System

Abstract

An object of the system according to the embodiment is to objectively detect a sign of harassment and notify a warning at an early stage.SOLUTION: A system includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects biological data. The analyzer analyzes the biological data collected by the collector. The detection unit detects a sign of harassment based on the data analyzed by the analysis unit. The notification unit notifies a warning based on the result detected by the detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to objectively detect signs of harassment and take early action.

[0005] The system according to the embodiment aims to objectively detect signs of harassment and issue an early warning. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects biometric data. The analysis unit analyzes the biometric data collected by the collection unit. The detection unit detects signs of harassment based on the data analyzed by the analysis unit. The notification unit issues a warning based on the results detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can objectively detect signs of harassment and issue an early warning. [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 harassment detection system according to an embodiment of the present invention utilizes the functions of a wearable device, smartphone, or camera (video and audio recording) to objectively determine whether a person is experiencing harassment. The harassment detection system collects biometric data such as heart rate, body temperature, and breathing patterns, and uses AI to analyze the data and identify differences from normal behavior. Furthermore, AI analyzes the camera's video and audio data to detect signs of harassment. This allows for the determination of possible harassment, even when the individual is unaware of it. Finally, it sends a warning alert to the victim and the harasser, leading to the prevention of serious harassment and harm. For example, a harassment detection system uses a wearable device or smartphone worn by the user on a daily basis to collect biometric data such as heart rate, body temperature, and breathing patterns in real time. Next, AI analyzes the collected data and detects signs of stress and tension by analyzing fluctuations in heart rate and body temperature. Furthermore, AI analyzes the camera's video and audio data to detect offensive words and tones through audio analysis, and intimidating attitudes and behaviors through video analysis. This allows for a clear understanding of the specific circumstances of harassment. Finally, if AI detects signs of harassment, it will send a real-time alert to the victim and perpetrator. This can lead to the prevention of serious harassment and perpetrators. As a result, the harassment detection system enables early detection and prevention of harassment, protecting the mental and physical health of victims. In addition, by issuing a warning to perpetrators, it is expected to have a deterrent effect on harassment. For example, it can prevent harassment from occurring in environments such as the workplace or school.

[0029] A harassment detection system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects biometric data. Examples of biometric data include, but are not limited to, heart rate, body temperature, and breathing patterns. The collection unit collects data such as heart rate, body temperature, and breathing patterns in real time using, for example, a wearable device or a smartphone. The collection unit can also collect video and audio data using a camera. For example, the collection unit acquires heart rate data from a smartwatch worn by a user. The collection unit can also acquire body temperature data using a smartphone sensor. The collection unit can also acquire video and audio data using a camera. The analysis unit analyzes the biometric data collected by the collection unit. For example, the analysis unit can analyze patterns of heart rate and body temperature fluctuations using AI to identify differences from normal conditions. For example, the analysis unit inputs heart rate data into an AI model to detect signs of stress or tension. The analysis unit can also analyze body temperature data to detect abnormal increases. The analysis unit can also analyze breathing pattern data and detect abnormal fluctuations. The detection unit detects signs of harassment based on the data analyzed by the analysis unit. The detection unit, for example, detects offensive words or tones through audio analysis. For example, the detection unit can analyze recorded data using AI to detect offensive words. The detection unit can also detect intimidating attitudes or behaviors through video analysis. For example, the detection unit can analyze recorded data using AI to detect intimidating attitudes. The notification unit issues a warning based on the results detected by the detection unit. For example, the notification unit can display a warning to the victim, such as "Possible harassment." The notification unit can also notify the perpetrator, such as "Harassment has been detected." For example, the notification unit can display a warning using a smartphone's notification function. The notification unit can also notify the perpetrator by email or SMS. As a result, the harassment detection system according to the embodiment can objectively determine signs of harassment and issue a warning by collecting, analyzing, detecting, and notifying biometric data.

[0030] The collection unit can collect biometric data including heart rate, body temperature, and breathing patterns. The collection unit, for example, uses a wearable device to measure heart rate. For example, the collection unit collects heart rate data in real time using a smartwatch. The collection unit can also use a smartphone sensor to measure body temperature. For example, the collection unit collects body temperature data using a temperature sensor in the smartphone. The collection unit can also use a dedicated device to measure breathing patterns. For example, the collection unit collects breathing pattern data using a breathing sensor. By collecting biometric data such as heart rate, body temperature, and breathing patterns, the user's condition can be understood in detail. Some or all of the above-mentioned 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 heart rate data acquired from the wearable device into AI and have the AI ​​collect the data.

[0031] The analysis unit can analyze the collected biometric data and recognize differences from normal times, which serve as a reference. The analysis unit, for example, uses AI to analyze heart rate data and recognize differences from normal times. For example, the analysis unit inputs the heart rate data into an AI model to detect signs of stress or tension. The analysis unit can also analyze body temperature data and detect abnormal increases. For example, the analysis unit can analyze body temperature data using AI to detect abnormal increases. The analysis unit can also analyze breathing pattern data and detect abnormal fluctuations. For example, the analysis unit can analyze breathing pattern data using AI to detect abnormal fluctuations. By recognizing differences from normal times, an abnormal condition can be detected. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected biometric data into AI and have the AI ​​analyze the data.

[0032] The detection unit can detect signs of harassment through voice analysis. For example, the detection unit can analyze recorded data using AI to detect offensive words and tones. For example, the detection unit can input recorded data into an AI model to detect offensive words. The detection unit can also extract voice features to detect signs of harassment. For example, the detection unit can analyze the tone and speed of the voice to detect offensive tones. The detection unit can also detect specific words and phrases using voice recognition technology. For example, the detection unit can detect offensive words using voice recognition technology. This makes it possible to detect signs of harassment through voice analysis. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input recorded data into AI and have the AI ​​perform voice analysis.

[0033] The detection unit can detect signs of harassment through video analysis. The detection unit, for example, uses AI to analyze recorded data and detect intimidating attitudes and behaviors. For example, the detection unit inputs recorded data into an AI model to detect intimidating attitudes. The detection unit can also detect changes in facial expressions using facial recognition technology. For example, the detection unit can detect intimidating facial expressions using facial recognition technology. The detection unit can also detect intimidating behavior using motion analysis technology. For example, the detection unit can detect intimidating behavior using motion analysis technology. This makes it possible to detect signs of harassment through video analysis. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input recorded data into AI and have the AI ​​perform video analysis.

[0034] The notification unit can issue a warning to the victim. For example, the notification unit displays a warning to the victim saying, "Possible harassment." For example, the notification unit displays the warning using a smartphone's notification function. The notification unit can also issue the warning using email or SMS. For example, the notification unit issues the warning to the victim using email. The notification unit can also issue the warning using a voice alert. For example, the notification unit issues the warning to the victim using a voice alert. In this way, by notifying the victim of the warning, measures can be taken early. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can generate the content of the warning using AI and notify the victim.

[0035] The notification unit can issue a warning to the perpetrator. For example, the notification unit notifies the perpetrator that "harassment has been detected." For example, the notification unit displays the warning using a smartphone's notification function. The notification unit can also issue the warning using email or SMS. For example, the notification unit issues the warning to the perpetrator using email. The notification unit can also issue the warning using a voice alert. For example, the notification unit issues the warning to the perpetrator using a voice alert. In this way, by notifying the perpetrator of the warning, harassment can be deterred. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can generate the content of the warning using AI and notify the perpetrator.

[0036] The collection unit can analyze the user's past biometric data and select an appropriate collection method. The collection unit can analyze the user's past biometric data using, for example, AI. For example, the collection unit can analyze past heart rate data and increase the collection frequency if an abnormal fluctuation is detected. The collection unit can also shorten the collection interval if an abnormal increase is detected based on past body temperature data. Furthermore, the collection unit can analyze past breathing patterns and collect data in real time if an abnormal pattern is detected. This allows the optimal collection method to be selected by analyzing past data. Some or all of the above-mentioned 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 input past biometric data into AI and have the AI ​​select the collection method.

[0037] When collecting biometric data, the collection unit can filter the biometric data based on the user's current activity status and environment. The collection unit, for example, uses AI to analyze the user's current activity status and environment. For example, the collection unit analyzes the user's activity status using a pedometer or GPS data. The collection unit can also collect environmental data using a temperature sensor or humidity sensor. For example, the collection unit measures environmental temperature using a temperature sensor. The collection unit filters the biometric data based on the user's activity status and environmental data. For example, when the user is exercising, the collection unit filters heart rate data to remove abnormal values. When the user is taking a break, the collection unit can filter body temperature data to use normal values ​​as a reference. Furthermore, when the user is in a meeting, the collection unit can filter breathing pattern data to detect tension. This allows for more accurate data collection by filtering data based on the activity status and environment. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input activity status and environmental data into AI and have the AI ​​perform data filtering.

[0038] When collecting biometric data, the collection unit can select an appropriate collection means depending on the user's input method. The collection unit, for example, uses AI to analyze the user's input method. For example, the collection unit analyzes methods such as voice input, text input, and image input. The collection unit selects an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit analyzes the voice data to evaluate the stress level. Also, if the user uses text input, the collection unit can analyze the input speed and content to evaluate the tension level. Furthermore, if the user uses image input, the collection unit can perform facial expression analysis to evaluate the emotional state. This enables efficient data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input data into AI and have the AI ​​select the collection means.

[0039] When collecting biometric data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, uses AI to analyze the user's geographical location information. For example, the collection unit acquires the user's location information using GPS data or an IP address. The collection unit collects biometric data based on the user's geographical location information. For example, when the user is at work, the collection unit prioritizes collecting heart rate and breathing pattern data. Also, when the user is at home, the collection unit can prioritize collecting body temperature and heart rate data. Furthermore, when the user is in a public place, the collection unit can prioritize collecting breathing pattern and body temperature data. In this way, by taking the geographical location information into account, highly relevant data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's location information into AI and have the AI ​​collect data.

[0040] The collection unit can analyze the user's social media activity and collect related data when collecting biometric data. The collection unit, for example, uses AI to analyze the user's social media activity. For example, the collection unit analyzes the user's post content, the number of likes, the number of followers, etc. The collection unit collects biometric data based on the user's social media activity. For example, if the user posts on social media that they are feeling stressed, the collection unit can collect heart rate and breathing pattern data. Also, if the user posts that they are relaxed, the collection unit can collect body temperature and heart rate data. Furthermore, if the user posts that they are feeling nervous, the collection unit can collect breathing pattern and body temperature data. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect the data.

[0041] When collecting biometric data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, uses AI to analyze the user's past feedback. For example, the collection unit analyzes questionnaire surveys and user reviews. The collection unit customizes the biometric data collection method based on the user's past feedback. For example, if the user previously requested collection of heart rate data, the collection unit can prioritize collecting heart rate data. Also, if the user previously requested collection of body temperature data, the collection unit can also prioritize collecting body temperature data. Furthermore, if the user previously requested collection of breathing pattern data, the collection unit can also prioritize collecting breathing pattern data. In this way, the collection method can be customized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into AI and have the AI ​​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 biometric data. The analysis unit, for example, uses AI to evaluate the importance of the biometric data. For example, the analysis unit evaluates the degree of abnormality or urgency of heart rate data. The analysis unit adjusts the level of detail of the analysis based on the importance. For example, if heart rate data is important, the analysis unit performs a detailed analysis. Also, if body temperature data is important, the analysis unit can also perform a detailed analysis. Furthermore, if breathing pattern data is important, the analysis unit can also perform a detailed analysis. In this way, adjusting the level of detail of the analysis based on the importance enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input biometric data to AI and have the AI ​​adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biometric data. The analysis unit, for example, uses AI to classify the categories of biometric data. For example, the analysis unit classifies categories such as heart rate data, body temperature data, and breathing pattern data. The analysis unit applies different analysis algorithms depending on the category. For example, a heart rate variability analysis algorithm can be applied to heart rate data. A body temperature variability analysis algorithm can also be applied to body temperature data. Furthermore, a respiratory variability analysis algorithm can also be applied to breathing pattern data. In this way, highly accurate analysis is possible by applying an analysis algorithm depending on the category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the biometric data into AI and have the AI ​​apply the analysis 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, uses AI to analyze the user's past analysis results. For example, the analysis unit can improve the current analysis accuracy by referring to past heart rate analysis results. The current analysis accuracy can also be improved by referring to past body temperature analysis results. Furthermore, the current analysis accuracy can also be improved by referring to past breathing pattern analysis results. In this way, by referring to past analysis results, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the biometric data was collected. The analysis unit, for example, uses AI to analyze the time when the biometric data was collected. For example, the analysis unit analyzes the timestamp and collection frequency of the data. The analysis unit determines the priority of analysis based on the time of collection. For example, the analysis unit prioritizes analysis of recently collected heart rate data. It can also prioritize analysis of recently collected body temperature data. It can also prioritize analysis of recently collected breathing pattern data. In this way, by determining the priority of analysis based on the time of collection, it is possible to prioritize analysis of the most recent data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the biometric data into AI and have the AI ​​determine the priority of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the biometric data. The analysis unit, for example, uses AI to evaluate the relevance of the biometric data. For example, the analysis unit calculates a correlation coefficient between heart rate data and breathing pattern data. The analysis unit adjusts the order of analysis based on the relevance. For example, if heart rate data and breathing pattern data are correlated, the analysis unit analyzes them simultaneously. Also, if body temperature data and heart rate data are correlated, the analysis unit can analyze them simultaneously. Furthermore, if breathing pattern data and body temperature data are correlated, the analysis unit can analyze them simultaneously. This enables efficient analysis by adjusting the order of analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input biometric data to AI and have the AI ​​adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses AI to evaluate the user's level of expertise. For example, the analysis unit analyzes the user's occupation and past learning history. The analysis unit adjusts the use of technical terms in the analysis according to the level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can select appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's technical expertise data into AI and have the AI ​​execute the use of technical terms.

[0048] The detection unit can improve detection accuracy by taking into account the interrelationships of biometric data during detection. The detection unit, for example, uses AI to evaluate the interrelationships of biometric data. For example, the detection unit performs correlation analysis between heart rate data and breathing pattern data. The detection unit improves detection accuracy by taking into account the interrelationships of biometric data. For example, heart rate and breathing pattern data can be combined to improve detection accuracy. Body temperature and heart rate data can also be combined to improve detection accuracy. Furthermore, breathing pattern and body temperature data can be combined to improve detection accuracy. In this way, by taking the interrelationships into account, detection accuracy can be improved. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input biometric data into AI and have the AI ​​evaluate the interrelationships.

[0049] The detection unit can perform detection while taking into account attribute information of the biometric data submitter. The detection unit, for example, uses AI to analyze the attribute information of the biometric data submitter. For example, the detection unit analyzes attribute information such as age, gender, and occupation. The detection unit performs detection while taking into account the attribute information of the submitter. For example, if the submitter is young, the detection unit strictly detects heart rate fluctuations. Also, if the submitter is elderly, the detection unit can also detect body temperature fluctuations more gradually. Furthermore, if the submitter has a specific health condition, detection can be performed according to that condition. This enables individually appropriate detection by taking attribute information into account. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the submitter's attribute information into AI and have the AI ​​adjust the detection.

[0050] The detection unit can weight the detection based on the frequency of submission of the biometric data at the time of detection. The detection unit, for example, uses AI to analyze the frequency of submission of the biometric data. For example, the detection unit analyzes the number of times data is submitted and the submission interval. The detection unit weights the detection based on the frequency of submission. For example, a high weight is assigned to heart rate data that is frequently submitted. A high weight can also be assigned to body temperature data that is frequently submitted. Furthermore, a high weight can be assigned to breathing pattern data that is frequently submitted. In this way, by weighting based on the frequency of submission, the importance of frequently submitted data can be increased. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the frequency of submission of the biometric data to AI and have the AI ​​adjust the weighting.

[0051] The detection unit can perform detection taking into account the geographical distribution of the biometric data. The detection unit, for example, uses AI to analyze the geographical distribution of the biometric data. For example, the detection unit performs data distribution by region and geographic cluster analysis. The detection unit performs detection taking into account the geographical distribution. For example, if the user is in a specific region, the detection unit prioritizes detecting data from that region. Also, if the user is moving, the detection unit can detect data from the user's destination in real time. Furthermore, if the user is in multiple regions, the detection unit can integrate and detect data from each region. This allows detection to be performed according to the characteristics of each region by taking the geographical distribution into account. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the geographical distribution of the biometric data into AI and have the AI ​​adjust the detection.

[0052] During detection, the detection unit can improve the accuracy of detection by referring to literature related to the biometric data. The detection unit, for example, uses AI to refer to literature related to the biometric data. For example, the detection unit refers to medical literature related to heart rate data. The detection unit improves the accuracy of detection by referring to the related literature. For example, the detection unit improves the detection accuracy by referring to medical literature related to heart rate data. The detection accuracy can also be improved by referring to medical literature related to body temperature data. Furthermore, the detection accuracy can be improved by referring to medical literature related to breathing pattern data. In this way, the detection accuracy can be improved by referring to the related literature. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit inputs the biometric data into AI and causes the AI ​​to refer to the related literature.

[0053] The detection unit can perform detection taking into consideration the market value of the biometric data. The detection unit, for example, uses AI to evaluate the market value of the biometric data. For example, the detection unit evaluates the commercial value and demand forecast of the data. The detection unit performs detection taking into consideration market value. For example, it may preferentially detect heart rate data, which has a high market value. It may also preferentially detect body temperature data, which has a high market value. It may also preferentially detect breathing pattern data, which has a high market value. In this way, important data can be preferentially detected by taking market value into consideration. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit may input the market value of the biometric data into AI and have the AI ​​adjust the detection.

[0054] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. The notification unit, for example, uses AI to analyze the user's past notification history. For example, the notification unit analyzes the content and timing of past notifications. The notification unit selects the optimal notification method based on the user's past notification history. For example, the notification unit prioritizes the selection of notification methods that the user has previously preferred. The notification unit can also exclude notification methods that the user has previously avoided. Furthermore, the notification unit can analyze the user's past notification history and suggest the optimal notification method. In this way, the optimal notification method for the user can be selected by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's notification history data into AI and have the AI ​​select the notification method.

[0055] The notification unit can customize the notification content according to the user's current task when notifying the user. The notification unit, for example, uses AI to analyze the user's current task. For example, the notification unit analyzes the user's schedule and activities. The notification unit customizes the notification content according to the user's current task. For example, if the user is in a meeting, the notification unit selects a quiet notification method. Also, if the user is exercising, the notification unit can notify the user by vibration or sound. Furthermore, if the user is taking a break, the notification unit can display detailed notification content. This allows the notification content to be customized according to the current task, enabling appropriate notifications for the user. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's task data into AI and have the AI ​​customize the notification content.

[0056] The notification unit can select the optimal notification method by taking into account the user's device information when notifying the user. The notification unit, for example, uses AI to analyze the user's device information. For example, the notification unit analyzes the device type and OS version. The notification unit selects the optimal notification method based on the user's device information. For example, if the user is using a smartphone, the notification unit displays the notification on the screen. Also, if the user is using a smartwatch, the notification unit can notify by vibration. Furthermore, if the user is using a tablet, the notification unit can display the notification on a larger screen. In this way, the optimal notification method for the user can be selected by taking into account the device information. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's device information into AI and have the AI ​​select the notification method.

[0057] The notification unit can select the optimal notification method by taking into account the user's device information when notifying the user. The notification unit, for example, uses AI to analyze the user's device information. For example, the notification unit analyzes the device type and OS version. The notification unit selects the optimal notification method based on the user's device information. For example, if the user is using a smartphone, the notification unit displays the notification on the screen. Also, if the user is using a smartwatch, the notification unit can notify by vibration. Furthermore, if the user is using a tablet, the notification unit can display the notification on a larger screen. In this way, the optimal notification method for the user can be selected by taking into account the device information. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's device information into AI and have the AI ​​select the notification method.

[0058] The notification unit can provide multilingual notification content according to the user's language setting at the time of notification. The notification unit, for example, uses AI to analyze the user's language setting. For example, the notification unit analyzes the user's device settings and application settings. The notification unit provides multilingual notification content based on the user's language setting. For example, the notification unit automatically sets the notification language based on the user's device language setting. In addition, if the user uses multiple languages, the notification unit can provide a language switching function. Furthermore, if the user selects a specific language, the notification unit can provide notifications in that language. This enables multilingual notification according to the language setting, making it easy for the user to understand. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without AI. For example, the notification unit can input the user's language setting data into AI and have the AI ​​provide multilingual notification content.

[0059] The notification unit can analyze the user's social media activity and provide related information at the time of notification. The notification unit can, for example, use AI to analyze the user's social media activity. For example, the notification unit can analyze the user's posts, the number of likes, the number of followers, etc. The notification unit can provide related information based on the user's social media activity. For example, the notification unit can provide information about places the user has checked in on social media. The notification unit can also analyze the user's social media posts to provide information about related tourist spots and stores. Furthermore, the notification unit can provide information about related places and events based on the activities of the user's friends on social media. In this way, related information can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media data into AI and have the AI ​​provide related information.

[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 behavioral patterns and optimize the collection of biometric data under specific circumstances. For example, the collection unit can identify situations in which the user felt stressed in the past and focus on collecting data on heart rate and breathing patterns in those situations. The collection unit can also identify situations in which the user is relaxed and collect body temperature and heart rate data in those situations. Furthermore, the collection unit can identify situations in which the user is tense and collect breathing pattern data in real time in those situations. This allows for more accurate data collection by optimizing data collection based on past behavioral patterns.

[0062] The collection unit can adjust the method of collecting biometric data based on the user's current activity status and environment. For example, when the user is exercising, the collection unit can frequently collect heart rate data. When the user is resting, the collection unit can collect body temperature data at regular intervals. Furthermore, when the user is in a meeting, the collection unit can collect breathing pattern data in real time. This allows for more accurate data collection by adjusting the data collection method based on the user's activity status and environment.

[0063] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the current analysis accuracy by referring to past heart rate analysis results. It can also improve the current analysis accuracy by referring to past body temperature analysis results. It can also improve the current analysis accuracy by referring to past breathing pattern analysis results. In this way, the analysis accuracy can be improved by referring to past analysis results.

[0064] The notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit analyzes the content and timing of past notifications. The notification unit selects the optimal notification method based on the user's past notification history. For example, the notification unit prioritizes the selection of notification methods that the user has preferred in the past. It can also exclude notification methods that the user has avoided in the past. Furthermore, the notification unit can analyze the user's past notification history and suggest the optimal notification method. In this way, the optimal notification method for the user can be selected by referring to the past notification history.

[0065] The collection unit can analyze the user's social media activity and collect related biometric data. For example, the collection unit analyzes the user's posted content, the number of likes, the number of followers, etc. The collection unit collects biometric data based on the user's social media activity. For example, if the user posts on social media that they are feeling stressed, the collection unit can collect heart rate and breathing pattern data. Also, if the user posts that they are relaxed, the collection unit can collect body temperature and heart rate data. Furthermore, if the user posts that they are feeling tense, the collection unit can collect breathing pattern and body temperature data. In this way, related data can be collected by analyzing social media activity.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects biometric data. Biometric data includes heart rate, body temperature, breathing patterns, etc. The collection unit collects this data in real time using a wearable device or smartphone. It can also collect video and audio data using a camera. For example, the collection unit obtains heart rate data from a smartwatch worn by the user, body temperature data using a smartphone sensor, and video and audio data using a camera. Step 2: The analysis unit analyzes the biometric data collected by the collection unit. The analysis unit uses AI to analyze patterns of heart rate and body temperature fluctuations and recognize differences from normal. For example, it inputs heart rate data to detect signs of stress or tension, analyzes body temperature data to detect abnormal increases, and analyzes breathing pattern data to detect abnormal fluctuations. Step 3: The detection unit detects signs of harassment based on the data analyzed by the analysis unit. The detection unit uses audio analysis to detect offensive words and tones, and video analysis to detect intimidating attitudes and behaviors. For example, AI can be used to analyze audio recordings to detect offensive words, and video recordings to detect intimidating attitudes. Step 4: The notification unit issues a warning based on the results detected by the detection unit. The notification unit displays a warning to the victim saying "Possible harassment" and notifies the perpetrator that "Harassment has been detected." For example, the warning can be displayed using the smartphone's notification function, and the warning can be sent via email or SMS.

[0068] (Example 2) A harassment detection system according to an embodiment of the present invention utilizes the functions of a wearable device, smartphone, or camera (video and audio recording) to objectively determine whether a person is experiencing harassment. The harassment detection system collects biometric data such as heart rate, body temperature, and breathing patterns, and uses AI to analyze the data and identify differences from normal behavior. Furthermore, AI analyzes the camera's video and audio data to detect signs of harassment. This allows for the determination of possible harassment, even when the individual is unaware of it. Finally, it sends a warning alert to the victim and the harasser, leading to the prevention of serious harassment and harm. For example, a harassment detection system uses a wearable device or smartphone worn by the user on a daily basis to collect biometric data such as heart rate, body temperature, and breathing patterns in real time. Next, AI analyzes the collected data and detects signs of stress and tension by analyzing fluctuations in heart rate and body temperature. Furthermore, AI analyzes the camera's video and audio data to detect offensive words and tones through audio analysis, and intimidating attitudes and behaviors through video analysis. This allows for a clear understanding of the specific circumstances of harassment. Finally, if AI detects signs of harassment, it will send a real-time alert to the victim and perpetrator. This can lead to the prevention of serious harassment and perpetrators. As a result, the harassment detection system enables early detection and prevention of harassment, protecting the mental and physical health of victims. In addition, by issuing a warning to perpetrators, it is expected to have a deterrent effect on harassment. For example, it can prevent harassment from occurring in environments such as the workplace or school.

[0069] A harassment detection system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects biometric data. Examples of biometric data include, but are not limited to, heart rate, body temperature, and breathing patterns. The collection unit collects data such as heart rate, body temperature, and breathing patterns in real time using, for example, a wearable device or a smartphone. The collection unit can also collect video and audio data using a camera. For example, the collection unit acquires heart rate data from a smartwatch worn by a user. The collection unit can also acquire body temperature data using a smartphone sensor. The collection unit can also acquire video and audio data using a camera. The analysis unit analyzes the biometric data collected by the collection unit. For example, the analysis unit can analyze patterns of heart rate and body temperature fluctuations using AI to identify differences from normal conditions. For example, the analysis unit inputs heart rate data into an AI model to detect signs of stress or tension. The analysis unit can also analyze body temperature data to detect abnormal increases. The analysis unit can also analyze breathing pattern data and detect abnormal fluctuations. The detection unit detects signs of harassment based on the data analyzed by the analysis unit. The detection unit, for example, detects offensive words or tones through audio analysis. For example, the detection unit can analyze recorded data using AI to detect offensive words. The detection unit can also detect intimidating attitudes or behaviors through video analysis. For example, the detection unit can analyze recorded data using AI to detect intimidating attitudes. The notification unit issues a warning based on the results detected by the detection unit. For example, the notification unit can display a warning to the victim, such as "Possible harassment." The notification unit can also notify the perpetrator, such as "Harassment has been detected." For example, the notification unit can display a warning using a smartphone's notification function. The notification unit can also notify the perpetrator by email or SMS. As a result, the harassment detection system according to the embodiment can objectively determine signs of harassment and issue a warning by collecting, analyzing, detecting, and notifying biometric data.

[0070] The collection unit can collect biometric data including heart rate, body temperature, and breathing patterns. The collection unit, for example, uses a wearable device to measure heart rate. For example, the collection unit collects heart rate data in real time using a smartwatch. The collection unit can also use a smartphone sensor to measure body temperature. For example, the collection unit collects body temperature data using a temperature sensor in the smartphone. The collection unit can also use a dedicated device to measure breathing patterns. For example, the collection unit collects breathing pattern data using a breathing sensor. By collecting biometric data such as heart rate, body temperature, and breathing patterns, the user's condition can be understood in detail. Some or all of the above-mentioned 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 heart rate data acquired from the wearable device into AI and have the AI ​​collect the data.

[0071] The analysis unit can analyze the collected biometric data and recognize differences from normal times, which serve as a reference. The analysis unit, for example, uses AI to analyze heart rate data and recognize differences from normal times. For example, the analysis unit inputs the heart rate data into an AI model to detect signs of stress or tension. The analysis unit can also analyze body temperature data and detect abnormal increases. For example, the analysis unit can analyze body temperature data using AI to detect abnormal increases. The analysis unit can also analyze breathing pattern data and detect abnormal fluctuations. For example, the analysis unit can analyze breathing pattern data using AI to detect abnormal fluctuations. By recognizing differences from normal times, an abnormal condition can be detected. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected biometric data into AI and have the AI ​​analyze the data.

[0072] The detection unit can detect signs of harassment through voice analysis. For example, the detection unit can analyze recorded data using AI to detect offensive words and tones. For example, the detection unit can input recorded data into an AI model to detect offensive words. The detection unit can also extract voice features to detect signs of harassment. For example, the detection unit can analyze the tone and speed of the voice to detect offensive tones. The detection unit can also detect specific words and phrases using voice recognition technology. For example, the detection unit can detect offensive words using voice recognition technology. This makes it possible to detect signs of harassment through voice analysis. Some or all of the above-mentioned processing in the detection unit can be performed using AI, for example, or without AI. For example, the detection unit can input recorded data into AI and have the AI ​​perform voice analysis.

[0073] The detection unit can detect signs of harassment through video analysis. The detection unit, for example, uses AI to analyze recorded data and detect intimidating attitudes and behaviors. For example, the detection unit inputs recorded data into an AI model to detect intimidating attitudes. The detection unit can also detect changes in facial expressions using facial recognition technology. For example, the detection unit can detect intimidating facial expressions using facial recognition technology. The detection unit can also detect intimidating behavior using motion analysis technology. For example, the detection unit can detect intimidating behavior using motion analysis technology. This makes it possible to detect signs of harassment through video analysis. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input recorded data into AI and have the AI ​​perform video analysis.

[0074] The notification unit can issue a warning to the victim. For example, the notification unit displays a warning to the victim saying, "Possible harassment." For example, the notification unit displays the warning using a smartphone's notification function. The notification unit can also issue the warning using email or SMS. For example, the notification unit issues the warning to the victim using email. The notification unit can also issue the warning using a voice alert. For example, the notification unit issues the warning to the victim using a voice alert. In this way, by notifying the victim of the warning, measures can be taken early. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can generate the content of the warning using AI and notify the victim.

[0075] The notification unit can issue a warning to the perpetrator. For example, the notification unit notifies the perpetrator that "harassment has been detected." For example, the notification unit displays the warning using a smartphone's notification function. The notification unit can also issue the warning using email or SMS. For example, the notification unit issues the warning to the perpetrator using email. The notification unit can also issue the warning using a voice alert. For example, the notification unit issues the warning to the perpetrator using a voice alert. In this way, by notifying the perpetrator of the warning, harassment can be deterred. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can generate the content of the warning using AI and notify the perpetrator.

[0076] The collection unit can estimate the user's emotions and adjust the timing of collecting biometric data based on the estimated user's emotions. The collection unit, for example, uses AI to estimate the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression analysis technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can estimate the user's emotions by analyzing the tone and speed of the voice. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit can estimate the user's emotions by analyzing the user's text input. Based on the estimated user's emotions, the collection unit adjusts the timing of collecting biometric data. For example, if the user is feeling stressed, the collection unit can frequently collect heart rate and breathing pattern data. Also, if the user is relaxed, the collection unit can collect body temperature and heart rate data at regular intervals. Furthermore, if the user is tense, the collection unit can collect breathing pattern data in real time. This allows more appropriate data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit may input the user's emotion data into the AI ​​and have the AI ​​adjust the collection timing.

[0077] The collection unit can analyze the user's past biometric data and select an appropriate collection method. The collection unit can analyze the user's past biometric data using, for example, AI. For example, the collection unit can analyze past heart rate data and increase the collection frequency if an abnormal fluctuation is detected. The collection unit can also shorten the collection interval if an abnormal increase is detected based on past body temperature data. Furthermore, the collection unit can analyze past breathing patterns and collect data in real time if an abnormal pattern is detected. This allows the optimal collection method to be selected by analyzing past data. Some or all of the above-mentioned 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 input past biometric data into AI and have the AI ​​select the collection method.

[0078] When collecting biometric data, the collection unit can filter the biometric data based on the user's current activity status and environment. The collection unit, for example, uses AI to analyze the user's current activity status and environment. For example, the collection unit analyzes the user's activity status using a pedometer or GPS data. The collection unit can also collect environmental data using a temperature sensor or humidity sensor. For example, the collection unit measures environmental temperature using a temperature sensor. The collection unit filters the biometric data based on the user's activity status and environmental data. For example, when the user is exercising, the collection unit filters heart rate data to remove abnormal values. When the user is taking a break, the collection unit can filter body temperature data to use normal values ​​as a reference. Furthermore, when the user is in a meeting, the collection unit can filter breathing pattern data to detect tension. This allows for more accurate data collection by filtering data based on the activity status and environment. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input activity status and environmental data into AI and have the AI ​​perform data filtering.

[0079] When collecting biometric data, the collection unit can select an appropriate collection means depending on the user's input method. The collection unit, for example, uses AI to analyze the user's input method. For example, the collection unit analyzes methods such as voice input, text input, and image input. The collection unit selects an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit analyzes the voice data to evaluate the stress level. Also, if the user uses text input, the collection unit can analyze the input speed and content to evaluate the tension level. Furthermore, if the user uses image input, the collection unit can perform facial expression analysis to evaluate the emotional state. This enables efficient data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's input data into AI and have the AI ​​select the collection means.

[0080] The collection unit can estimate the user's emotions and determine the priority of biometric data to be collected based on the estimated user's emotions. The collection unit, for example, uses AI to estimate the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression analysis technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can estimate the user's emotions by analyzing the tone and speed of the voice. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit can estimate the user's emotions by analyzing the user's text input. Based on the estimated user's emotions, the collection unit determines the priority of the biometric data to be collected. For example, if the user is feeling stressed, the collection unit can prioritize collecting heart rate and breathing pattern data. Also, if the user is relaxed, the collection unit can prioritize collecting body temperature and heart rate data. Furthermore, if the user is tense, the collection unit can prioritize collecting breathing pattern and body temperature data. In this way, by prioritizing data based on emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit may input the user's emotion data into an AI and have the AI ​​determine the priority of the data.

[0081] When collecting biometric data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, uses AI to analyze the user's geographical location information. For example, the collection unit acquires the user's location information using GPS data or an IP address. The collection unit collects biometric data based on the user's geographical location information. For example, when the user is at work, the collection unit prioritizes collecting heart rate and breathing pattern data. Also, when the user is at home, the collection unit can prioritize collecting body temperature and heart rate data. Furthermore, when the user is in a public place, the collection unit can prioritize collecting breathing pattern and body temperature data. In this way, by taking the geographical location information into account, highly relevant data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's location information into AI and have the AI ​​collect data.

[0082] The collection unit can analyze the user's social media activity and collect related data when collecting biometric data. The collection unit, for example, uses AI to analyze the user's social media activity. For example, the collection unit analyzes the user's post content, the number of likes, the number of followers, etc. The collection unit collects biometric data based on the user's social media activity. For example, if the user posts on social media that they are feeling stressed, the collection unit can collect heart rate and breathing pattern data. Also, if the user posts that they are relaxed, the collection unit can collect body temperature and heart rate data. Furthermore, if the user posts that they are feeling nervous, the collection unit can collect breathing pattern and body temperature data. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect the data.

[0083] When collecting biometric data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, uses AI to analyze the user's past feedback. For example, the collection unit analyzes questionnaire surveys and user reviews. The collection unit customizes the biometric data collection method based on the user's past feedback. For example, if the user previously requested collection of heart rate data, the collection unit can prioritize collecting heart rate data. Also, if the user previously requested collection of body temperature data, the collection unit can also prioritize collecting body temperature data. Furthermore, if the user previously requested collection of breathing pattern data, the collection unit can also prioritize collecting breathing pattern data. In this way, the collection method can be customized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into AI and have the AI ​​customize the collection method.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user's emotions. The analysis unit, for example, uses AI to estimate the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression analysis technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can estimate the user's emotions by analyzing the tone and speed of the voice. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit can estimate the user's emotions by analyzing the user's text input. The analysis unit adjusts the presentation of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can display the analysis results in a simple and easy-to-understand format. If the user is relaxed, the analysis unit can display detailed analysis results. Furthermore, if the user is tense, the analysis unit can display analysis results that focus on the main points. By adjusting the presentation of the analysis based on emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the AI ​​and have the AI ​​adjust the method of expression of the analysis.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the biometric data. The analysis unit, for example, uses AI to evaluate the importance of the biometric data. For example, the analysis unit evaluates the degree of abnormality or urgency of heart rate data. The analysis unit adjusts the level of detail of the analysis based on the importance. For example, if heart rate data is important, the analysis unit performs a detailed analysis. Also, if body temperature data is important, the analysis unit can also perform a detailed analysis. Furthermore, if breathing pattern data is important, the analysis unit can also perform a detailed analysis. In this way, adjusting the level of detail of the analysis based on the importance enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input biometric data to AI and have the AI ​​adjust the level of detail of the analysis.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the category of biometric data. The analysis unit, for example, uses AI to classify the categories of biometric data. For example, the analysis unit classifies categories such as heart rate data, body temperature data, and breathing pattern data. The analysis unit applies different analysis algorithms depending on the category. For example, a heart rate variability analysis algorithm can be applied to heart rate data. A body temperature variability analysis algorithm can also be applied to body temperature data. Furthermore, a respiratory variability analysis algorithm can also be applied to breathing pattern data. In this way, highly accurate analysis is possible by applying an analysis algorithm depending on the category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the biometric data into AI and have the AI ​​apply the analysis algorithm.

[0087] 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, uses AI to analyze the user's past analysis results. For example, the analysis unit can improve the current analysis accuracy by referring to past heart rate analysis results. The current analysis accuracy can also be improved by referring to past body temperature analysis results. Furthermore, the current analysis accuracy can also be improved by referring to past breathing pattern analysis results. In this way, by referring to past analysis results, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, uses AI to estimate the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression analysis technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can estimate the user's emotions by analyzing the tone and speed of the voice. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit can estimate the user's emotions by analyzing the user's text input. The analysis unit adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can display a short, concise analysis result. If the user is relaxed, the analysis unit can display a detailed analysis result. Furthermore, if the user is tense, the analysis unit can display a concise analysis result. By adjusting the length of the analysis based on the user's emotions, it is possible to provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, an AI. For example, the analysis unit may input user emotion data into the AI ​​and have the AI ​​adjust the length of the analysis.

[0089] During analysis, the analysis unit can determine the priority of analysis based on the time when the biometric data was collected. The analysis unit, for example, uses AI to analyze the time when the biometric data was collected. For example, the analysis unit analyzes the timestamp and collection frequency of the data. The analysis unit determines the priority of analysis based on the time of collection. For example, the analysis unit prioritizes analysis of recently collected heart rate data. It can also prioritize analysis of recently collected body temperature data. It can also prioritize analysis of recently collected breathing pattern data. In this way, by determining the priority of analysis based on the time of collection, it is possible to prioritize analysis of the most recent data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the biometric data into AI and have the AI ​​determine the priority of analysis.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the biometric data. The analysis unit, for example, uses AI to evaluate the relevance of the biometric data. For example, the analysis unit calculates a correlation coefficient between heart rate data and breathing pattern data. The analysis unit adjusts the order of analysis based on the relevance. For example, if heart rate data and breathing pattern data are correlated, the analysis unit analyzes them simultaneously. Also, if body temperature data and heart rate data are correlated, the analysis unit can analyze them simultaneously. Furthermore, if breathing pattern data and body temperature data are correlated, the analysis unit can analyze them simultaneously. This enables efficient analysis by adjusting the order of analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input biometric data to AI and have the AI ​​adjust the order of analysis.

[0091] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses AI to evaluate the user's level of expertise. For example, the analysis unit analyzes the user's occupation and past learning history. The analysis unit adjusts the use of technical terms in the analysis according to the level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can select appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's technical expertise data into AI and have the AI ​​execute the use of technical terms.

[0092] The detection unit can estimate the user's emotion and adjust the detection criteria based on the estimated user's emotion. The detection unit can estimate the user's emotion using, for example, AI. For example, the detection unit can estimate the user's emotion using facial expression analysis technology. The detection unit can also estimate the user's emotion using voice analysis technology. For example, the detection unit can estimate the user's emotion by analyzing the tone and speed of the voice. The detection unit can also estimate the user's emotion using text analysis technology. For example, the detection unit can estimate the user's emotion by analyzing the user's text input. The detection unit adjusts the detection criteria based on the estimated user's emotion. For example, if the user is stressed, the detection unit can closely detect fluctuations in heart rate. If the user is relaxed, the detection unit can also gently detect fluctuations in body temperature. Furthermore, if the user is tense, the detection unit can closely detect fluctuations in breathing patterns. This allows for more accurate detection by adjusting the detection criteria based on the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, or may be performed without using AI. For example, the detection unit may input user emotion data into the AI ​​and cause the AI ​​to adjust the detection criteria.

[0093] The detection unit can improve detection accuracy by taking into account the interrelationships of biometric data during detection. The detection unit, for example, uses AI to evaluate the interrelationships of biometric data. For example, the detection unit performs correlation analysis between heart rate data and breathing pattern data. The detection unit improves detection accuracy by taking into account the interrelationships of biometric data. For example, heart rate and breathing pattern data can be combined to improve detection accuracy. Body temperature and heart rate data can also be combined to improve detection accuracy. Furthermore, breathing pattern and body temperature data can be combined to improve detection accuracy. In this way, by taking the interrelationships into account, detection accuracy can be improved. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input biometric data into AI and have the AI ​​evaluate the interrelationships.

[0094] The detection unit can perform detection while taking into account attribute information of the biometric data submitter. The detection unit, for example, uses AI to analyze the attribute information of the biometric data submitter. For example, the detection unit analyzes attribute information such as age, gender, and occupation. The detection unit performs detection while taking into account the attribute information of the submitter. For example, if the submitter is young, the detection unit strictly detects heart rate fluctuations. Also, if the submitter is elderly, the detection unit can also detect body temperature fluctuations more gradually. Furthermore, if the submitter has a specific health condition, detection can be performed according to that condition. This enables individually appropriate detection by taking attribute information into account. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the submitter's attribute information into AI and have the AI ​​adjust the detection.

[0095] The detection unit can weight the detection based on the frequency of submission of the biometric data at the time of detection. The detection unit, for example, uses AI to analyze the frequency of submission of the biometric data. For example, the detection unit analyzes the number of times data is submitted and the submission interval. The detection unit weights the detection based on the frequency of submission. For example, a high weight is assigned to heart rate data that is frequently submitted. A high weight can also be assigned to body temperature data that is frequently submitted. Furthermore, a high weight can be assigned to breathing pattern data that is frequently submitted. In this way, by weighting based on the frequency of submission, the importance of frequently submitted data can be increased. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the frequency of submission of the biometric data to AI and have the AI ​​adjust the weighting.

[0096] The detection unit can estimate the user's emotion and adjust the display order of the detection results based on the estimated user's emotion. The detection unit, for example, uses AI to estimate the user's emotion. For example, the detection unit can estimate the user's emotion using facial expression analysis technology. The detection unit can also estimate the user's emotion using voice analysis technology. For example, the detection unit can estimate the user's emotion by analyzing the tone and speed of the voice. The detection unit can also estimate the user's emotion using text analysis technology. For example, the detection unit can estimate the user's emotion by analyzing the user's text input. Based on the estimated user's emotion, the detection unit adjusts the display order of the detection results. For example, if the user is stressed, the detection unit can display important detection results first. Alternatively, if the user is relaxed, the detection unit can sequentially display detailed detection results. Furthermore, if the user is nervous, the detection unit can display key detection results first. In this way, by adjusting the display order based on emotion, important information can be provided to the user preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, or may be performed without using AI. For example, the detection unit may input user emotion data into the AI ​​and cause the AI ​​to adjust the display order.

[0097] The detection unit can perform detection taking into account the geographical distribution of the biometric data. The detection unit, for example, uses AI to analyze the geographical distribution of the biometric data. For example, the detection unit performs data distribution by region and geographic cluster analysis. The detection unit performs detection taking into account the geographical distribution. For example, if the user is in a specific region, the detection unit prioritizes detecting data from that region. Also, if the user is moving, the detection unit can detect data from the user's destination in real time. Furthermore, if the user is in multiple regions, the detection unit can integrate and detect data from each region. This allows detection to be performed according to the characteristics of each region by taking the geographical distribution into account. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input the geographical distribution of the biometric data into AI and have the AI ​​adjust the detection.

[0098] During detection, the detection unit can improve the accuracy of detection by referring to literature related to the biometric data. The detection unit, for example, uses AI to refer to literature related to the biometric data. For example, the detection unit refers to medical literature related to heart rate data. The detection unit improves the accuracy of detection by referring to the related literature. For example, the detection unit improves the detection accuracy by referring to medical literature related to heart rate data. The detection accuracy can also be improved by referring to medical literature related to body temperature data. Furthermore, the detection accuracy can be improved by referring to medical literature related to breathing pattern data. In this way, the detection accuracy can be improved by referring to the related literature. Some or all of the above-mentioned processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit inputs the biometric data into AI and causes the AI ​​to refer to the related literature.

[0099] The detection unit can perform detection taking into consideration the market value of the biometric data. The detection unit, for example, uses AI to evaluate the market value of the biometric data. For example, the detection unit evaluates the commercial value and demand forecast of the data. The detection unit performs detection taking into consideration market value. For example, it may preferentially detect heart rate data, which has a high market value. It may also preferentially detect body temperature data, which has a high market value. It may also preferentially detect breathing pattern data, which has a high market value. In this way, important data can be preferentially detected by taking market value into consideration. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit may input the market value of the biometric data into AI and have the AI ​​adjust the detection.

[0100] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. The notification unit can estimate the user's emotion using, for example, AI. For example, the notification unit can estimate the user's emotion using facial expression analysis technology. The notification unit can also estimate the user's emotion using voice analysis technology. For example, the notification unit can estimate the user's emotion by analyzing the tone and speed of the voice. The notification unit can also estimate the user's emotion using text analysis technology. For example, the notification unit can estimate the user's emotion by analyzing the user's text input. The notification unit adjusts the notification method based on the estimated user's emotion. For example, if the user is feeling stressed, the notification unit can select a simple and easy-to-understand notification method. If the user is relaxed, the notification unit can select a detailed notification method. Furthermore, if the user is nervous, the notification unit can select a notification method that focuses on the main points. This allows the notification method to be adjusted based on the emotion, enabling appropriate notifications for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, or without, an AI. For example, the notification unit may input user emotion data into the AI ​​and have the AI ​​adjust the notification method.

[0101] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. The notification unit, for example, uses AI to analyze the user's past notification history. For example, the notification unit analyzes the content and timing of past notifications. The notification unit selects the optimal notification method based on the user's past notification history. For example, the notification unit prioritizes the selection of notification methods that the user has previously preferred. The notification unit can also exclude notification methods that the user has previously avoided. Furthermore, the notification unit can analyze the user's past notification history and suggest the optimal notification method. In this way, the optimal notification method for the user can be selected by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's notification history data into AI and have the AI ​​select the notification method.

[0102] The notification unit can customize the notification content according to the user's current task when notifying the user. The notification unit, for example, uses AI to analyze the user's current task. For example, the notification unit analyzes the user's schedule and activities. The notification unit customizes the notification content according to the user's current task. For example, if the user is in a meeting, the notification unit selects a quiet notification method. Also, if the user is exercising, the notification unit can notify the user by vibration or sound. Furthermore, if the user is taking a break, the notification unit can display detailed notification content. This allows the notification content to be customized according to the current task, enabling appropriate notifications for the user. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's task data into AI and have the AI ​​customize the notification content.

[0103] The notification unit can select the optimal notification method by taking into account the user's device information when notifying the user. The notification unit, for example, uses AI to analyze the user's device information. For example, the notification unit analyzes the device type and OS version. The notification unit selects the optimal notification method based on the user's device information. For example, if the user is using a smartphone, the notification unit displays the notification on the screen. Also, if the user is using a smartwatch, the notification unit can notify by vibration. Furthermore, if the user is using a tablet, the notification unit can display the notification on a larger screen. In this way, the optimal notification method for the user can be selected by taking into account the device information. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's device information into AI and have the AI ​​select the notification method.

[0104] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. The notification unit can estimate the user's emotions using, for example, AI. For example, the notification unit can estimate the user's emotions using facial expression analysis technology. The notification unit can also estimate the user's emotions using voice analysis technology. For example, the notification unit can estimate the user's emotions by analyzing the tone and speed of the voice. The notification unit can also estimate the user's emotions using text analysis technology. For example, the notification unit can estimate the user's emotions by analyzing the user's text input. The notification unit determines the priority of notifications based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can prioritize displaying important notifications. Also, if the user is relaxed, the notification unit can sequentially display detailed notifications. Furthermore, if the user is nervous, the notification unit can prioritize displaying notifications that highlight the main points. In this way, by determining the priority of notifications based on emotions, important notifications can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, or without, an AI. For example, the notification unit may input user emotion data into the AI ​​and have the AI ​​determine the priority of notifications.

[0105] The notification unit can select the optimal notification method by taking into account the user's device information when notifying the user. The notification unit, for example, uses AI to analyze the user's device information. For example, the notification unit analyzes the device type and OS version. The notification unit selects the optimal notification method based on the user's device information. For example, if the user is using a smartphone, the notification unit displays the notification on the screen. Also, if the user is using a smartwatch, the notification unit can notify by vibration. Furthermore, if the user is using a tablet, the notification unit can display the notification on a larger screen. In this way, the optimal notification method for the user can be selected by taking into account the device information. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's device information into AI and have the AI ​​select the notification method.

[0106] The notification unit can provide multilingual notification content according to the user's language setting at the time of notification. The notification unit, for example, uses AI to analyze the user's language setting. For example, the notification unit analyzes the user's device settings and application settings. The notification unit provides multilingual notification content based on the user's language setting. For example, the notification unit automatically sets the notification language based on the user's device language setting. In addition, if the user uses multiple languages, the notification unit can provide a language switching function. Furthermore, if the user selects a specific language, the notification unit can provide notifications in that language. This enables multilingual notification according to the language setting, making it easy for the user to understand. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without AI. For example, the notification unit can input the user's language setting data into AI and have the AI ​​provide multilingual notification content.

[0107] The notification unit can analyze the user's social media activity and provide related information at the time of notification. The notification unit can, for example, use AI to analyze the user's social media activity. For example, the notification unit can analyze the user's posts, the number of likes, the number of followers, etc. The notification unit can provide related information based on the user's social media activity. For example, the notification unit can provide information about places the user has checked in on social media. The notification unit can also analyze the user's social media posts to provide information about related tourist spots and stores. Furthermore, the notification unit can provide information about related places and events based on the activities of the user's friends on social media. In this way, related information can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the notification unit can be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media data into AI and have the AI ​​provide related information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects biometric data such as heart rate, body temperature, and breathing patterns using sensors in the smart device 14. The collection unit can also collect video and audio data using the camera in the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected biometric data to identify differences from normal conditions. The detection unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and detects signs of harassment based on the analyzed data. The notification unit is realized, for example, by the control unit 46A in the smart device 14, and issues a warning based on the detected results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and notification 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 is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects biometric data such as heart rate, body temperature, and breathing patterns using sensors in the smart glasses 214. The collection unit can also collect video and audio data using the camera in the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected biometric data to recognize differences from normal data. The detection unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and detects signs of harassment based on the analyzed data. The notification unit is realized, for example, by the control unit 46A in the smart glasses 214, and issues a warning based on the detected results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and notification 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 is realized by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects biometric data such as heart rate, body temperature, and breathing patterns using a sensor in the headset-type terminal 314. The collection unit can also collect video and audio data using a camera in the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected biometric data to identify differences from normal conditions. The detection unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and detects signs of harassment based on the analyzed data. The notification unit is realized, for example, by the control unit 46A in the headset-type terminal 314, and issues a warning based on the detected results. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and notification 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 is realized by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects biometric data such as heart rate, body temperature, and breathing patterns using sensors in the robot 414. The collection unit can also collect video and audio data using a camera in the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected biometric data to recognize differences from normal behavior. The detection unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and detects signs of harassment based on the analyzed data. The notification unit is realized, for example, by the control unit 46A in the robot 414, and issues a warning based on the detection results.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The collection unit can analyze the user's past behavioral patterns and optimize the collection of biometric data under specific circumstances. For example, the collection unit can identify situations in which the user felt stressed in the past and focus on collecting data on heart rate and breathing patterns in those situations. The collection unit can also identify situations in which the user is relaxed and collect body temperature and heart rate data in those situations. Furthermore, the collection unit can identify situations in which the user is tense and collect breathing pattern data in real time in those situations. This allows for more accurate data collection by optimizing data collection based on past behavioral patterns.

[0110] 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 analyzing heart rate and breathing pattern data. Also, if the user is relaxed, the analysis unit can prioritize analyzing body temperature and heart rate data. Furthermore, if the user is tense, the analysis unit can prioritize analyzing breathing pattern and body temperature data. In this way, by determining the priority of analysis based on emotions, important data can be analyzed preferentially.

[0111] The detection unit can estimate the user's emotion and adjust the detection accuracy based on the estimated user's emotion. For example, if the user is feeling stressed, the detection unit can strictly detect fluctuations in heart rate. If the user is relaxed, the detection unit can also gently detect fluctuations in body temperature. Furthermore, if the user is tense, the detection unit can strictly detect fluctuations in breathing patterns. This allows for more accurate detection by adjusting the detection accuracy based on the emotion.

[0112] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can select simple and easy-to-understand notification content. If the user is relaxed, the notification unit can also select detailed notification content. Furthermore, if the user is nervous, the notification unit can also select notification content that focuses on the main points. In this way, by adjusting the notification content based on emotions, it is possible to provide notifications that are appropriate for the user.

[0113] The collection unit can adjust the method of collecting biometric data based on the user's current activity status and environment. For example, when the user is exercising, the collection unit can frequently collect heart rate data. When the user is resting, the collection unit can collect body temperature data at regular intervals. Furthermore, when the user is in a meeting, the collection unit can collect breathing pattern data in real time. This allows for more accurate data collection by adjusting the data collection method based on the user's activity status and environment.

[0114] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the current analysis accuracy by referring to past heart rate analysis results. It can also improve the current analysis accuracy by referring to past body temperature analysis results. It can also improve the current analysis accuracy by referring to past breathing pattern analysis results. In this way, the analysis accuracy can be improved by referring to past analysis results.

[0115] The detection unit can estimate the user's emotion and adjust the order in which the detection results are displayed based on the estimated user's emotion. For example, if the user is feeling stressed, the detection unit can display important detection results first. Also, if the user is relaxed, the detection unit can sequentially display detailed detection results. Furthermore, if the user is nervous, the detection unit can display detection results that highlight the main points first. In this way, by adjusting the display order based on the emotion, it is possible to provide information that is important to the user with priority.

[0116] The notification unit can select the optimal notification method by referring to the user's past notification history. For example, the notification unit analyzes the content and timing of past notifications. The notification unit selects the optimal notification method based on the user's past notification history. For example, the notification unit prioritizes the selection of notification methods that the user has preferred in the past. It can also exclude notification methods that the user has avoided in the past. Furthermore, the notification unit can analyze the user's past notification history and suggest the optimal notification method. In this way, the optimal notification method for the user can be selected by referring to the past notification history.

[0117] The collection unit can analyze the user's social media activity and collect related biometric data. For example, the collection unit analyzes the user's posted content, the number of likes, the number of followers, etc. The collection unit collects biometric data based on the user's social media activity. For example, if the user posts on social media that they are feeling stressed, the collection unit can collect heart rate and breathing pattern data. Also, if the user posts that they are relaxed, the collection unit can collect body temperature and heart rate data. Furthermore, if the user posts that they are feeling tense, the collection unit can collect breathing pattern and body temperature data. In this way, related data can be collected by analyzing social media activity.

[0118] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can display a short, concise analysis result. If the user is relaxed, the analysis unit can also display a detailed analysis result. Furthermore, if the user is tense, the analysis unit can also display a concise analysis result. In this way, by adjusting the length of the analysis based on emotions, it is possible to provide an analysis result of an appropriate length for the user.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects biometric data. Biometric data includes heart rate, body temperature, breathing patterns, etc. The collection unit collects this data in real time using a wearable device or smartphone. It can also collect video and audio data using a camera. For example, the collection unit obtains heart rate data from a smartwatch worn by the user, body temperature data using a smartphone sensor, and video and audio data using a camera. Step 2: The analysis unit analyzes the biometric data collected by the collection unit. The analysis unit uses AI to analyze patterns of heart rate and body temperature fluctuations and recognize differences from normal. For example, it inputs heart rate data to detect signs of stress or tension, analyzes body temperature data to detect abnormal increases, and analyzes breathing pattern data to detect abnormal fluctuations. Step 3: The detection unit detects signs of harassment based on the data analyzed by the analysis unit. The detection unit uses audio analysis to detect offensive words and tones, and video analysis to detect intimidating attitudes and behaviors. For example, AI can be used to analyze audio recordings to detect offensive words, and video recordings to detect intimidating attitudes. Step 4: The notification unit issues a warning based on the results detected by the detection unit. The notification unit displays a warning to the victim saying "Possible harassment" and notifies the perpetrator that "Harassment has been detected." For example, the warning can be displayed using the smartphone's notification function, and the warning can be sent via email or SMS.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 biometric data; an analysis unit that analyzes the biological data collected by the collection unit; a detection unit that detects signs of harassment based on the data analyzed by the analysis unit; a notification unit that notifies a warning based on the result detected by the detection unit. A system characterized by:

2. The collecting unit Collect biometric data, including heart rate, temperature, and breathing patterns 2. The system of claim 1.

3. The analysis unit Analyze collected biometric data and recognize any differences from the baseline normal state 2. The system of claim 1.

4. The detection unit Detecting signs of harassment through audio analysis 2. The system of claim 1.

5. The detection unit Detecting signs of harassment through video analytics 2. The system of claim 1.

6. The notification unit Notify the victim of the warning 2. The system of claim 1.

7. The notification unit Issue a warning to the perpetrator 2. The system of claim 1.

8. The collecting unit The system estimates the user's emotions and adjusts the timing of biometric data collection based on the estimated user emotions.

2. The system of claim 1.

Images