system

Abstract

The system according to this embodiment aims to detect abnormalities while ensuring privacy in monitoring elderly individuals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects sound. The analysis unit analyzes the sound collected by the collection unit. The detection unit detects abnormalities from the sound analyzed by the analysis unit. The notification unit notifies the abnormalities detected by the detection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to detect abnormalities while ensuring privacy in monitoring the elderly.

[0005] The system according to the embodiment aims to detect abnormalities while ensuring privacy in monitoring the elderly.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects voices. The analysis unit analyzes the voices collected by the collection unit. The detection unit detects abnormalities from the voices analyzed by the analysis unit. The notification unit notifies the abnormalities detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect abnormalities while ensuring privacy when monitoring elderly people. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The monitoring system according to an embodiment of the present invention is a voice-based monitoring system for children and landlords who face challenges in monitoring elderly people. This monitoring system has the advantage of reliably monitoring while also ensuring the privacy of the person being monitored. For example, the monitoring system involves installing a monitoring smart speaker in the parent's residence. This smart speaker listens to all sounds (voices and noises) 24 hours a day, 365 days a year, and an AI analyzes the content. For example, it can detect abnormalities such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. If an abnormality is detected, the monitoring system notifies the child, and if the child cannot be contacted, it notifies an operator. Next, the monitoring system's AI checks on the parent's health every day. For example, it might ask, "Hello. How are you feeling today?" and the parent might reply, "I'm feeling fine today. I'm in a good mood too." If there is no response or the parent is unwell, the monitoring system notifies the child, and if the child cannot be contacted, it notifies an operator. Furthermore, the monitoring smart speaker detects abnormalities using voice, thus protecting the privacy of the person being monitored. Conventional monitoring systems use video to confirm abnormalities, raising concerns about the privacy of the person being monitored, but this invention eliminates such concerns. This monitoring smart speaker is intended for use by children of elderly parents, real estate companies managing rental properties where elderly people live, and home care workers. Because the AI ​​manages safety and health on a daily basis, it has the advantage of being resource-efficient and enabling safety checks. In addition, the monitoring smart speaker's AI learns to listen only to specific sounds and voices related to health and safety management, so it does not infringe on privacy. This makes it possible to check safety over a wide area while ensuring the privacy of the person being monitored. As a result, the monitoring system can reliably monitor the elderly while ensuring the privacy of the person being monitored.

[0029] The monitoring system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects sound. The collection unit collects everyday sounds 24 hours a day, 365 days a year, for example, using a smart speaker installed in the parents' residence. The collection unit can collect sound for detecting abnormalities such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. The collection unit can learn to listen only to specific sounds or voices related to health and safety management, for example, using AI. The analysis unit analyzes the sound collected by the collection unit. The analysis unit analyzes the collected sound and detects abnormalities, for example, using AI. The analysis unit can analyze sound for detecting abnormalities such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. The detection unit detects abnormalities from the sound analyzed by the analysis unit. The detection unit detects abnormalities from the analyzed sound, for example, using AI. The detection unit can detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. The notification unit notifies the system of the anomalies detected by the detection unit. The notification unit uses AI, for example, to notify the detected anomalies. If an anomaly is detected, the notification unit can notify the child, and if the child cannot be contacted, it can notify an operator. As a result, the monitoring system according to this embodiment can reliably monitor and ensure privacy in the monitoring of the elderly by collecting and analyzing audio, detecting anomalies, and making notifications.

[0030] The data collection unit collects audio. For example, the data collection unit uses a smart speaker installed in the parents' residence to collect ambient sounds 24 hours a day, 365 days a year. Specifically, the smart speaker is equipped with a high-sensitivity microphone that can clearly collect audio from the entire room. This allows the unit to collect everyday sounds, conversations, and ambient sounds in real time and transmit them to a central database. The data collection unit can also collect audio to detect anomalies, such as shouts, cries for help, the presence of intruders, falls, drops, prolonged silence, or absence. This ensures that the data collection unit reliably collects not only everyday sounds but also audio indicating emergencies and abnormal situations. The data collection unit can use AI to learn to listen only to specific sounds and voices related to health and safety management. The AI ​​uses machine learning algorithms to analyze the collected audio data and learn specific patterns and features. This allows the data collection unit to filter out irrelevant audio and collect only important audio. For example, the AI ​​can identify screams, cries for help, footsteps of intruders, and sounds of falls or drops, and is configured to prioritize the collection of these sounds. This allows the collection unit to collect sounds efficiently and effectively, improving the overall system performance. Furthermore, the collection unit can centrally manage the collected audio data and collaborate with other systems and departments as needed. For instance, the collected audio data can be stored on a cloud server, making it accessible to the analysis and detection units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect sounds efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the audio collected by the collection department. For example, the analysis department uses AI to analyze the collected audio and detect anomalies. Specifically, the AI ​​uses speech recognition technology to analyze the collected audio data and identify abnormal audio patterns. For example, it can identify shouts, cries for help, footsteps of intruders, and sounds of falls or drops, and prioritizes the analysis of these sounds. The AI ​​can analyze the audio data along a timeline to identify the timing and frequency of abnormal audio patterns. This allows the analysis department to quickly and accurately analyze the collected audio data and detect anomalies. Furthermore, the analysis department can utilize historical audio data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past abnormal audio data, it can predict risk fluctuations in specific time periods or situations and formulate future countermeasures. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal audio, issuing early warnings. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system. Furthermore, the analysis unit can continuously learn from the collected audio data and improve its analysis accuracy. For example, if a new abnormal audio pattern occurs, the AI ​​learns that pattern and incorporates it into subsequent analyses. This allows the analysis unit to always provide highly accurate analyses based on the latest information, supporting quick and appropriate responses.

[0032] The detection unit detects anomalies from the audio analyzed by the analysis unit. The detection unit uses, for example, AI to detect anomalies from the analyzed audio. Specifically, the AI ​​uses an anomaly detection algorithm to analyze the audio data and identify abnormal audio patterns. For example, it can identify shouts, cries for help, footsteps of intruders, and sounds of falls or drops, and prioritize the detection of these sounds. The AI ​​can analyze the audio data along a timeline to identify the timing and frequency of abnormal audio patterns. This allows the detection unit to quickly and accurately analyze the collected audio data and detect anomalies. Furthermore, the detection unit can utilize past audio data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past abnormal audio data, it can predict risk fluctuations in specific time periods or situations and formulate future countermeasures. The detection unit can also use the anomaly detection algorithm to detect unusual patterns and abnormal audio, issuing warnings early. This allows the detection unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system. Furthermore, the detection unit can continuously learn from the collected audio data and improve its analysis accuracy. For example, if a new abnormal audio pattern occurs, the AI ​​learns that pattern and incorporates it into subsequent analyses. This allows the detection unit to always provide highly accurate analysis based on the latest information, supporting quick and appropriate responses.

[0033] The reporting unit reports anomalies detected by the detection unit. The reporting unit uses AI, for example, to report detected anomalies. Specifically, when an anomaly is detected, the reporting unit automatically notifies pre-configured contacts. For example, if an anomaly is detected, it can notify the child, and if the child cannot be reached, it can notify an operator. The reporting unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the reporting unit to quickly and reliably report anomalies and support appropriate responses. Furthermore, the reporting unit can continuously improve the accuracy and effectiveness of its reports. For example, it can review and improve the content of reports based on feedback after reporting. In addition, the reporting unit can flexibly change the recipients and methods of reporting depending on the type and urgency of the anomaly. For example, if a highly urgent anomaly is detected, it can simultaneously notify multiple contacts to encourage a rapid response. This allows the reporting unit to quickly and reliably notify users of anomalies and minimize the risk of disaster. Furthermore, the reporting unit can continuously improve the accuracy and effectiveness of its reports. For example, based on feedback received after a report is submitted, the content of the report can be reviewed and improved. Furthermore, the reporting department can flexibly change the recipients and methods of reporting depending on the type and urgency of the anomaly. For instance, if a highly urgent anomaly is detected, multiple contacts can be notified simultaneously to encourage a rapid response. This allows the reporting department to quickly and reliably notify users of anomalies, minimizing the risk of damage.

[0034] The verification unit checks the child's health. The verification unit, for example, uses AI to check on the parent's health every day. The verification unit asks questions such as, "Hello. How are you feeling today?" and the parent replies, "I'm feeling fine today. I'm in a good mood too." If there is no response or the child is unwell, the verification unit can notify the child, and if the child cannot be contacted, it can notify an operator. This allows for monitoring the child's health, understanding their health status, and detecting abnormalities early. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input questions for checking the parent's health into a generating AI, which can then generate appropriate questions.

[0035] The protection unit protects privacy. The protection unit protects the privacy of the person being monitored by using AI, for example, to detect abnormalities through voice. The protection unit's AI learns to listen only to content related to health and safety management, such as specific sounds or voices. This enhances the sense of security of the person being monitored by protecting their privacy. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input anonymization and access restriction of voice data into a generating AI, which can then generate an appropriate privacy protection method.

[0036] The operator notification unit notifies the operator. The operator notification unit, for example, uses AI to notify the child if an abnormality is detected, and if the child cannot be contacted, it notifies the operator. This allows for a quick response in emergencies by notifying the operator. Some or all of the above processing in the operator notification unit may be performed using AI, for example, or without AI. For example, the operator notification unit can input the notification content when an abnormality is detected into a generating AI, and the generating AI can generate appropriate notification content.

[0037] The collection unit can collect ambient sounds 24 hours a day, 365 days a year. For example, the collection unit can use a smart speaker installed in the parents' residence to collect ambient sounds 24 hours a day, 365 days a year. The collection unit can collect sounds to detect anomalies, such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. This allows for constant monitoring by collecting ambient sounds 24 hours a day, 365 days a year. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input settings for collecting ambient sounds 24 hours a day, 365 days a year into a generating AI, which can then generate appropriate collection settings.

[0038] The analysis unit can analyze the collected audio and detect anomalies. The analysis unit can, for example, use AI to analyze the collected audio and detect anomalies. The analysis unit can analyze audio to detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. By analyzing the collected audio and detecting anomalies, a rapid response becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected audio into a generating AI, which can then perform analysis to detect anomalies.

[0039] The data collection unit can customize the types of audio collected based on the user's lifestyle patterns. For example, the data collection unit can use AI to customize the types of audio collected based on the user's lifestyle patterns. For example, if the user is active at night, the data collection unit will focus on collecting nighttime audio. For example, if the user is active during the day, the data collection unit will focus on collecting daytime audio. For example, if the user goes out on weekends, the data collection unit will focus on collecting weekend audio. By customizing the types of audio based on the user's lifestyle patterns, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user lifestyle pattern data into a generating AI and customize the types of audio that the generating AI collects.

[0040] The collection unit can limit the range of sound to be collected to a specific area within the user's living space. For example, the collection unit can use AI to limit the range of sound to be collected to a specific area within the user's living space. For example, if the user is in the living room, the collection unit will only collect sound from the living room. For example, if the user is in the bedroom, the collection unit will only collect sound from the bedroom. For example, if the user is in the kitchen, the collection unit will only collect sound from the kitchen. By limiting the range of sound collection to a specific area, data can be collected while protecting privacy. Some or all of the above 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 living space data into a generating AI, and the generating AI can limit the range of sound to be collected to a specific area.

[0041] The collection unit can filter the audio to be collected by referring to the user's past audio history. The collection unit can, for example, use AI to filter the audio to be collected by referring to the user's past audio history. The collection unit can, for example, prioritize the collection of important audio based on audio data previously collected by the user. The collection unit can, for example, filter out unnecessary audio based on audio data previously collected by the user. The collection unit can, for example, emphasize and collect specific audio based on audio data previously collected by the user. In this way, by filtering by referring to past audio history, important audio can be collected preferentially. Some or all of the above 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 past audio history data into a generating AI, and the generating AI can filter the audio to be collected.

[0042] The collection unit can select the optimal collection method for the audio to be collected based on the user's device information. For example, the collection unit may use AI to select the optimal collection method for the audio to be collected based on the user's device information. For example, if the user is using a smartphone, the collection unit will use the smartphone's microphone to collect the audio. For example, if the user is using a smart speaker, the collection unit will use the smart speaker's microphone to collect the audio. For example, if the user is using a wearable device, the collection unit will use the wearable device's microphone to collect the audio. This allows for efficient audio collection by selecting the optimal collection method based on device information. Some or all of the above 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 device information into a generating AI, which can then select the optimal collection method.

[0043] The analysis unit can classify and analyze the audio data to be analyzed according to different time periods. The analysis unit can, for example, use AI to classify and analyze the audio data according to different time periods. The analysis unit can, for example, analyze daytime audio data and nighttime audio data separately and detect anomalies. The analysis unit can, for example, analyze weekday audio data and weekend audio data separately and detect anomalies. The analysis unit can, for example, classify and analyze audio data for specific time periods (e.g., morning, noon, night) and detect anomalies. By classifying and analyzing audio data according to different time periods, the accuracy of anomaly detection is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data from different time periods into a generating AI, and the generating AI can classify and analyze the audio data.

[0044] The analysis unit can apply different algorithms to the audio data being analyzed based on the user's lifestyle patterns. For example, the analysis unit can use AI to apply different algorithms to the audio data being analyzed based on the user's lifestyle patterns. For example, if the user is active at night, the analysis unit will apply a night-specific algorithm for analysis. For example, if the user is active during the day, the analysis unit will apply a day-specific algorithm for analysis. For example, if the user goes out on weekends, the analysis unit will apply a weekend-specific algorithm for analysis. By applying different algorithms based on lifestyle patterns, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI, and the generating AI can apply different algorithms for analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referencing the user's past voice data into the audio data being analyzed. For example, the analysis unit uses AI to improve the accuracy of its analysis by referencing the user's past voice data into the audio data being analyzed. For example, the analysis unit improves the accuracy of detecting anomalies based on audio data previously collected by the user. For example, the analysis unit distinguishes between normal and abnormal voices based on audio data previously collected by the user. For example, the analysis unit emphasizes and analyzes specific anomalies based on audio data previously collected by the user. By improving the accuracy of analysis by referencing past voice data, the accuracy of anomaly detection is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past voice data into a generating AI, which can then improve the accuracy of its analysis.

[0046] The analysis unit can perform analysis on audio data based on the user's geographical location information. For example, the analysis unit can use AI to perform analysis on audio data based on the user's geographical location information. For example, if the user is in a specific region, the analysis unit will consider the characteristics of that region during the analysis. For example, if the user is on the move, the analysis unit will consider the characteristics of the destination region during the analysis. For example, if the user is at home, the analysis unit will consider the home environment during the analysis. This allows for more appropriate anomaly detection by performing analysis based on geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then perform the analysis.

[0047] The detection unit can customize the types of anomalies it detects based on the user's lifestyle patterns. For example, the detection unit can use AI to customize the types of anomalies it detects based on the user's lifestyle patterns. For example, if the user is active at night, the detection unit will focus on detecting anomalies at night. For example, if the user is active during the day, the detection unit will focus on detecting anomalies during the day. For example, if the user goes out on weekends, the detection unit will focus on detecting anomalies on weekends. By customizing the types of anomalies based on lifestyle patterns, more appropriate anomaly detection becomes possible. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user lifestyle pattern data into a generating AI and customize the types of anomalies that the generating AI detects.

[0048] The detection unit can limit the range of anomalies it detects to a specific area within the user's living space. For example, the detection unit can use AI to limit the range of anomalies it detects to a specific area within the user's living space. For example, if the user is in the living room, the detection unit will only detect anomalies in the living room. For example, if the user is in the bedroom, the detection unit will only detect anomalies in the bedroom. For example, if the user is in the kitchen, the detection unit will only detect anomalies in the kitchen. This allows for anomaly detection while protecting privacy by limiting the detection range to a specific area. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's living space data into a generating AI, and the range of anomalies detected by the generating AI can be limited to a specific area.

[0049] The detection unit can improve its detection accuracy by referring to the user's past anomaly history when detecting anomalies. The detection unit can improve its detection accuracy by referring to the user's past anomaly history when detecting anomalies, for example, using AI. The detection unit can improve the accuracy of anomaly detection based on anomalies the user has experienced in the past, for example. The detection unit can distinguish between normal and abnormal states based on anomalies the user has experienced in the past, for example. The detection unit can highlight and detect specific anomalies based on anomalies the user has experienced in the past. In this way, the accuracy of anomaly detection is improved by improving detection accuracy by referring to past anomaly history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the user's past anomaly history data into a generating AI, which can then improve the detection accuracy.

[0050] The detection unit can detect anomalies based on the user's geographical location information. The detection unit can, for example, use AI to detect anomalies based on the user's geographical location information. For example, if the user is in a specific area, the detection unit will detect anomalies considering the characteristics of that area. For example, if the user is on the move, the detection unit will detect anomalies considering the characteristics of the destination area. For example, if the user is at home, the detection unit will detect anomalies considering the home environment. This makes it possible to detect anomalies more appropriately by performing detection based on geographical location information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location information into a generating AI, and the generating AI can perform the detection.

[0051] The reporting unit can customize the content of its reports according to the type of anomaly. For example, the reporting unit can use AI to customize the content of its reports according to the type of anomaly. For example, if a fall is detected, the reporting unit will report detailed information about the fall. For example, if an illegal entry is detected, the reporting unit will report detailed information about the illegal entry. For example, if a prolonged period of silence is detected, the reporting unit will report detailed information about the silence. By customizing the content of the reports according to the type of anomaly, it becomes possible to provide more appropriate information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the content of the reports according to the type of anomaly into a generating AI, and the generating AI can generate appropriate content for the reports.

[0052] The reporting unit can change the recipient of the report based on the user's specifications. The reporting unit can, for example, use AI to change the recipient of the report based on the user's specifications. The reporting unit can, for example, report to a family member specified by the user. The reporting unit can, for example, report to a friend specified by the user. The reporting unit can, for example, report to a medical institution specified by the user. By changing the recipient of the report based on the user's specifications, more appropriate reporting becomes possible. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the recipient of the report based on the user's specifications into a generating AI, and the generating AI can select an appropriate recipient.

[0053] The reporting unit can improve the accuracy of reports by referring to the user's past reporting history. For example, the reporting unit can use AI to improve the accuracy of reports by referring to the user's past reporting history. For example, the reporting unit can prioritize reporting important information based on the user's past reports. For example, the reporting unit can filter out unnecessary information based on the user's past reports. For example, the reporting unit can highlight specific information based on the user's past reports. By improving reporting accuracy by referring to past reporting history, it becomes possible to provide more appropriate information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the user's past reporting history data into a generating AI, which can then improve the accuracy of reports.

[0054] The reporting unit can make reports based on the user's geographical location information. For example, the reporting unit can use AI to make reports based on the user's geographical location information. For example, if the user is in a specific area, the reporting unit will make reports considering the characteristics of that area. For example, if the user is on the move, the reporting unit will make reports considering the characteristics of the destination area. For example, if the user is at home, the reporting unit will make reports considering the home environment. This makes it possible to provide more appropriate information by making reports based on geographical location information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the user's geographical location information into a generating AI, and the generating AI can make a report.

[0055] The verification unit can customize the health items to be checked based on the user's health status. For example, the verification unit can use AI to customize the health items to be checked based on the user's health status. For example, if the user has high blood pressure, the verification unit will prioritize asking questions about blood pressure. For example, if the user has diabetes, the verification unit will prioritize asking questions about blood sugar levels. For example, if the user has heart disease, the verification unit will prioritize asking questions about heart rate. By customizing the health items based on the user's health status, a more appropriate health check becomes possible. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's health status data into a generating AI and customize the health items that the generating AI checks.

[0056] The verification unit can improve the accuracy of the health checks by referring to the user's past health history for the health check items to be checked. For example, the verification unit uses AI to improve the accuracy of the health checks by referring to the user's past health history for the health check items to be checked. For example, the verification unit prioritizes checking important health check items based on health problems the user has experienced in the past. For example, the verification unit filters out unnecessary health check items based on health problems the user has experienced in the past. For example, the verification unit emphasizes checking specific health check items based on health problems the user has experienced in the past. By improving the accuracy of the checks by referring to past health history, it becomes possible to perform more appropriate health checks. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the user's past health history data into a generating AI, which can then improve the accuracy of the checks.

[0057] The protection unit can customize the privacy items to be protected based on user specifications. The protection unit can, for example, use AI to customize the privacy items to be protected based on user specifications. The protection unit can, for example, prioritize the protection of privacy items specified by the user. The protection unit can, for example, filter out unnecessary information based on the privacy items specified by the user. The protection unit can, for example, highlight and protect specific information based on the privacy items specified by the user. This makes it possible to provide more appropriate privacy protection by customizing the privacy items based on user specifications. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input privacy items based on user specifications into a generating AI, and the generating AI can select appropriate privacy items.

[0058] The protection unit can improve the accuracy of protection by referring to the user's past privacy protection history for the privacy items to be protected. For example, the protection unit uses AI to improve the accuracy of protection by referring to the user's past privacy protection history for the privacy items to be protected. For example, the protection unit prioritizes the protection of important information based on the privacy items the user has protected in the past. For example, the protection unit filters out unnecessary information based on the privacy items the user has protected in the past. For example, the protection unit emphasizes the protection of specific information based on the privacy items the user has protected in the past. This makes it possible to improve the accuracy of protection by referring to past privacy protection history, thereby enabling more appropriate privacy protection. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's past privacy protection history data into a generating AI, which can then improve the accuracy of protection.

[0059] The operator notification unit can customize the content of its notifications according to the type of anomaly. For example, the operator notification unit can use AI to customize the content of its notifications according to the type of anomaly. For example, if a fall is detected, the operator notification unit will report detailed information about the fall. For example, if an illegal entry is detected, the operator notification unit will report detailed information about the illegal entry. For example, if a prolonged period of silence is detected, the operator notification unit will report detailed information about the silence. By customizing the notification content according to the type of anomaly, it becomes possible to provide more appropriate information. Some or all of the above processing in the operator notification unit may be performed using AI, for example, or without AI. For example, the operator notification unit can input notification content according to the type of anomaly into a generating AI, and the generating AI can generate appropriate notification content.

[0060] The operator reporting unit can improve the accuracy of reports by referring to the user's past reporting history. For example, the operator reporting unit uses AI to improve the accuracy of reports by referring to the user's past reporting history. For example, the operator reporting unit prioritizes reporting important information based on the user's past reports. For example, the operator reporting unit filters out unnecessary information based on the user's past reports. For example, the operator reporting unit highlights specific information based on the user's past reports. By improving reporting accuracy by referring to past reporting history, it becomes possible to provide more appropriate information. Some or all of the above processing in the operator reporting unit may be performed using AI, for example, or without AI. For example, the operator reporting unit can input the user's past reporting history data into a generating AI, which can then improve the accuracy of reports.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The monitoring system can also be equipped with an environmental sensor unit. The environmental sensor unit collects environmental data such as temperature, humidity, illuminance, and air quality. The environmental sensor unit collects environmental data 24 hours a day, 365 days a year, using sensors installed in the parents' residence, for example. The environmental sensor unit can detect abnormalities such as abnormally high or low temperatures, abnormally high or low humidity, abnormally high or low illuminance, or deterioration of air quality. By collecting environmental data and detecting abnormalities, the system can monitor the environment of the parents' residence and reduce health risks.

[0063] The monitoring system can also be equipped with a reminder function. The reminder function provides parents with reminders, for example, for taking medication or going to medical appointments. The reminder function can use AI to generate reminders based on the parent's schedule and notify them by voice. For example, the reminder function can make voice notifications such as, "It's time for your medication. Don't forget to take it." If the parent does not respond to the reminder, the reminder function can notify the child, and if the child cannot be contacted, it can notify an operator. This supports parents in managing their health and helps prevent missed medication doses and late doctor's appointments.

[0064] The monitoring system can also be equipped with an entertainment provision unit. This unit provides parents with entertainment content such as music, radio, and audiobooks. It can use AI to select content based on the parent's preferences and play it via voice. The unit can also provide voice notifications, such as, "Today we'll play classical music." Furthermore, it can play content requested by the parent. This can improve the parent's quality of life and reduce feelings of loneliness.

[0065] The monitoring system can also be equipped with an exercise promotion unit. This unit can, for example, provide parents with instructions for light exercise or stretching. It can use AI to suggest appropriate exercises based on the parent's physical condition and health status, and provide voice instructions. The unit can also send voice notifications, such as, "Let's do 5 minutes of stretching now." Furthermore, it can check whether the parent has exercised and send a reminder if they haven't. This helps support the parent's health and prevents a sedentary lifestyle.

[0066] The monitoring system can also be equipped with a communication facilitator. This facilitator facilitates communication between, for example, parents and children, parents and friends, and parents and caregivers. The facilitator, for example, uses AI to prompt communication at the appropriate time based on the parent's emotions and physical condition. The facilitator, for example, sends voice notifications such as, "Why don't you call your child today?" The facilitator, for example, can check whether the parent has communicated and send a reminder if not. This can prevent parental isolation and support their mental health.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects sound. For example, it uses a smart speaker installed in the parents' home to collect everyday sounds 24 hours a day, 365 days a year. The collection unit can collect sounds to detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. It can also use AI to learn to listen only to specific sounds or voices that are related to health and safety management. Step 2: The analysis unit analyzes the audio collected by the collection unit. For example, it uses AI to analyze the collected audio and detect anomalies. The analysis unit can analyze audio to detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. Step 3: The detection unit detects anomalies from the audio analyzed by the analysis unit. For example, AI is used to detect anomalies from the analyzed audio. The detection unit can detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. Step 4: The notification unit reports the anomaly detected by the detection unit. For example, it uses AI to report detected anomalies. If an anomaly is detected, the notification unit can notify the child, and if the child cannot be contacted, it can notify the operator.

[0069] (Example of form 2) The monitoring system according to an embodiment of the present invention is a voice-based monitoring system for children and landlords who face challenges in monitoring elderly people. This monitoring system has the advantage of reliably monitoring while also ensuring the privacy of the person being monitored. For example, the monitoring system involves installing a monitoring smart speaker in the parent's residence. This smart speaker listens to all sounds (voices and noises) 24 hours a day, 365 days a year, and an AI analyzes the content. For example, it can detect abnormalities such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. If an abnormality is detected, the monitoring system notifies the child, and if the child cannot be contacted, it notifies an operator. Next, the monitoring system's AI checks on the parent's health every day. For example, it might ask, "Hello. How are you feeling today?" and the parent might reply, "I'm feeling fine today. I'm in a good mood too." If there is no response or the parent is unwell, the monitoring system notifies the child, and if the child cannot be contacted, it notifies an operator. Furthermore, the monitoring smart speaker detects abnormalities using voice, thus protecting the privacy of the person being monitored. Conventional monitoring systems use video to confirm abnormalities, raising concerns about the privacy of the person being monitored, but this invention eliminates such concerns. This monitoring smart speaker is intended for use by children of elderly parents, real estate companies managing rental properties where elderly people live, and home care workers. Because the AI ​​manages safety and health on a daily basis, it has the advantage of being resource-efficient and enabling safety checks. In addition, the monitoring smart speaker's AI learns to listen only to specific sounds and voices related to health and safety management, so it does not infringe on privacy. This makes it possible to check safety over a wide area while ensuring the privacy of the person being monitored. As a result, the monitoring system can reliably monitor the elderly while ensuring the privacy of the person being monitored.

[0070] The monitoring system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects sound. The collection unit collects everyday sounds 24 hours a day, 365 days a year, for example, using a smart speaker installed in the parents' residence. The collection unit can collect sound for detecting abnormalities such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. The collection unit can learn to listen only to specific sounds or voices related to health and safety management, for example, using AI. The analysis unit analyzes the sound collected by the collection unit. The analysis unit analyzes the collected sound and detects abnormalities, for example, using AI. The analysis unit can analyze sound for detecting abnormalities such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. The detection unit detects abnormalities from the sound analyzed by the analysis unit. The detection unit detects abnormalities from the analyzed sound, for example, using AI. The detection unit can detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. The notification unit notifies the system of the anomalies detected by the detection unit. The notification unit uses AI, for example, to notify the detected anomalies. If an anomaly is detected, the notification unit can notify the child, and if the child cannot be contacted, it can notify an operator. As a result, the monitoring system according to this embodiment can reliably monitor and ensure privacy in the monitoring of the elderly by collecting and analyzing audio, detecting anomalies, and making notifications.

[0071] The data collection unit collects audio. For example, the data collection unit uses a smart speaker installed in the parents' residence to collect ambient sounds 24 hours a day, 365 days a year. Specifically, the smart speaker is equipped with a high-sensitivity microphone that can clearly collect audio from the entire room. This allows the unit to collect everyday sounds, conversations, and ambient sounds in real time and transmit them to a central database. The data collection unit can also collect audio to detect anomalies, such as shouts, cries for help, the presence of intruders, falls, drops, prolonged silence, or absence. This ensures that the data collection unit reliably collects not only everyday sounds but also audio indicating emergencies and abnormal situations. The data collection unit can use AI to learn to listen only to specific sounds and voices related to health and safety management. The AI ​​uses machine learning algorithms to analyze the collected audio data and learn specific patterns and features. This allows the data collection unit to filter out irrelevant audio and collect only important audio. For example, the AI ​​can identify screams, cries for help, footsteps of intruders, and sounds of falls or drops, and is configured to prioritize the collection of these sounds. This allows the collection unit to collect sounds efficiently and effectively, improving the overall system performance. Furthermore, the collection unit can centrally manage the collected audio data and collaborate with other systems and departments as needed. For instance, the collected audio data can be stored on a cloud server, making it accessible to the analysis and detection units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect sounds efficiently and effectively, improving the overall system performance.

[0072] The analysis department analyzes the audio collected by the collection department. For example, the analysis department uses AI to analyze the collected audio and detect anomalies. Specifically, the AI ​​uses speech recognition technology to analyze the collected audio data and identify abnormal audio patterns. For example, it can identify shouts, cries for help, footsteps of intruders, and sounds of falls or drops, and prioritizes the analysis of these sounds. The AI ​​can analyze the audio data along a timeline to identify the timing and frequency of abnormal audio patterns. This allows the analysis department to quickly and accurately analyze the collected audio data and detect anomalies. Furthermore, the analysis department can utilize historical audio data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past abnormal audio data, it can predict risk fluctuations in specific time periods or situations and formulate future countermeasures. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal audio, issuing early warnings. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system. Furthermore, the analysis unit can continuously learn from the collected audio data and improve its analysis accuracy. For example, if a new abnormal audio pattern occurs, the AI ​​learns that pattern and incorporates it into subsequent analyses. This allows the analysis unit to always provide highly accurate analyses based on the latest information, supporting quick and appropriate responses.

[0073] The detection unit detects anomalies from the audio analyzed by the analysis unit. The detection unit uses, for example, AI to detect anomalies from the analyzed audio. Specifically, the AI ​​uses an anomaly detection algorithm to analyze the audio data and identify abnormal audio patterns. For example, it can identify shouts, cries for help, footsteps of intruders, and sounds of falls or drops, and prioritize the detection of these sounds. The AI ​​can analyze the audio data along a timeline to identify the timing and frequency of abnormal audio patterns. This allows the detection unit to quickly and accurately analyze the collected audio data and detect anomalies. Furthermore, the detection unit can utilize past audio data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past abnormal audio data, it can predict risk fluctuations in specific time periods or situations and formulate future countermeasures. The detection unit can also use the anomaly detection algorithm to detect unusual patterns and abnormal audio, issuing warnings early. This allows the detection unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system. Furthermore, the detection unit can continuously learn from the collected audio data and improve its analysis accuracy. For example, if a new abnormal audio pattern occurs, the AI ​​learns that pattern and incorporates it into subsequent analyses. This allows the detection unit to always provide highly accurate analysis based on the latest information, supporting quick and appropriate responses.

[0074] The reporting unit reports anomalies detected by the detection unit. The reporting unit uses AI, for example, to report detected anomalies. Specifically, when an anomaly is detected, the reporting unit automatically notifies pre-configured contacts. For example, if an anomaly is detected, it can notify the child, and if the child cannot be reached, it can notify an operator. The reporting unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the reporting unit to quickly and reliably report anomalies and support appropriate responses. Furthermore, the reporting unit can continuously improve the accuracy and effectiveness of its reports. For example, it can review and improve the content of reports based on feedback after reporting. In addition, the reporting unit can flexibly change the recipients and methods of reporting depending on the type and urgency of the anomaly. For example, if a highly urgent anomaly is detected, it can simultaneously notify multiple contacts to encourage a rapid response. This allows the reporting unit to quickly and reliably notify users of anomalies and minimize the risk of disaster. Furthermore, the reporting unit can continuously improve the accuracy and effectiveness of its reports. For example, based on feedback received after a report is submitted, the content of the report can be reviewed and improved. Furthermore, the reporting department can flexibly change the recipients and methods of reporting depending on the type and urgency of the anomaly. For instance, if a highly urgent anomaly is detected, multiple contacts can be notified simultaneously to encourage a rapid response. This allows the reporting department to quickly and reliably notify users of anomalies, minimizing the risk of damage.

[0075] The verification unit checks the child's health. The verification unit, for example, uses AI to check on the parent's health every day. The verification unit asks questions such as, "Hello. How are you feeling today?" and the parent replies, "I'm feeling fine today. I'm in a good mood too." If there is no response or the child is unwell, the verification unit can notify the child, and if the child cannot be contacted, it can notify an operator. This allows for monitoring the child's health, understanding their health status, and detecting abnormalities early. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input questions for checking the parent's health into a generating AI, which can then generate appropriate questions.

[0076] The protection unit protects privacy. The protection unit protects the privacy of the person being monitored by using AI, for example, to detect abnormalities through voice. The protection unit's AI learns to listen only to content related to health and safety management, such as specific sounds or voices. This enhances the sense of security of the person being monitored by protecting their privacy. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input anonymization and access restriction of voice data into a generating AI, which can then generate an appropriate privacy protection method.

[0077] The operator notification unit notifies the operator. The operator notification unit, for example, uses AI to notify the child if an abnormality is detected, and if the child cannot be contacted, it notifies the operator. This allows for a quick response in emergencies by notifying the operator. Some or all of the above processing in the operator notification unit may be performed using AI, for example, or without AI. For example, the operator notification unit can input the notification content when an abnormality is detected into a generating AI, and the generating AI can generate appropriate notification content.

[0078] The collection unit can collect ambient sounds 24 hours a day, 365 days a year. For example, the collection unit can use a smart speaker installed in the parents' residence to collect ambient sounds 24 hours a day, 365 days a year. The collection unit can collect sounds to detect anomalies, such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. This allows for constant monitoring by collecting ambient sounds 24 hours a day, 365 days a year. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input settings for collecting ambient sounds 24 hours a day, 365 days a year into a generating AI, which can then generate appropriate collection settings.

[0079] The analysis unit can analyze the collected audio and detect anomalies. The analysis unit can, for example, use AI to analyze the collected audio and detect anomalies. The analysis unit can analyze audio to detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. By analyzing the collected audio and detecting anomalies, a rapid response becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected audio into a generating AI, which can then perform analysis to detect anomalies.

[0080] The collection unit can estimate the user's emotions and adjust the timing of audio collection based on the estimated emotions. The collection unit can, for example, use AI to estimate the user's emotions and adjust the timing of audio collection based on the estimated emotions. For example, if the user is stressed, the collection unit can reduce the frequency of audio collection to respect privacy. For example, if the user is relaxed, the collection unit can increase the frequency of audio collection to collect more detailed data. For example, if the user is in a hurry, the collection unit can temporarily stop audio collection and resume it later. This allows for data collection while respecting privacy by adjusting the timing of audio collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not using AI. For example, the collection unit can input user emotion data into a generative AI, which can then adjust the timing of audio collection.

[0081] The data collection unit can customize the types of audio collected based on the user's lifestyle patterns. For example, the data collection unit can use AI to customize the types of audio collected based on the user's lifestyle patterns. For example, if the user is active at night, the data collection unit will focus on collecting nighttime audio. For example, if the user is active during the day, the data collection unit will focus on collecting daytime audio. For example, if the user goes out on weekends, the data collection unit will focus on collecting weekend audio. By customizing the types of audio based on the user's lifestyle patterns, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user lifestyle pattern data into a generating AI and customize the types of audio that the generating AI collects.

[0082] The collection unit can limit the range of sound to be collected to a specific area within the user's living space. For example, the collection unit can use AI to limit the range of sound to be collected to a specific area within the user's living space. For example, if the user is in the living room, the collection unit will only collect sound from the living room. For example, if the user is in the bedroom, the collection unit will only collect sound from the bedroom. For example, if the user is in the kitchen, the collection unit will only collect sound from the kitchen. By limiting the range of sound collection to a specific area, data can be collected while protecting privacy. Some or all of the above 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 living space data into a generating AI, and the generating AI can limit the range of sound to be collected to a specific area.

[0083] The data collection unit can estimate the user's emotions and determine the priority of audio to collect based on the estimated user emotions. The data collection unit can, for example, use AI to estimate the user's emotions and determine the priority of audio to collect based on the estimated user emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting audio that provides a sense of security. For example, if the user is excited, the data collection unit will prioritize collecting audio that helps maintain calmness. For example, if the user is tired, the data collection unit will prioritize collecting audio that helps relax. By prioritizing audio based on the user's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can determine the priority of audio to collect.

[0084] The collection unit can filter the audio to be collected by referring to the user's past audio history. The collection unit can, for example, use AI to filter the audio to be collected by referring to the user's past audio history. The collection unit can, for example, prioritize the collection of important audio based on audio data previously collected by the user. The collection unit can, for example, filter out unnecessary audio based on audio data previously collected by the user. The collection unit can, for example, emphasize and collect specific audio based on audio data previously collected by the user. In this way, by filtering by referring to past audio history, important audio can be collected preferentially. Some or all of the above 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 past audio history data into a generating AI, and the generating AI can filter the audio to be collected.

[0085] The collection unit can select the optimal collection method for the audio to be collected based on the user's device information. For example, the collection unit may use AI to select the optimal collection method for the audio to be collected based on the user's device information. For example, if the user is using a smartphone, the collection unit will use the smartphone's microphone to collect the audio. For example, if the user is using a smart speaker, the collection unit will use the smart speaker's microphone to collect the audio. For example, if the user is using a wearable device, the collection unit will use the wearable device's microphone to collect the audio. This allows for efficient audio collection by selecting the optimal collection method based on device information. Some or all of the above 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 device information into a generating AI, which can then select the optimal collection method.

[0086] The analysis unit can estimate the user's emotions and adjust the voice analysis method based on the estimated user emotions. For example, the analysis unit may use AI to estimate the user's emotions and adjust the voice analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will perform a detailed analysis and detect subtle anomalies. For example, if the user is in a hurry, the analysis unit will perform a rapid analysis and prioritize the detection of major anomalies. For example, if the user is excited, the analysis unit will perform an analysis that takes emotional fluctuations into account. This allows for more appropriate analysis by adjusting the voice analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the voice analysis method.

[0087] The analysis unit can classify and analyze the audio data to be analyzed according to different time periods. The analysis unit can, for example, use AI to classify and analyze the audio data according to different time periods. The analysis unit can, for example, analyze daytime audio data and nighttime audio data separately and detect anomalies. The analysis unit can, for example, analyze weekday audio data and weekend audio data separately and detect anomalies. The analysis unit can, for example, classify and analyze audio data for specific time periods (e.g., morning, noon, night) and detect anomalies. By classifying and analyzing audio data according to different time periods, the accuracy of anomaly detection is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data from different time periods into a generating AI, and the generating AI can classify and analyze the audio data.

[0088] The analysis unit can apply different algorithms to the audio data being analyzed based on the user's lifestyle patterns. For example, the analysis unit can use AI to apply different algorithms to the audio data being analyzed based on the user's lifestyle patterns. For example, if the user is active at night, the analysis unit will apply a night-specific algorithm for analysis. For example, if the user is active during the day, the analysis unit will apply a day-specific algorithm for analysis. For example, if the user goes out on weekends, the analysis unit will apply a weekend-specific algorithm for analysis. By applying different algorithms based on lifestyle patterns, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's lifestyle pattern data into a generating AI, and the generating AI can apply different algorithms for analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can use AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the display method of the analysis results.

[0090] The analysis unit can improve the accuracy of its analysis by referencing the user's past voice data into the audio data being analyzed. For example, the analysis unit uses AI to improve the accuracy of its analysis by referencing the user's past voice data into the audio data being analyzed. For example, the analysis unit improves the accuracy of detecting anomalies based on audio data previously collected by the user. For example, the analysis unit distinguishes between normal and abnormal voices based on audio data previously collected by the user. For example, the analysis unit emphasizes and analyzes specific anomalies based on audio data previously collected by the user. By improving the accuracy of analysis by referencing past voice data, the accuracy of anomaly detection is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past voice data into a generating AI, which can then improve the accuracy of its analysis.

[0091] The analysis unit can perform analysis on audio data based on the user's geographical location information. For example, the analysis unit can use AI to perform analysis on audio data based on the user's geographical location information. For example, if the user is in a specific region, the analysis unit will consider the characteristics of that region during the analysis. For example, if the user is on the move, the analysis unit will consider the characteristics of the destination region during the analysis. For example, if the user is at home, the analysis unit will consider the home environment during the analysis. This allows for more appropriate anomaly detection by performing analysis based on geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then perform the analysis.

[0092] The detection unit can estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. The detection unit can, for example, use AI to estimate the user's emotions and adjust the anomaly detection criteria based on the estimated user emotions. For example, the detection unit tightens the anomaly detection criteria if the user is feeling anxious. For example, the detection unit loosens the anomaly detection criteria if the user is relaxed. For example, the detection unit dynamically adjusts the anomaly detection criteria if the user is excited. This allows for more appropriate anomaly detection by adjusting the anomaly detection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generative AI, which can then adjust the anomaly detection criteria.

[0093] The detection unit can customize the types of anomalies it detects based on the user's lifestyle patterns. For example, the detection unit can use AI to customize the types of anomalies it detects based on the user's lifestyle patterns. For example, if the user is active at night, the detection unit will focus on detecting anomalies at night. For example, if the user is active during the day, the detection unit will focus on detecting anomalies during the day. For example, if the user goes out on weekends, the detection unit will focus on detecting anomalies on weekends. By customizing the types of anomalies based on lifestyle patterns, more appropriate anomaly detection becomes possible. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user lifestyle pattern data into a generating AI and customize the types of anomalies that the generating AI detects.

[0094] The detection unit can limit the range of anomalies it detects to a specific area within the user's living space. For example, the detection unit can use AI to limit the range of anomalies it detects to a specific area within the user's living space. For example, if the user is in the living room, the detection unit will only detect anomalies in the living room. For example, if the user is in the bedroom, the detection unit will only detect anomalies in the bedroom. For example, if the user is in the kitchen, the detection unit will only detect anomalies in the kitchen. This allows for anomaly detection while protecting privacy by limiting the detection range to a specific area. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's living space data into a generating AI, and the range of anomalies detected by the generating AI can be limited to a specific area.

[0095] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. The detection unit can, for example, use AI to estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. For example, if the user is tense, the detection unit provides a simple and highly visible display method. For example, if the user is relaxed, the detection unit provides a display method that includes detailed information. For example, if the user is in a hurry, the detection unit provides a display method that gets straight to the point. By adjusting the display method of the detection results based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI, and the generative AI can adjust the display method of the detection results.

[0096] The detection unit can improve its detection accuracy by referring to the user's past anomaly history when detecting anomalies. The detection unit can improve its detection accuracy by referring to the user's past anomaly history when detecting anomalies, for example, using AI. The detection unit can improve the accuracy of anomaly detection based on anomalies the user has experienced in the past, for example. The detection unit can distinguish between normal and abnormal states based on anomalies the user has experienced in the past, for example. The detection unit can highlight and detect specific anomalies based on anomalies the user has experienced in the past. In this way, the accuracy of anomaly detection is improved by improving detection accuracy by referring to past anomaly history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the user's past anomaly history data into a generating AI, which can then improve the detection accuracy.

[0097] The detection unit can detect anomalies based on the user's geographical location information. The detection unit can, for example, use AI to detect anomalies based on the user's geographical location information. For example, if the user is in a specific area, the detection unit will detect anomalies considering the characteristics of that area. For example, if the user is on the move, the detection unit will detect anomalies considering the characteristics of the destination area. For example, if the user is at home, the detection unit will detect anomalies considering the home environment. This makes it possible to detect anomalies more appropriately by performing detection based on geographical location information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location information into a generating AI, and the generating AI can perform the detection.

[0098] The reporting unit can estimate the user's emotions and adjust the timing of the report based on the estimated emotions. The reporting unit can, for example, use AI to estimate the user's emotions and adjust the timing of the report based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit will report quickly. For example, if the user is relaxed, the reporting unit will delay the timing of the report. For example, if the user is excited, the reporting unit will dynamically adjust the timing of the report. By adjusting the timing of the report based on the user's emotions, it becomes possible to report at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input user emotion data into a generative AI, which can then adjust the timing of the report.

[0099] The reporting unit can customize the content of its reports according to the type of anomaly. For example, the reporting unit can use AI to customize the content of its reports according to the type of anomaly. For example, if a fall is detected, the reporting unit will report detailed information about the fall. For example, if an illegal entry is detected, the reporting unit will report detailed information about the illegal entry. For example, if a prolonged period of silence is detected, the reporting unit will report detailed information about the silence. By customizing the content of the reports according to the type of anomaly, it becomes possible to provide more appropriate information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the content of the reports according to the type of anomaly into a generating AI, and the generating AI can generate appropriate content for the reports.

[0100] The reporting unit can change the recipient of the report based on the user's specifications. The reporting unit can, for example, use AI to change the recipient of the report based on the user's specifications. The reporting unit can, for example, report to a family member specified by the user. The reporting unit can, for example, report to a friend specified by the user. The reporting unit can, for example, report to a medical institution specified by the user. By changing the recipient of the report based on the user's specifications, more appropriate reporting becomes possible. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the recipient of the report based on the user's specifications into a generating AI, and the generating AI can select an appropriate recipient.

[0101] The reporting unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, the reporting unit may use AI to estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit will prioritize important reports. For example, if the user is relaxed, the reporting unit will postpone less important reports. For example, if the user is agitated, the reporting unit will dynamically adjust the priority of reports. This allows for prioritizing more important reports by determining the priority of reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI, which can then determine the priority of reports.

[0102] The reporting unit can improve the accuracy of reports by referring to the user's past reporting history. For example, the reporting unit can use AI to improve the accuracy of reports by referring to the user's past reporting history. For example, the reporting unit can prioritize reporting important information based on the user's past reports. For example, the reporting unit can filter out unnecessary information based on the user's past reports. For example, the reporting unit can highlight specific information based on the user's past reports. By improving reporting accuracy by referring to past reporting history, it becomes possible to provide more appropriate information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the user's past reporting history data into a generating AI, which can then improve the accuracy of reports.

[0103] The reporting unit can make reports based on the user's geographical location information. For example, the reporting unit can use AI to make reports based on the user's geographical location information. For example, if the user is in a specific area, the reporting unit will make reports considering the characteristics of that area. For example, if the user is on the move, the reporting unit will make reports considering the characteristics of the destination area. For example, if the user is at home, the reporting unit will make reports considering the home environment. This makes it possible to provide more appropriate information by making reports based on geographical location information. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the user's geographical location information into a generating AI, and the generating AI can make a report.

[0104] The verification unit can estimate the user's emotions and adjust the timing of health checks based on the estimated emotions. The verification unit can, for example, use AI to estimate the user's emotions and adjust the timing of health checks based on the estimated emotions. For example, if the user is feeling anxious, the verification unit will perform health checks more frequently. For example, if the user is relaxed, the verification unit will delay the timing of health checks. For example, if the user is excited, the verification unit will dynamically adjust the timing of health checks. By adjusting the timing of health checks based on the user's emotions, health checks can be performed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input user emotion data into a generative AI, which can then adjust the timing of health checks.

[0105] The verification unit can customize the health items to be checked based on the user's health status. For example, the verification unit can use AI to customize the health items to be checked based on the user's health status. For example, if the user has high blood pressure, the verification unit will prioritize asking questions about blood pressure. For example, if the user has diabetes, the verification unit will prioritize asking questions about blood sugar levels. For example, if the user has heart disease, the verification unit will prioritize asking questions about heart rate. By customizing the health items based on the user's health status, a more appropriate health check becomes possible. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's health status data into a generating AI and customize the health items that the generating AI checks.

[0106] The verification unit can estimate the user's emotions and determine the priority of health checks based on the estimated emotions. The verification unit can, for example, use AI to estimate the user's emotions and determine the priority of health checks based on the estimated emotions. For example, if the user is feeling anxious, the verification unit will prioritize checking important health items. For example, if the user is relaxed, the verification unit will postpone checking less important health items. For example, if the user is excited, the verification unit will dynamically adjust the priority of health checks. This allows for prioritizing health checks based on the user's emotions, thereby prioritizing the checking of more important health items. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's emotion data into a generative AI, which can then determine the priority of health checks.

[0107] The verification unit can improve the accuracy of the health checks by referring to the user's past health history for the health check items to be checked. For example, the verification unit uses AI to improve the accuracy of the health checks by referring to the user's past health history for the health check items to be checked. For example, the verification unit prioritizes checking important health check items based on health problems the user has experienced in the past. For example, the verification unit filters out unnecessary health check items based on health problems the user has experienced in the past. For example, the verification unit emphasizes checking specific health check items based on health problems the user has experienced in the past. By improving the accuracy of the checks by referring to past health history, it becomes possible to perform more appropriate health checks. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the user's past health history data into a generating AI, which can then improve the accuracy of the checks.

[0108] The protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated user emotions. The protection unit can, for example, use AI to estimate the user's emotions and adjust the privacy protection method based on the estimated user emotions. For example, if the user is feeling anxious, the protection unit can increase the level of privacy protection. For example, if the user is relaxed, the protection unit can loosen the level of privacy protection. For example, if the user is excited, the protection unit can dynamically adjust the privacy protection method. This allows for more appropriate privacy protection by adjusting the privacy protection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input user emotion data into a generative AI, which can then adjust the privacy protection method.

[0109] The protection unit can customize the privacy items to be protected based on user specifications. The protection unit can, for example, use AI to customize the privacy items to be protected based on user specifications. The protection unit can, for example, prioritize the protection of privacy items specified by the user. The protection unit can, for example, filter out unnecessary information based on the privacy items specified by the user. The protection unit can, for example, highlight and protect specific information based on the privacy items specified by the user. This makes it possible to provide more appropriate privacy protection by customizing the privacy items based on user specifications. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input privacy items based on user specifications into a generating AI, and the generating AI can select appropriate privacy items.

[0110] The protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated user emotions. The protection unit can, for example, use AI to estimate the user's emotions and determine the priority of privacy protection based on the estimated user emotions. For example, if the user is feeling anxious, the protection unit will prioritize protecting important privacy items. For example, if the user is relaxed, the protection unit will postpone less important privacy items. For example, if the user is excited, the protection unit will dynamically adjust the priority of privacy protection. This allows for prioritizing the protection of more important privacy items by determining the priority of privacy protection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the protection unit may be performed using AI or not using AI. For example, the protection unit can input user emotion data into a generative AI, which can then determine the priority of privacy protection.

[0111] The protection unit can improve the accuracy of protection by referring to the user's past privacy protection history for the privacy items to be protected. For example, the protection unit uses AI to improve the accuracy of protection by referring to the user's past privacy protection history for the privacy items to be protected. For example, the protection unit prioritizes the protection of important information based on the privacy items the user has protected in the past. For example, the protection unit filters out unnecessary information based on the privacy items the user has protected in the past. For example, the protection unit emphasizes the protection of specific information based on the privacy items the user has protected in the past. This makes it possible to improve the accuracy of protection by referring to past privacy protection history, thereby enabling more appropriate privacy protection. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's past privacy protection history data into a generating AI, which can then improve the accuracy of protection.

[0112] The operator notification unit can estimate the user's emotions and adjust the timing of the operator notification based on the estimated emotions. The operator notification unit can, for example, use AI to estimate the user's emotions and adjust the timing of the operator notification based on the estimated emotions. For example, if the user is feeling anxious, the operator notification unit will make a notification quickly. For example, if the user is relaxed, the operator notification unit will delay the timing of the notification. For example, if the user is excited, the operator notification unit will dynamically adjust the timing of the notification. By adjusting the timing of the operator notification based on the user's emotions, it becomes possible to make notifications at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operator notification unit may be performed using AI, for example, or without AI. For example, the operator notification unit can input user emotion data into a generative AI, and the generative AI can adjust the timing of the operator notification.

[0113] The operator notification unit can customize the content of its notifications according to the type of anomaly. For example, the operator notification unit can use AI to customize the content of its notifications according to the type of anomaly. For example, if a fall is detected, the operator notification unit will report detailed information about the fall. For example, if an illegal entry is detected, the operator notification unit will report detailed information about the illegal entry. For example, if a prolonged period of silence is detected, the operator notification unit will report detailed information about the silence. By customizing the notification content according to the type of anomaly, it becomes possible to provide more appropriate information. Some or all of the above processing in the operator notification unit may be performed using AI, for example, or without AI. For example, the operator notification unit can input notification content according to the type of anomaly into a generating AI, and the generating AI can generate appropriate notification content.

[0114] The operator notification unit can estimate the user's emotions and determine the priority of operator notifications based on the estimated emotions. For example, the operator notification unit may use AI to estimate the user's emotions and determine the priority of operator notifications based on the estimated emotions. For example, if the user is feeling anxious, the operator notification unit will prioritize important notifications. For example, if the user is relaxed, the operator notification unit will postpone less important notifications. For example, if the user is agitated, the operator notification unit will dynamically adjust the notification priority. This allows for prioritizing more important notifications by determining the priority of operator notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the operator notification unit may be performed using AI or not. For example, the operator notification unit can input user emotion data into a generative AI, which can then determine the priority of operator notifications.

[0115] The operator reporting unit can improve the accuracy of reports by referring to the user's past reporting history. For example, the operator reporting unit uses AI to improve the accuracy of reports by referring to the user's past reporting history. For example, the operator reporting unit prioritizes reporting important information based on the user's past reports. For example, the operator reporting unit filters out unnecessary information based on the user's past reports. For example, the operator reporting unit highlights specific information based on the user's past reports. By improving reporting accuracy by referring to past reporting history, it becomes possible to provide more appropriate information. Some or all of the above processing in the operator reporting unit may be performed using AI, for example, or without AI. For example, the operator reporting unit can input the user's past reporting history data into a generating AI, which can then improve the accuracy of reports.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The monitoring system can also be equipped with an environmental sensor unit. The environmental sensor unit collects environmental data such as temperature, humidity, illuminance, and air quality. The environmental sensor unit collects environmental data 24 hours a day, 365 days a year, using sensors installed in the parents' residence, for example. The environmental sensor unit can detect abnormalities such as abnormally high or low temperatures, abnormally high or low humidity, abnormally high or low illuminance, or deterioration of air quality. By collecting environmental data and detecting abnormalities, the system can monitor the environment of the parents' residence and reduce health risks.

[0118] The monitoring system can also be equipped with a reminder function. The reminder function provides parents with reminders, for example, for taking medication or going to medical appointments. The reminder function can use AI to generate reminders based on the parent's schedule and notify them by voice. For example, the reminder function can make voice notifications such as, "It's time for your medication. Don't forget to take it." If the parent does not respond to the reminder, the reminder function can notify the child, and if the child cannot be contacted, it can notify an operator. This supports parents in managing their health and helps prevent missed medication doses and late doctor's appointments.

[0119] The monitoring system can also be equipped with an entertainment provision unit. This unit provides parents with entertainment content such as music, radio, and audiobooks. It can use AI to select content based on the parent's preferences and play it via voice. The unit can also provide voice notifications, such as, "Today we'll play classical music." Furthermore, it can play content requested by the parent. This can improve the parent's quality of life and reduce feelings of loneliness.

[0120] The monitoring system can also be equipped with an exercise promotion unit. This unit can, for example, provide parents with instructions for light exercise or stretching. It can use AI to suggest appropriate exercises based on the parent's physical condition and health status, and provide voice instructions. The unit can also send voice notifications, such as, "Let's do 5 minutes of stretching now." Furthermore, it can check whether the parent has exercised and send a reminder if they haven't. This helps support the parent's health and prevents a sedentary lifestyle.

[0121] The monitoring system can also be equipped with a communication facilitator. This facilitator facilitates communication between, for example, parents and children, parents and friends, and parents and caregivers. The facilitator, for example, uses AI to prompt communication at the appropriate time based on the parent's emotions and physical condition. The facilitator, for example, sends voice notifications such as, "Why don't you call your child today?" The facilitator, for example, can check whether the parent has communicated and send a reminder if not. This can prevent parental isolation and support their mental health.

[0122] The monitoring system can also be equipped with an emotion estimation unit. The emotion estimation unit estimates emotions from, for example, the parent's voice and facial expressions, and takes appropriate action based on the estimated emotion. For example, if the parent is feeling anxious, the emotion estimation unit will play relaxing music. For example, if the parent is sad, the emotion estimation unit will offer words of encouragement. For example, if the parent is happy, the emotion estimation unit will offer words of empathy. This allows the system to respond in a way that is attentive to the parent's emotions and provide emotional support.

[0123] The monitoring system can also be equipped with a stress management unit. This unit can, for example, estimate the parent's stress level from their voice and behavior, and take appropriate action based on the estimated stress level. For example, if the parent is experiencing high stress, the stress management unit might play relaxing music. If the parent is experiencing moderate stress, it might suggest light exercise or stretching. If the parent is experiencing low stress, it might provide entertainment content. This can help reduce parental stress and support their mental health.

[0124] The monitoring system can also be equipped with an emotion-sharing function. This function can, for example, share a parent's emotions with children or caregivers to encourage appropriate responses. For example, if a parent is feeling anxious, the function can notify the child and encourage them to contact the parent. For example, if a parent is happy, the function can notify the caregiver and encourage them to share in the parent's joy. For example, if a parent is sad, the function can notify a friend and encourage them to comfort the parent. This allows for the sharing of the parent's emotions and enables those around them to provide appropriate support.

[0125] The monitoring system can also be equipped with an emotional history unit. This unit can, for example, record changes in the parent's emotions and analyze long-term emotional trends. It can also record, for example, what emotions the parent experienced in what situations. Furthermore, it can display the parent's emotional changes in graphs or charts for children and caregivers to review. Finally, it can predict future emotional changes based on the parent's emotional trends and suggest appropriate responses. This allows for understanding the parent's emotional changes and supporting their long-term mental health.

[0126] The monitoring system can also be equipped with an emotional feedback unit. This unit can, for example, provide feedback on the parent's emotions and support emotional self-management. For example, if the parent is feeling anxious, it can identify the cause and suggest coping strategies. For example, if the parent is happy, it can provide advice on how to maintain that feeling. For example, if the parent is sad, it can suggest ways to alleviate that feeling. This allows the parent to understand their own emotions and deal with them appropriately.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The collection unit collects sound. For example, it uses a smart speaker installed in the parents' home to collect everyday sounds 24 hours a day, 365 days a year. The collection unit can collect sounds to detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. It can also use AI to learn to listen only to specific sounds or voices that are related to health and safety management. Step 2: The analysis unit analyzes the audio collected by the collection unit. For example, it uses AI to analyze the collected audio and detect anomalies. The analysis unit can analyze audio to detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. Step 3: The detection unit detects anomalies from the audio analyzed by the analysis unit. For example, AI is used to detect anomalies from the analyzed audio. The detection unit can detect anomalies such as screams or cries for help, the presence of intruders, falls or drops, and prolonged silence or absence. Step 4: The notification unit reports the anomaly detected by the detection unit. For example, it uses AI to report detected anomalies. If an anomaly is detected, the notification unit can notify the child, and if the child cannot be contacted, it can notify the operator.

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, confirmation unit, protection unit, and operator notification unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects voice using the microphone 38B of the smart device 14 and transmits the collected voice to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected voice. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and detects abnormalities from the analyzed voice. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and notifies the detected abnormality. The confirmation unit is implemented, for example, by the control unit 46A of the smart device 14 and checks the health of the parent by speaking to them. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and detects whether there are any abnormalities in the voice. The operator notification unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and notifies the operator when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, confirmation unit, protection unit, and operator notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects voice using the microphone 238 of the smart glasses 214 and transmits the collected voice to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected voice. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects abnormalities from the analyzed voice. The notification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and notifies the detected abnormality. The confirmation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and checks the health of the parent by speaking to them. The protection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects whether there are any abnormalities in the voice. The operator notification unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and notifies the operator when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, confirmation unit, protection unit, and operator notification unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects voice using the microphone 238 of the headset terminal 314 and transmits the collected voice to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected voice. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and detects abnormalities from the analyzed voice. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and notifies the detected abnormality. The confirmation unit is implemented, for example, by the control unit 46A of the headset terminal 314 and checks the health of the parent by calling out to them. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and detects whether there are any abnormalities in the voice. The operator notification unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and notifies the operator when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 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.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, notification unit, confirmation unit, protection unit, and operator notification unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects voice using the microphone 238 of the robot 414 and transmits the collected voice to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected voice. The detection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and detects abnormalities from the analyzed voice. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and notifies the detected abnormality. The confirmation unit is implemented, for example, by the control unit 46A of the robot 414 and checks the health of the parent by speaking to them. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and detects whether there are any abnormalities in the voice. The operator notification unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and notifies the operator when an abnormality is detected. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0182] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A collection unit that collects sound, An analysis unit analyzes the sound collected by the aforementioned collection unit, A detection unit that detects anomalies from the audio analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies the abnormality detected by the aforementioned detection unit. A system characterized by the following features. (Note 2) Equipped with a unit to check the user's physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a protective section to protect privacy. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with an operator notification unit that notifies an operator. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collects everyday sounds 24 hours a day, 365 days a year. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is The collected audio is analyzed to detect anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of audio collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Customize the types of audio collected based on the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The range of audio collected is limited to a specific area within the user's living space. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of audio to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The collected audio is filtered by referencing the user's past voice history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The optimal audio collection method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the voice analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is The audio data to be analyzed is classified and analyzed according to different time periods. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is The audio data to be analyzed is analyzed using different algorithms based on the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is By referencing the user's past voice data in the audio data being analyzed, the accuracy of the analysis can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is The analysis of the audio data is performed based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is The system estimates the user's emotions and adjusts the anomaly detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is Customize the types of anomalies to detect based on the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is The scope of detected anomalies is limited to a specific area within the user's living space. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is The system improves detection accuracy by referencing the user's past anomaly history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is The system detects anomalies based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reporting unit, The system estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reporting unit, Customize the content of the report according to the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reporting unit, The recipient of the report can be changed based on the user's specifications. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reporting unit, The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reporting unit, The system improves the accuracy of reports by referencing the user's past reporting history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reporting unit, The report will be based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is The system estimates the user's emotions and adjusts the timing of health checks based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned verification unit is Customize the health check items based on the user's health status. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned verification unit is The system estimates the user's emotions and prioritizes health checks based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned verification unit is The system improves the accuracy of the health checks by referencing the user's past health history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned protective part is We estimate the user's emotions and adjust our privacy protection methods based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned protective part is Customize the privacy items to protect based on user specifications. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned protective part is It estimates user sentiment and determines privacy protection priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned protective part is By referencing the user's past privacy protection history, we can improve the accuracy of the privacy items being protected. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned operator notification unit is The system estimates the user's emotions and adjusts the timing of operator notifications based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned operator notification unit is Customize the content of the report according to the type of anomaly. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned operator notification unit is The system estimates the user's emotions and prioritizes operator calls based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned operator notification unit is The system improves the accuracy of reports by referencing the user's past reporting history. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects sound, An analysis unit analyzes the sound collected by the aforementioned collection unit, A detection unit that detects anomalies from the audio analyzed by the aforementioned analysis unit, The system includes a notification unit that notifies the abnormality detected by the aforementioned detection unit. A system characterized by the following features.

2. Equipped with a unit to check the user's physical condition. The system according to feature 1.

3. Equipped with a protective section to protect privacy. The system according to feature 1.

4. It is equipped with an operator notification unit that notifies an operator. The system according to feature 1.

5. The aforementioned collection unit is Collects everyday sounds 24 hours a day, 365 days a year. The system according to feature 1.

6. The aforementioned analysis unit is The collected audio is analyzed to detect anomalies. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of audio collection based on those emotions. The system according to feature 1.

8. The aforementioned collection unit is Customize the types of audio collected based on the user's lifestyle patterns. The system according to feature 1.

Images