user's emotions based on the collected data. The analysis unit, for example, analyzes collected voice data to detect changes in the user's emotions. For example, the analysis unit analyzes changes in voice tone to detect changes in emotions. The analysis unit can also analyze collected text data to detect changes in the user's emotions. For example, the analysis unit performs sentiment analysis on the text to detect changes in emotions. The analysis unit can also analyze collected SNS posts to detect changes in the user's emotions. For example, the analysis unit analyzes the content of the SNS posts to detect changes in emotions. This makes it possible to understand the user's inner thoughts by detecting changes in their emotions. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input collected data into a generation AI and have the generation AI detect changes in emotions.

[0033] The question and answer unit can use the generation AI to ask the user questions at irregular intervals and analyze the answers. For example, the question and answer unit can use the generation AI to ask the user questions such as, "Is there anything that's been bothering you lately?" and analyze the answers. For example, the question and answer unit can analyze the content of the user's answers to understand the user's state of mind. The question and answer unit can also use the generation AI to ask the user questions such as, "Have you been feeling stressed lately?" and analyze the answers. For example, the question and answer unit can analyze the content of the user's answers to detect signs of stress. The question and answer unit can also use the generation AI to ask the user questions such as, "Has anything fun happened to you recently?" and analyze the answers. For example, the question and answer unit can analyze the content of the user's answers to detect changes in positive emotions. By asking questions at irregular intervals, the user's state of mind can be understood in more detail. Some or all of the above-described processing in the question and answer unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the Q&A section can have a generation AI generate questions and analyze answers.

[0034] The warning unit can issue a warning to the user based on the analysis results. For example, the warning unit can issue a warning to the user based on the analysis results, such as, "You seem to be feeling stressed recently. I recommend you take some time to relax." For example, the warning unit can detect signs of stress in the user and issue an appropriate warning. The warning unit can also issue a warning to the user based on the analysis results, such as, "You seem to be feeling anxious recently. I recommend you talk to someone." For example, the warning unit can detect signs of anxiety in the user and issue an appropriate warning. The warning unit can also issue a warning to the user based on the analysis results, such as, "You seem to be feeling more positive recently. Keep it up." For example, the warning unit can detect changes in the user's positive emotions and provide appropriate feedback. By issuing a warning based on the analysis results, the user can take appropriate measures. Some or all of the above-described processing in the warning unit can be performed using, or without, a generation AI. For example, the warning unit can cause a generation AI to generate and transmit a warning message.

[0035] The collection unit can anonymize data to protect the user's privacy. For example, the collection unit anonymizes the data by deleting the user's personal identifying information. For example, the collection unit anonymizes the data by deleting personal information such as the user's name and address. The collection unit can also mask the data to protect the user's privacy. For example, the collection unit masks part of the data to make it impossible to identify individuals. The collection unit can also encrypt the data to protect the user's privacy. For example, the collection unit encrypts the data to prevent third parties from accessing it. In this way, the data is anonymized, thereby protecting the user's privacy. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can have the generation AI perform the anonymization process on the data.

[0036] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit can analyze the user's past heart rate data and, if an abnormality is found, increase the frequency of heart rate collection. The collection unit can also analyze the user's past sleep data and, if the sleep quality is low, strengthen collection of sleep data. For example, the collection unit can analyze the user's past step count data and, if a lack of exercise is found, increase the frequency of step count collection. In this way, the optimal collection method can be selected by analyzing the past health data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze the past health data and select the optimal collection method.

[0037] The collection unit can perform filtering based on the user's current activity status and environment when collecting data. For example, when the user is exercising, the collection unit prioritizes collecting data related to exercise (heart rate, number of steps, etc.). Furthermore, when the user is resting, the collection unit can prioritize collecting data related to relaxation (heart rate, breathing rate, etc.). For example, when the user is working, the collection unit prioritizes collecting data related to stress (heart rate, voice tone, etc.). This allows for filtering data based on the user's activity status and environment, thereby collecting more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze the activity status and environment and filter the data.

[0038] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit prioritizes collecting voice data. Also, if the user frequently uses text messages, the collection unit can prioritize collecting text data. For example, if the user frequently posts images, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have a generation AI analyze the input method and select the optimal collection means.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location (environmental sounds, temperature, etc.). Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the travel destination (tourist information, transportation methods, etc.). For example, when the user is at home, the collection unit prioritizes collecting data related to the home environment (indoor temperature, humidity, etc.). This allows highly relevant data to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze the geographical location information and determine the priority of the data.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content posted by the user on social media and detect changes in emotions. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit collects related data based on the user's check-in information on social media. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can cause a generation AI to analyze social media activities and collect related data.

[0041] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the frequency of data collection based on feedback provided by the user in the past. The collection unit can also adjust the type of data to be collected based on feedback provided by the user in the past. For example, the collection unit customizes the collection method (audio, text, image, etc.) based on feedback provided by the user in the past. This allows the collection method to be optimized for the user by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze past feedback and customize the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit analyzes data with high importance (heart rate, voice tone, etc.) in detail. The analysis unit can also analyze data with low importance (step count, environmental sounds, etc.) in a simplified manner. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a health analysis algorithm to health data (heart rate, sleep time, etc.). The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a natural language processing algorithm to text data. This improves the accuracy of analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can have the generation AI analyze the data category and apply an appropriate algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. For example, the analysis unit builds a feedback loop to improve the accuracy of the analysis based on the user's past analysis results. This improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to refer to past analysis results and correct the current analysis result.

[0045] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. For example, the analysis unit optimally allocates analysis resources based on the time of submission. This allows the most recent data to be analyzed preferentially by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to evaluate the time of data submission and determine the analysis priority.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit optimally allocates analysis resources based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to evaluate the relevance of the data and adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. For example, the analysis unit adjusts the level of detail of the analysis results according to the user's level of expertise. This allows for analysis results that are easy to understand to be provided by providing analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to evaluate the level of expertise and adjust the analysis results.

[0048] During a question and answer session, the question and answer unit can select the most appropriate question by referring to the user's past answer history. For example, the question and answer unit selects relevant questions based on the user's past answer history. The question and answer unit can also select questions for digging deeper based on the user's past answer history. For example, the question and answer unit selects questions that match the user's interests based on the user's past answer history. This makes it possible to provide highly relevant questions by referring to the past answer history. Some or all of the above-described processing in the question and answer unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the question and answer unit can cause the generation AI to refer to the past answer history and select the most appropriate question.

[0049] The question and answer unit can customize the content of questions based on the user's current situation during the question and answer session. For example, if the user is at work, the question and answer unit can ask work-related questions. Also, if the user is on a break, the question and answer unit can ask questions to help the user relax. For example, if the user is exercising, the question and answer unit can ask exercise-related questions. This allows questions to be customized based on the user's current situation, thereby providing more appropriate questions. Some or all of the above-described processing in the question and answer unit may be performed using, or without, a generation AI. For example, the question and answer unit can cause the generation AI to analyze the current situation and customize the content of the questions.

[0050] The question and answer unit can improve the questioning method by reflecting user feedback during the question and answer session. For example, the question and answer unit adjusts the content of the questions based on feedback previously provided by the user. The question and answer unit can also adjust the frequency of questions based on feedback previously provided by the user. For example, the question and answer unit improves the format of the questions (audio, text, etc.) based on feedback previously provided by the user. This allows the questioning method to be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the question and answer unit may be performed using, or without, a generation AI, for example. For example, the question and answer unit can cause the generation AI to analyze the feedback and improve the questioning method.

[0051] The question and answer unit can provide optimal questions during a question and answer session by taking into account the user's geographical location information. For example, if the user is in a specific location, the question and answer unit poses questions related to that location. Furthermore, if the user is traveling, the question and answer unit can also pose questions related to the user's travel destination. For example, if the user is at home, the question and answer unit poses questions related to the user's home environment. This allows highly relevant questions to be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the question and answer unit may be performed using, or without, a generation AI. For example, the question and answer unit can cause the generation AI to analyze the geographical location information and generate optimal questions.

[0052] The question and answer unit can generate relevant questions by analyzing the user's social media activity during the question and answer session. For example, the question and answer unit can analyze the content posted by the user on social media and generate relevant questions. The question and answer unit can also generate relevant questions by referring to the activity of the user's friends on social media. For example, the question and answer unit generates relevant questions based on the user's check-in information on social media. This makes it possible to provide highly relevant questions by analyzing social media activity. Some or all of the above-described processing in the question and answer unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the question and answer unit can cause a generation AI to analyze social media activity and generate relevant questions.

[0053] The question and answer unit can customize the content of questions during a question and answer session by reflecting the user's past feedback. For example, the question and answer unit adjusts the content of questions based on feedback provided by the user in the past. The question and answer unit can also adjust the frequency of questions based on feedback provided by the user in the past. For example, the question and answer unit improves the format of questions (audio, text, etc.) based on feedback provided by the user in the past. This allows the content of questions to be optimized for the user by reflecting past feedback. Some or all of the above-mentioned processing in the question and answer unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the question and answer unit can cause a generation AI to analyze past feedback and customize the content of questions.

[0054] When issuing a warning, the warning unit can select the most appropriate warning by referring to the user's past behavioral history. The warning unit, for example, selects a relevant warning based on the user's past behavioral history. The warning unit can also select a warning for digging deeper based on the user's past behavioral history. For example, the warning unit selects a warning tailored to the user's interests based on the user's past behavioral history. This makes it possible to provide a highly relevant warning by referring to the past behavioral history. Some or all of the above-described processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the warning unit can cause the generation AI to refer to the past behavioral history and select the most appropriate warning.

[0055] The warning unit can customize the content of the warning based on the user's current situation when issuing a warning. For example, if the user is at work, the warning unit issues a work-related warning. Also, if the user is taking a break, the warning unit can issue a relaxation warning. For example, if the user is exercising, the warning unit issues an exercise-related warning. This allows for customizing the warning based on the user's current situation, thereby providing a more appropriate warning. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can cause the generation AI to analyze the current situation and customize the warning content.

[0056] The warning unit can improve the warning method by reflecting user feedback when issuing a warning. For example, the warning unit can adjust the content of the warning based on feedback previously provided by the user. The warning unit can also adjust the frequency of warnings based on feedback previously provided by the user. For example, the warning unit can improve the format of the warning (audio, text, etc.) based on feedback previously provided by the user. This allows the warning method to be optimized by reflecting user feedback. Some or all of the above-mentioned processing in the warning unit can be performed using, or without, a generation AI. For example, the warning unit can cause the generation AI to analyze the feedback and improve the warning method.

[0057] When issuing a warning, the warning unit can provide an optimal warning by taking into account the user's geographical location information. For example, if the user is in a specific location, the warning unit can issue a warning related to that location. Also, if the user is traveling, the warning unit can issue a warning related to the user's travel destination. For example, if the user is at home, the warning unit can issue a warning related to the home environment. In this way, by taking the user's geographical location information into account, highly relevant warnings can be provided. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can cause the generation AI to analyze the geographical location information and generate an optimal warning.

[0058] At the time of issuing a warning, the warning unit can analyze the user's social media activity and generate a relevant warning. For example, the warning unit can analyze the content posted by the user on social media and generate a relevant warning. The warning unit can also generate a relevant warning by referring to the activity of the user's friends on social media. For example, the warning unit generates a relevant warning based on the user's check-in information on social media. In this way, by analyzing social media activity, highly relevant warnings can be provided. Some or all of the above-described processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the warning unit can cause a generation AI to analyze social media activity and generate a relevant warning.

[0059] The warning unit can customize the content of the warning by reflecting the user's past feedback when issuing a warning. The warning unit can, for example, adjust the content of the warning based on feedback provided by the user in the past. The warning unit can also adjust the frequency of warnings based on feedback provided by the user in the past. For example, the warning unit can improve the format of the warning (audio, text, etc.) based on feedback provided by the user in the past. This allows the content of the warning to be optimized for the user by reflecting past feedback. Some or all of the above-mentioned processing in the warning unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the warning unit can cause the generation AI to analyze past feedback and customize the content of the warning.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze the user's past health data and select the optimal collection method. For example, it can analyze the user's past heart rate data and, if an abnormality is found, increase the frequency of heart rate collection. It can also analyze the user's past sleep data and, if the sleep quality is low, strengthen the collection of sleep data. For example, it can analyze the user's past step count data and, if a lack of exercise is found, increase the frequency of step count collection. In this way, by analyzing the past health data, the optimal collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to analyze the past health data and select the optimal collection method.

[0062] The collection unit can anonymize the data to protect the user's privacy. For example, the collection unit anonymizes the data by deleting the user's personal identifying information. For example, the collection unit anonymizes the data by deleting personal information such as the user's name and address. The collection unit can also mask the data to protect the user's privacy. For example, the collection unit masks part of the data to make it impossible to identify individuals. The collection unit can also encrypt the data to protect the user's privacy. For example, the collection unit encrypts the data to prevent third parties from accessing it. In this way, the data is anonymized to protect the user's privacy. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can have the generation AI perform the anonymization process on the data.

[0063] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, data with high importance (heart rate, voice tone, etc.) can be analyzed in detail. Data with low importance (step count, environmental sounds, etc.) can also be analyzed simply. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can cause the generation AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0064] During a question and answer session, the question and answer section can select the most appropriate question by referring to the user's past answer history. For example, the question and answer section can select a relevant question based on the user's past answer history. The question and answer section can also select a question for digging deeper based on the user's past answer history. For example, the question and answer section can select a question that matches the user's interests based on the user's past answer history. This makes it possible to provide highly relevant questions by referring to the past answer history. Some or all of the above-described processing in the question and answer section can be performed using, or without, a generation AI. For example, the question and answer section can cause the generation AI to refer to the past answer history and select the most appropriate question.

[0065] When issuing a warning, the warning unit can select the most appropriate warning by referring to the user's past behavioral history. For example, the warning unit selects a relevant warning based on the user's past behavioral history. The warning unit can also select a warning for digging deeper based on the user's past behavioral history. For example, the warning unit selects a warning tailored to the user's interests based on the user's past behavioral history. This makes it possible to provide a highly relevant warning by referring to the past behavioral history. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the warning unit can cause the generation AI to refer to the past behavioral history and select the most appropriate warning.

[0066] When collecting data, the collection unit can perform filtering based on the user's current activity status and environment. For example, when the user is exercising, the collection unit can prioritize collecting data related to exercise (heart rate, number of steps, etc.). In addition, when the user is resting, the collection unit can prioritize collecting data related to relaxation (heart rate, breathing rate, etc.). For example, when the user is working, the collection unit can prioritize collecting data related to stress (heart rate, voice tone, etc.). This allows for filtering data based on the user's activity status and environment, thereby collecting more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze the activity status and environment and filter the data.

[0067] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a health analysis algorithm is applied to health data (heart rate, sleep time, etc.). The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a natural language processing algorithm to text data. This improves the accuracy of analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can have a generation AI analyze the data category and apply an appropriate algorithm.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects the user's daily behavior and health status. For example, the collection unit collects health data such as the user's number of steps, heart rate, and sleep time. The collection unit can also collect the user's daily voice, social media posts, and text messages. For example, the collection unit collects the user's conversation voice and social media text posts. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, the analysis unit detects changes in the user's emotions based on the collected data. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to detect changes in emotions from the content of the user's SNS posts and text messages. Step 3: The Q&A section uses the generation AI to randomly answer questions based on the data analyzed by the analysis section. For example, in the Q&A section, the generation AI asks the user questions such as, "Is there anything that's been bothering you lately?" and analyzes the answers. Step 4: The warning section determines the risk based on the analysis results obtained by the question and answer section and issues a warning in advance. For example, if the user is feeling stressed or anxious, the warning section will issue a warning such as, "It seems you've been feeling stressed recently. We recommend that you take some time to relax."

[0070] (Example 2) A system according to an embodiment of the present invention collects a user's daily behavior and health status, analyzes them with a generation AI, analyzes their mental state through question and answer sessions, determines whether there is a risk, and issues a warning. For example, the system collects the user's daily behavior and health status. For example, it collects data such as the user's number of steps, heart rate, and sleep time. It also collects the user's daily voice, social media posts, and text messages. Next, the system uses a generation AI to analyze the collected data. The generation AI analyzes the user's mental state based on the collected data. For example, it detects changes in the user's emotions from the content of the user's social media posts and text messages. It also detects signs of stress or anxiety from the user's daily voice. Furthermore, the system uses the generation AI to further analyze the user's mental state through irregular, unstructured question and answer sessions. For example, the generation AI asks the user questions such as, "Is there anything that's been bothering you lately?" and analyzes the user's answers. Finally, the system uses the generation AI to determine whether there is a risk based on the analysis results and issues a warning in advance. For example, if a user is feeling stressed or anxious, the generating AI will issue a warning such as, "You seem to be feeling stressed recently. We recommend that you take some time to relax." This allows the system to comprehensively analyze the user's daily behavior and health status and understand their mental state. This allows the system to comprehensively analyze the user's daily behavior and health status and understand their mental state. For example, the user can understand their mental state and take appropriate measures to maintain their mental health.

[0071] The system according to the embodiment includes a collection unit, an analysis unit, a question and answer unit, and a warning unit. The collection unit collects a user's daily behavior and health status. For example, the collection unit collects health data such as the user's step count, heart rate, and sleep time. The collection unit can also collect the user's daily voice, social media posts, and text messages. For example, the collection unit collects the user's conversational voice and social media text posts. The analysis unit uses a generation AI to analyze the data collected by the collection unit. For example, the analysis unit detects changes in the user's emotions based on the collected data. The generation AI detects changes in emotions from the content of the user's social media posts and text messages using a text generation AI (e.g., LLM) or a multimodal generation AI. The question and answer unit uses the generation AI to randomly answer questions based on the data analyzed by the analysis unit. For example, the question and answer unit uses the generation AI to ask the user a question such as, "Is there anything that's been bothering you lately?" and analyzes the answer. The warning unit determines the risk based on the analysis results obtained by the question and answer unit and issues a warning in advance. For example, if the user is feeling stressed or anxious, the generating AI will issue a warning such as, "You seem to be feeling stressed recently. I recommend you take some time to relax." This allows the system according to the embodiment to comprehensively analyze the user's daily behavior and health condition and understand their mental state.

[0072] The collection unit can collect health data including the user's number of steps, heart rate, and sleep time. Examples of health data include, but are not limited to, the number of steps, heart rate, and sleep time. For example, the collection unit collects the user's number of steps using a smartwatch or a fitness tracker. The collection unit can also collect the user's heart rate using a heart rate sensor. For example, the heart rate sensor measures the user's heart rate in real time and collects the data. The collection unit can also collect the user's sleep time using a sleep tracker. For example, the sleep tracker analyzes the user's sleep patterns and records the sleep time. By collecting the user's health data, the health condition can be understood. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartwatch or fitness tracker into a generation AI and have the generation AI analyze the data.

[0073] The collection unit can collect the user's everyday voice, SNS posts, and text messages. The collection unit, for example, collects the user's everyday voice using a microphone. For example, the collection unit records the user's conversation voice and saves it as data. The collection unit can also collect the user's SNS posts. For example, the collection unit collects the user's text posts and image posts on SNS. The collection unit can also collect the user's text messages. For example, the collection unit collects the user's chat messages and emails. By collecting the user's everyday voice, SNS posts, and text messages, it is possible to understand changes in behavior and emotions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data acquired by a microphone to a generation AI and have the generation AI analyze the voice data.

[0074] The analysis unit can detect changes in the user's emotions based on the collected data. The analysis unit, for example, analyzes collected voice data to detect changes in the user's emotions. For example, the analysis unit analyzes changes in voice tone to detect changes in emotions. The analysis unit can also analyze collected text data to detect changes in the user's emotions. For example, the analysis unit performs sentiment analysis on the text to detect changes in emotions. The analysis unit can also analyze collected SNS posts to detect changes in the user's emotions. For example, the analysis unit analyzes the content of the SNS posts to detect changes in emotions. This makes it possible to understand the user's inner thoughts by detecting changes in their emotions. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input collected data into a generation AI and have the generation AI detect changes in emotions.

[0075] The question and answer unit can use the generation AI to ask the user questions at irregular intervals and analyze the answers. For example, the question and answer unit can use the generation AI to ask the user questions such as, "Is there anything that's been bothering you lately?" and analyze the answers. For example, the question and answer unit can analyze the content of the user's answers to understand the user's state of mind. The question and answer unit can also use the generation AI to ask the user questions such as, "Have you been feeling stressed lately?" and analyze the answers. For example, the question and answer unit can analyze the content of the user's answers to detect signs of stress. The question and answer unit can also use the generation AI to ask the user questions such as, "Has anything fun happened to you recently?" and analyze the answers. For example, the question and answer unit can analyze the content of the user's answers to detect changes in positive emotions. By asking questions at irregular intervals, the user's state of mind can be understood in more detail. Some or all of the above-described processing in the question and answer unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the Q&A section can have a generation AI generate questions and analyze answers.

[0076] The warning unit can issue a warning to the user based on the analysis results. For example, the warning unit can issue a warning to the user based on the analysis results, such as, "You seem to be feeling stressed recently. I recommend you take some time to relax." For example, the warning unit can detect signs of stress in the user and issue an appropriate warning. The warning unit can also issue a warning to the user based on the analysis results, such as, "You seem to be feeling anxious recently. I recommend you talk to someone." For example, the warning unit can detect signs of anxiety in the user and issue an appropriate warning. The warning unit can also issue a warning to the user based on the analysis results, such as, "You seem to be feeling more positive recently. Keep it up." For example, the warning unit can detect changes in the user's positive emotions and provide appropriate feedback. By issuing a warning based on the analysis results, the user can take appropriate measures. Some or all of the above-described processing in the warning unit can be performed using, or without, a generation AI. For example, the warning unit can cause a generation AI to generate and transmit a warning message.

[0077] The collection unit can anonymize data to protect the user's privacy. For example, the collection unit anonymizes the data by deleting the user's personal identifying information. For example, the collection unit anonymizes the data by deleting personal information such as the user's name and address. The collection unit can also mask the data to protect the user's privacy. For example, the collection unit masks part of the data to make it impossible to identify individuals. The collection unit can also encrypt the data to protect the user's privacy. For example, the collection unit encrypts the data to prevent third parties from accessing it. In this way, the data is anonymized, thereby protecting the user's privacy. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can have the generation AI perform the anonymization process on the data.

[0078] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the frequency of data collection based on the emotions. For example, if the user is feeling stressed, the collection unit increases the frequency of data collection to collect detailed information. The collection unit can also reduce the frequency of data collection to reduce the user's burden when the user is relaxed. For example, if the user is in a hurry, the collection unit can temporarily reduce the frequency of data collection to prioritize the user's activities. This allows for more appropriate data collection by adjusting the frequency of data collection based on the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can have a generation AI perform emotion estimation and data collection frequency adjustment.

[0079] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit can analyze the user's past heart rate data and, if an abnormality is found, increase the frequency of heart rate collection. The collection unit can also analyze the user's past sleep data and, if the sleep quality is low, strengthen collection of sleep data. For example, the collection unit can analyze the user's past step count data and, if a lack of exercise is found, increase the frequency of step count collection. In this way, the optimal collection method can be selected by analyzing the past health data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze the past health data and select the optimal collection method.

[0080] The collection unit can perform filtering based on the user's current activity status and environment when collecting data. For example, when the user is exercising, the collection unit prioritizes collecting data related to exercise (heart rate, number of steps, etc.). Furthermore, when the user is resting, the collection unit can prioritize collecting data related to relaxation (heart rate, breathing rate, etc.). For example, when the user is working, the collection unit prioritizes collecting data related to stress (heart rate, voice tone, etc.). This allows for filtering data based on the user's activity status and environment, thereby collecting more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze the activity status and environment and filter the data.

[0081] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit prioritizes collecting voice data. Also, if the user frequently uses text messages, the collection unit can prioritize collecting text data. For example, if the user frequently posts images, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have a generation AI analyze the input method and select the optimal collection means.

[0082] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data related to stress (heart rate, voice tone, etc.). Furthermore, when the user is relaxed, the collection unit can also prioritize collecting data related to relaxation (respiration rate, sleep data, etc.). For example, when the user is excited, the collection unit prioritizes collecting data related to excitement (heart rate, voice tone, etc.). In this way, by determining the priority of data based on the user's emotions, important data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to execute emotion estimation and data priority determination.

[0083] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location (environmental sounds, temperature, etc.). Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the travel destination (tourist information, transportation methods, etc.). For example, when the user is at home, the collection unit prioritizes collecting data related to the home environment (indoor temperature, humidity, etc.). This allows highly relevant data to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze the geographical location information and determine the priority of the data.

[0084] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content posted by the user on social media and detect changes in emotions. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit collects related data based on the user's check-in information on social media. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can cause a generation AI to analyze social media activities and collect related data.

[0085] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the frequency of data collection based on feedback provided by the user in the past. The collection unit can also adjust the type of data to be collected based on feedback provided by the user in the past. For example, the collection unit customizes the collection method (audio, text, image, etc.) based on feedback provided by the user in the past. This allows the collection method to be optimized for the user by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze past feedback and customize the collection method.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize stress-related data in the analysis. Furthermore, if the user is relaxed, the analysis unit can also prioritize relaxation-related data in the analysis. For example, if the user is excited, the analysis unit prioritizes excitement-related data in the analysis. This improves the accuracy of the analysis by adjusting the analysis algorithm based on the user's emotions. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can cause the generation AI to estimate emotions and adjust the analysis algorithm.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit analyzes data with high importance (heart rate, voice tone, etc.) in detail. The analysis unit can also analyze data with low importance (step count, environmental sounds, etc.) in a simplified manner. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a health analysis algorithm to health data (heart rate, sleep time, etc.). The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a natural language processing algorithm to text data. This improves the accuracy of analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can have the generation AI analyze the data category and apply an appropriate algorithm.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. For example, the analysis unit builds a feedback loop to improve the accuracy of the analysis based on the user's past analysis results. This improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to refer to past analysis results and correct the current analysis result.

[0090] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize the analysis of stress-related data. Also, if the user is relaxed, the analysis unit can prioritize the analysis of relaxation-related data. For example, if the user is excited, the analysis unit can prioritize the analysis of excitement-related data. In this way, by determining the analysis priority based on the user's emotions, important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to estimate emotions and determine the analysis priority.

[0091] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. For example, the analysis unit optimally allocates analysis resources based on the time of submission. This allows the most recent data to be analyzed preferentially by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to evaluate the time of data submission and determine the analysis priority.

[0092] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit optimally allocates analysis resources based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to evaluate the relevance of the data and adjust the order of analysis.

[0093] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. For example, the analysis unit adjusts the level of detail of the analysis results according to the user's level of expertise. This allows for analysis results that are easy to understand to be provided by providing analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can cause the generation AI to evaluate the level of expertise and adjust the analysis results.

[0094] The question and answer unit can estimate the user's emotions and adjust the content of questions based on the estimated user emotions. For example, if the user is feeling stressed, the question and answer unit can ask questions to help the user relax. Furthermore, if the user is relaxed, the question and answer unit can ask questions to gain deeper insight. For example, if the user is excited, the question and answer unit can ask questions to help the user calm down. This allows the content of questions to be adjusted based on the user's emotions, thereby providing more appropriate questions. Some or all of the above-described processing in the question and answer unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the question and answer unit can cause the generation AI to estimate emotions and adjust the content of questions.

[0095] During a question and answer session, the question and answer unit can select the most appropriate question by referring to the user's past answer history. For example, the question and answer unit selects relevant questions based on the user's past answer history. The question and answer unit can also select questions for digging deeper based on the user's past answer history. For example, the question and answer unit selects questions that match the user's interests based on the user's past answer history. This makes it possible to provide highly relevant questions by referring to the past answer history. Some or all of the above-described processing in the question and answer unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the question and answer unit can cause the generation AI to refer to the past answer history and select the most appropriate question.

[0096] The question and answer unit can customize the content of questions based on the user's current situation during the question and answer session. For example, if the user is at work, the question and answer unit can ask work-related questions. Also, if the user is on a break, the question and answer unit can ask questions to help the user relax. For example, if the user is exercising, the question and answer unit can ask exercise-related questions. This allows questions to be customized based on the user's current situation, thereby providing more appropriate questions. Some or all of the above-described processing in the question and answer unit may be performed using, or without, a generation AI. For example, the question and answer unit can cause the generation AI to analyze the current situation and customize the content of the questions.

[0097] The question and answer unit can improve the questioning method by reflecting user feedback during the question and answer session. For example, the question and answer unit adjusts the content of the questions based on feedback previously provided by the user. The question and answer unit can also adjust the frequency of questions based on feedback previously provided by the user. For example, the question and answer unit improves the format of the questions (audio, text, etc.) based on feedback previously provided by the user. This allows the questioning method to be optimized by reflecting the user's feedback. Some or all of the above-mentioned processing in the question and answer unit may be performed using, or without, a generation AI, for example. For example, the question and answer unit can cause the generation AI to analyze the feedback and improve the questioning method.

[0098] The question and answer unit can estimate the user's emotions and adjust the frequency of questions based on the estimated user emotions. For example, if the user is feeling stressed, the question and answer unit can reduce the frequency of questions to reduce the burden on the user. The question and answer unit can also increase the frequency of questions to collect more detailed information if the user is relaxed. For example, if the user is excited, the question and answer unit can adjust the frequency of questions to provide the user with time to calm down. In this way, the burden on the user can be reduced by adjusting the frequency of questions based on the user's emotions. Some or all of the above-mentioned processing in the question and answer unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the question and answer unit can cause the generation AI to estimate emotions and adjust the frequency of questions.

[0099] The question and answer unit can provide optimal questions during a question and answer session by taking into account the user's geographical location information. For example, if the user is in a specific location, the question and answer unit poses questions related to that location. Furthermore, if the user is traveling, the question and answer unit can also pose questions related to the user's travel destination. For example, if the user is at home, the question and answer unit poses questions related to the user's home environment. This allows highly relevant questions to be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the question and answer unit may be performed using, or without, a generation AI. For example, the question and answer unit can cause the generation AI to analyze the geographical location information and generate optimal questions.

[0100] The question and answer unit can generate relevant questions by analyzing the user's social media activity during the question and answer session. For example, the question and answer unit can analyze the content posted by the user on social media and generate relevant questions. The question and answer unit can also generate relevant questions by referring to the activity of the user's friends on social media. For example, the question and answer unit generates relevant questions based on the user's check-in information on social media. This makes it possible to provide highly relevant questions by analyzing social media activity. Some or all of the above-described processing in the question and answer unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the question and answer unit can cause a generation AI to analyze social media activity and generate relevant questions.

[0101] The question and answer unit can customize the content of questions during a question and answer session by reflecting the user's past feedback. For example, the question and answer unit adjusts the content of questions based on feedback provided by the user in the past. The question and answer unit can also adjust the frequency of questions based on feedback provided by the user in the past. For example, the question and answer unit improves the format of questions (audio, text, etc.) based on feedback provided by the user in the past. This allows the content of questions to be optimized for the user by reflecting past feedback. Some or all of the above-mentioned processing in the question and answer unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the question and answer unit can cause a generation AI to analyze past feedback and customize the content of questions.

[0102] The warning unit can estimate the user's emotions and adjust the content of the warning based on the estimated user emotions. For example, if the user is feeling stressed, the warning unit issues a warning to relax. Furthermore, if the user is relaxed, the warning unit can also issue a warning to urge caution. For example, if the user is excited, the warning unit issues a warning to calm down. In this way, by adjusting the content of the warning based on the user's emotions, a more appropriate warning can be provided. Some or all of the above-described processing in the warning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the warning unit can cause the generation AI to estimate emotions and adjust the content of the warning.

[0103] When issuing a warning, the warning unit can select the most appropriate warning by referring to the user's past behavioral history. The warning unit, for example, selects a relevant warning based on the user's past behavioral history. The warning unit can also select a warning for digging deeper based on the user's past behavioral history. For example, the warning unit selects a warning tailored to the user's interests based on the user's past behavioral history. This makes it possible to provide a highly relevant warning by referring to the past behavioral history. Some or all of the above-described processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the warning unit can cause the generation AI to refer to the past behavioral history and select the most appropriate warning.

[0104] The warning unit can customize the content of the warning based on the user's current situation when issuing a warning. For example, if the user is at work, the warning unit issues a work-related warning. Also, if the user is taking a break, the warning unit can issue a relaxation warning. For example, if the user is exercising, the warning unit issues an exercise-related warning. This allows for customizing the warning based on the user's current situation, thereby providing a more appropriate warning. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can cause the generation AI to analyze the current situation and customize the warning content.

[0105] The warning unit can improve the warning method by reflecting user feedback when issuing a warning. For example, the warning unit can adjust the content of the warning based on feedback previously provided by the user. The warning unit can also adjust the frequency of warnings based on feedback previously provided by the user. For example, the warning unit can improve the format of the warning (audio, text, etc.) based on feedback previously provided by the user. This allows the warning method to be optimized by reflecting user feedback. Some or all of the above-mentioned processing in the warning unit can be performed using, or without, a generation AI. For example, the warning unit can cause the generation AI to analyze the feedback and improve the warning method.

[0106] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated user's emotions. For example, if the user is feeling stressed, the warning unit can prioritize warnings related to stress. Also, if the user is relaxed, the warning unit can prioritize warnings related to relaxation. For example, if the user is excited, the warning unit can prioritize warnings related to excitement. In this way, by determining the priority of warnings based on the user's emotions, important warnings can be provided preferentially. Some or all of the above-described processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the warning unit can cause the generation AI to estimate emotions and determine the priority of warnings.

[0107] When issuing a warning, the warning unit can provide an optimal warning by taking into account the user's geographical location information. For example, if the user is in a specific location, the warning unit can issue a warning related to that location. Also, if the user is traveling, the warning unit can issue a warning related to the user's travel destination. For example, if the user is at home, the warning unit can issue a warning related to the home environment. In this way, by taking the user's geographical location information into account, highly relevant warnings can be provided. Some or all of the above-described processing in the warning unit may be performed using, or without, a generation AI. For example, the warning unit can cause the generation AI to analyze the geographical location information and generate an optimal warning.

[0108] At the time of issuing a warning, the warning unit can analyze the user's social media activity and generate a relevant warning. For example, the warning unit can analyze the content posted by the user on social media and generate a relevant warning. The warning unit can also generate a relevant warning by referring to the activity of the user's friends on social media. For example, the warning unit generates a relevant warning based on the user's check-in information on social media. In this way, by analyzing social media activity, highly relevant warnings can be provided. Some or all of the above-described processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the warning unit can cause a generation AI to analyze social media activity and generate a relevant warning.

[0109] The warning unit can customize the content of the warning by reflecting the user's past feedback when issuing a warning. The warning unit can, for example, adjust the content of the warning based on feedback provided by the user in the past. The warning unit can also adjust the frequency of warnings based on feedback provided by the user in the past. For example, the warning unit can improve the format of the warning (audio, text, etc.) based on feedback provided by the user in the past. This allows the content of the warning to be optimized for the user by reflecting past feedback. Some or all of the above-mentioned processing in the warning unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the warning unit can cause the generation AI to analyze past feedback and customize the content of the warning. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, question and answer unit, and warning unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's daily behavior and health status using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by a generation AI. The question and answer unit is realized, for example, by the control unit 46A of the smart device 14, and the generation AI asks questions to the user at random times and analyzes the answers. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines danger based on the analysis results and issues a warning in advance. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, question and answer unit, and warning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's daily behavior and health status using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by a generation AI. The question and answer unit is realized, for example, by the control unit 46A of the smart glasses 214, and the generation AI asks questions to the user at random times and analyzes the answers. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines danger based on the analysis results and issues a warning in advance. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, question and answer unit, and warning unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's daily behavior and health status using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by a generation AI. The question and answer unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and the generation AI asks questions to the user at random times and analyzes the answers. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines danger based on the analysis results and issues a warning in advance. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, question and answer unit, and warning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's daily behavior and health status using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the collected data is analyzed by a generation AI. The question and answer unit is realized, for example, by the control unit 46A of the robot 414, and the generation AI asks questions to the user at random times and analyzes the answers. The warning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines danger based on the analysis results and issues a warning in advance.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis of stress-related data can be prioritized. Also, if the user is relaxed, the analysis of relaxation-related data can be prioritized. For example, if the user is excited, the analysis of excitement-related data can be prioritized. In this way, by determining the analysis priority based on the user's emotions, important data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can cause the generation AI to estimate emotions and determine the analysis priority.

[0112] The collection unit can analyze the user's past health data and select the optimal collection method. For example, it can analyze the user's past heart rate data and, if an abnormality is found, increase the frequency of heart rate collection. It can also analyze the user's past sleep data and, if the sleep quality is low, strengthen the collection of sleep data. For example, it can analyze the user's past step count data and, if a lack of exercise is found, increase the frequency of step count collection. In this way, by analyzing the past health data, the optimal collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to analyze the past health data and select the optimal collection method.

[0113] The question and answer unit can estimate the user's emotions and adjust the content of questions based on the estimated user emotions. For example, if the user is feeling stressed, it can ask questions to help the user relax. Also, if the user is relaxed, it can ask questions to gain deeper insight. For example, if the user is excited, it can ask questions to help the user calm down. In this way, by adjusting the content of questions based on the user's emotions, more appropriate questions can be provided. Some or all of the above-mentioned processing in the question and answer unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the question and answer unit can cause a generation AI to estimate emotions and adjust the content of questions.

[0114] The warning unit can estimate the user's emotions and adjust the content of the warning based on the estimated user emotions. For example, if the user is feeling stressed, a warning to relax can be issued. Also, if the user is relaxed, a warning to urge caution can be issued. For example, if the user is excited, a warning to calm down can be issued. In this way, by adjusting the content of the warning based on the user's emotions, a more appropriate warning can be provided. Some or all of the above-mentioned processing in the warning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the warning unit can cause the generation AI to estimate emotions and adjust the content of the warning.

[0115] The collection unit can anonymize the data to protect the user's privacy. For example, the collection unit anonymizes the data by deleting the user's personal identifying information. For example, the collection unit anonymizes the data by deleting personal information such as the user's name and address. The collection unit can also mask the data to protect the user's privacy. For example, the collection unit masks part of the data to make it impossible to identify individuals. The collection unit can also encrypt the data to protect the user's privacy. For example, the collection unit encrypts the data to prevent third parties from accessing it. In this way, the data is anonymized to protect the user's privacy. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can have the generation AI perform the anonymization process on the data.

[0116] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, data with high importance (heart rate, voice tone, etc.) can be analyzed in detail. Data with low importance (step count, environmental sounds, etc.) can also be analyzed simply. For example, the analysis unit optimally allocates analysis resources according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can cause the generation AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0117] During a question and answer session, the question and answer section can select the most appropriate question by referring to the user's past answer history. For example, the question and answer section can select a relevant question based on the user's past answer history. The question and answer section can also select a question for digging deeper based on the user's past answer history. For example, the question and answer section can select a question that matches the user's interests based on the user's past answer history. This makes it possible to provide highly relevant questions by referring to the past answer history. Some or all of the above-described processing in the question and answer section can be performed using, or without, a generation AI. For example, the question and answer section can cause the generation AI to refer to the past answer history and select the most appropriate question.

[0118] When issuing a warning, the warning unit can select the most appropriate warning by referring to the user's past behavioral history. For example, the warning unit selects a relevant warning based on the user's past behavioral history. The warning unit can also select a warning for digging deeper based on the user's past behavioral history. For example, the warning unit selects a warning tailored to the user's interests based on the user's past behavioral history. This makes it possible to provide a highly relevant warning by referring to the past behavioral history. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the warning unit can cause the generation AI to refer to the past behavioral history and select the most appropriate warning.

[0119] When collecting data, the collection unit can perform filtering based on the user's current activity status and environment. For example, when the user is exercising, the collection unit can prioritize collecting data related to exercise (heart rate, number of steps, etc.). In addition, when the user is resting, the collection unit can prioritize collecting data related to relaxation (heart rate, breathing rate, etc.). For example, when the user is working, the collection unit can prioritize collecting data related to stress (heart rate, voice tone, etc.). This allows for filtering data based on the user's activity status and environment, thereby collecting more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause a generation AI to analyze the activity status and environment and filter the data.

[0120] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a health analysis algorithm is applied to health data (heart rate, sleep time, etc.). The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit applies a natural language processing algorithm to text data. This improves the accuracy of analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can have a generation AI analyze the data category and apply an appropriate algorithm.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects the user's daily behavior and health status. For example, the collection unit collects health data such as the user's number of steps, heart rate, and sleep time. The collection unit can also collect the user's daily voice, social media posts, and text messages. For example, the collection unit collects the user's conversation voice and social media text posts. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, the analysis unit detects changes in the user's emotions based on the collected data. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to detect changes in emotions from the content of the user's SNS posts and text messages. Step 3: The Q&A section uses the generation AI to randomly answer questions based on the data analyzed by the analysis section. For example, in the Q&A section, the generation AI asks the user questions such as, "Is there anything that's been bothering you lately?" and analyzes the answers. Step 4: The warning section determines the risk based on the analysis results obtained by the question and answer section and issues a warning in advance. For example, if the user is feeling stressed or anxious, the warning section will issue a warning such as, "It seems you've been feeling stressed recently. We recommend that you take some time to relax."

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information about the user's daily behavior and health condition; an analysis unit that analyzes the data collected by the collection unit; a question and answer section that randomly asks and answers questions based on the data analyzed by the analysis section; a warning unit that determines risk based on the analysis result obtained by the question and answer unit and issues a warning in advance. A system characterized by:

2. The collecting unit Collecting health data, including user steps, heart rate, and sleep duration 2. The system of claim 1.

3. The collecting unit Collect users' daily voice, social media posts, and text messages 2. The system of claim 1.

4. The analysis unit Detect changes in user emotions based on collected data 2. The system of claim 1.

5. The question and answer section may: Generative AI is used to randomly ask questions to users and analyze their answers.

2. The system of claim 1.

6. The warning unit Issue a warning to the user based on the analysis results 2. The system of claim 1.

7. The collecting unit Anonymize data to protect user privacy 2. The system of claim 1.

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

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