system

Abstract

The system according to the embodiment aims to convey information in a manner that is familiar to the user. [Solution] A system according to an embodiment includes a sensor unit, an analysis unit, an adjustment unit, and an output unit. The sensor unit detects an abnormality. The analysis unit analyzes the type and urgency of the abnormality detected by the sensor unit. The adjustment unit adjusts the warning sound in a specific manner based on the analysis results obtained by the analysis unit. The output unit outputs the warning sound adjusted by the adjustment unit.

Description

[Technical Field]

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

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

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

[0004] In conventional technology, the warning sounds of home appliances may be annoying to users.

[0005] The system according to the embodiment aims to convey information in a manner that is familiar to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, an analysis unit, an adjustment unit, and an output unit. The sensor unit detects an abnormality. The analysis unit analyzes the type and urgency of the abnormality detected by the sensor unit. The adjustment unit adjusts the warning sound in a specific manner based on the analysis results obtained by the analysis unit. The output unit outputs the warning sound adjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to the embodiment can convey information in a manner that is familiar to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An alarm sound adjustment system according to an embodiment of the present invention uses a generation AI to assess the situation and gradually increase the intensity of the alarm sound to prevent the alarm sound emitted by a home appliance from annoying the user. When the home appliance detects an abnormality, the system uses sensors and cameras to analyze the type and urgency of the abnormality. Next, the generation AI gradually adjusts the volume and type of the alarm sound based on the analysis results. The alarm initially issues a gentle sound and gradually increases the volume if the user does not respond. Furthermore, the generation AI monitors the user's reaction using voice recognition and motion detection and stops the alarm sound at the appropriate time. This mechanism allows the alarm sound emitted by the home appliance to effectively communicate information without annoying the user. For example, when a home appliance detects an abnormality, the system uses temperature sensors, humidity sensors, and cameras to analyze the type and urgency of the abnormality. The generation AI gradually adjusts the volume and type of the alarm sound based on the analysis results. The alarm initially issues a gentle sound and gradually increases the volume if the user does not respond. The generation AI monitors the user's reaction using voice recognition and motion detection and stops the alarm sound at the appropriate time. This mechanism allows the warning sounds emitted by home appliances to effectively communicate information without causing discomfort to users.As a result, the warning sound adjustment system allows the warning sounds emitted by home appliances to effectively communicate information without causing discomfort to users.

[0029] The warning sound adjustment system according to the embodiment includes a sensor unit, an analysis unit, an adjustment unit, and an output unit. The sensor unit is a component for detecting abnormalities. The sensor unit includes, for example, a temperature sensor, a humidity sensor, and a camera. The temperature sensor measures temperature using, for example, a thermistor, an RTD (resistance temperature detector), or a thermocouple. The humidity sensor measures humidity using, for example, a capacitance type, a resistance type, or a thermal conduction type. The camera detects abnormalities using, for example, a high-resolution camera, an infrared camera, or a wide-angle camera. The analysis unit analyzes data from the sensor unit and determines the type and urgency of the abnormality. The analysis unit analyzes the data using, for example, statistical analysis or a machine learning algorithm. The adjustment unit gradually adjusts the volume and type of sound of the warning sound based on the analysis results. The adjustment unit adjusts the volume in decibels (dB) and selects the type of sound, such as a beep, a chime, or an alarm. The output unit outputs the adjusted warning sound to a user. The output unit outputs the warning sound using a device such as a speaker, headphones, etc. As a result, the warning sound adjustment system according to the embodiment can effectively convey information without causing discomfort to the user when the warning sound emitted by the home appliance is used.

[0030] The sensor unit may include a temperature sensor, a humidity sensor, and a camera. The temperature sensor measures temperature using, for example, a thermistor, an RTD (resistance temperature detector), or a thermocouple. The humidity sensor measures humidity using, for example, a capacitance type, a resistance type, or a thermal conduction type. The camera detects abnormalities using, for example, a high-resolution camera, an infrared camera, or a wide-angle camera. This allows for more accurate detection of the type and urgency of the abnormality. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit may input data obtained from the temperature sensor, humidity sensor, or camera into the generation AI, and have the generation AI analyze the type and urgency of the abnormality.

[0031] The analysis unit can analyze data from the sensor unit and determine the type and urgency of the abnormality. The analysis unit analyzes the data using, for example, statistical analysis, machine learning algorithms, etc. For example, the analysis unit statistically analyzes temperature data from the sensor unit and determines the type and urgency of the abnormality. The analysis unit can also analyze humidity data from the sensor unit using machine learning algorithms and determine the type and urgency of the abnormality. Furthermore, the analysis unit can analyze video data from a camera using image analysis technology and determine the type and urgency of the abnormality. This allows for accurate determination of the type and urgency of the abnormality. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from the sensor unit to a generation AI and have the generation AI determine the type and urgency of the abnormality.

[0032] The adjustment unit can adjust the volume and type of the warning sound in a specific manner based on the analysis results. For example, the adjustment unit adjusts the volume in decibel (dB) units and selects the type of sound, such as a beep, chime, or alarm. For example, the adjustment unit adjusts the volume of the warning sound within a range of 10 dB to 50 dB based on the analysis results. The adjustment unit can also change the type of warning sound from a beep to a chime based on the analysis results. Furthermore, the adjustment unit can gradually increase the volume of the warning sound based on the analysis results. For example, the warning may be initially issued with a gentle sound, and if the user does not respond, the volume may be gradually increased. In this way, by gradually adjusting the warning sound, information can be conveyed without causing discomfort to the user. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the analysis results to a generation AI and cause the generation AI to adjust the volume and type of the warning sound.

[0033] The output unit can output the adjusted warning sound to the user. The output unit outputs the warning sound using a device such as a speaker or headphones. For example, the output unit outputs the adjusted warning sound from a speaker to notify the user. The output unit can also output the adjusted warning sound from headphones. Furthermore, the output unit can simultaneously output the adjusted warning sound from multiple devices. For example, the output unit simultaneously outputs the warning sound from both a speaker and headphones. In this way, information can be effectively conveyed by outputting the adjusted warning sound to the user. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit inputs data of the adjusted warning sound to a generation AI and causes the generation AI to output the warning sound.

[0034] The analysis unit can monitor the user's response using voice recognition or motion detection. The analysis unit can monitor the user's response using, for example, voice recognition technology. For example, the analysis unit can recognize the user's voice command and monitor the response. The analysis unit can also monitor the user's response using motion detection technology. For example, the analysis unit can detect the user's motion using an acceleration sensor or a gyro sensor and monitor the response. The analysis unit can also monitor the user's response by combining both voice recognition and motion detection. For example, the analysis unit can simultaneously monitor the user's voice command and motion and analyze the response. By monitoring the user's response, the warning sound can be stopped at an appropriate time. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's voice data and motion data into the generation AI and cause the generation AI to monitor the user's response.

[0035] The adjustment unit can stop the warning sound in a specific manner based on the user's reaction. The adjustment unit, for example, stops the warning sound based on the user's voice command. For example, the adjustment unit stops the warning sound when the user says "stop." The adjustment unit can also stop the warning sound based on the user's action. For example, the adjustment unit stops the warning sound when the user raises their hand. The adjustment unit can also stop the warning sound based on both the user's voice command and their action. For example, the adjustment unit stops the warning sound when the user raises their hand while saying "stop." In this way, by stopping the warning sound based on the user's reaction, information can be conveyed without causing discomfort to the user. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's voice data and action data into the generation AI and cause the generation AI to stop the warning sound.

[0036] The warning sound adjustment system further includes a sensor unit that, upon detection of an abnormality, dynamically changes the placement of sensors to identify details of the abnormality. For example, when an abnormality is detected, the sensor unit automatically adjusts the placement of the sensors to identify the source of the abnormality. For example, the sensor unit prioritizes placement of specific sensors depending on the type of abnormality to collect detailed data. The sensor unit can also cooperate with multiple sensors to acquire detailed information depending on the urgency of the abnormality. For example, when an abnormality is detected, the sensor unit automatically adjusts the placement of the sensors to identify the source of the abnormality. The sensor unit can prioritize placement of specific sensors depending on the type of abnormality to collect detailed data. Furthermore, the sensor unit can also cooperate with multiple sensors to acquire detailed information depending on the urgency of the abnormality. By dynamically changing the placement of the sensors, the details of the abnormality can be identified. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, when an abnormality is detected, the sensor unit inputs the placement of the sensors to a generation AI and causes the generation AI to identify the details of the abnormality.

[0037] Furthermore, the warning sound adjustment system includes a sensor unit that, when an abnormality is detected, integrates data from multiple sensors to improve the accuracy of the abnormality detection. The sensor unit, for example, integrates data from a temperature sensor and a humidity sensor to identify the cause of the abnormality. For example, the sensor unit integrates data from a temperature sensor and a humidity sensor to identify the cause of the abnormality. The sensor unit can also combine video data from a camera with sensor data to analyze the details of the abnormality. For example, the sensor unit combines video data from a camera with sensor data to analyze the details of the abnormality. Furthermore, the sensor unit can also integrate data from multiple sensors in real time to improve the accuracy of the abnormality detection. For example, the sensor unit integrates data from multiple sensors in real time to improve the accuracy of the abnormality detection. In this way, the accuracy of the abnormality detection can be improved by integrating data from multiple sensors. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input data from multiple sensors to a generation AI and cause the generation AI to improve the accuracy of the abnormality detection.

[0038] When an anomaly is detected, the sensor unit can transmit sensor data to the cloud to share it with other devices. For example, when an anomaly is detected, the sensor unit transmits sensor data to the cloud and shares it with other devices in real time. For example, when an anomaly is detected, the sensor unit transmits sensor data to the cloud and shares it with other devices in real time. The sensor unit can also transmit data to a specific device depending on the type of anomaly, enabling a rapid response. For example, the sensor unit transmits data to a specific device depending on the type of anomaly, enabling a rapid response. Furthermore, the sensor unit can transmit data to multiple devices depending on the urgency of the anomaly, allowing them to work together to address the anomaly. For example, the sensor unit transmits data to multiple devices depending on the urgency of the anomaly, allowing them to work together to address the anomaly. In this way, by transmitting the sensor data to the cloud, the data can be shared with other devices in real time. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, when an anomaly is detected, the sensor unit can input sensor data to a generation AI and have the generation AI transmit the data to the cloud.

[0039] When an anomaly is detected, the sensor unit can use sensor data to identify the location of the anomaly. For example, when an anomaly is detected, the sensor unit analyzes sensor data to identify the location of the anomaly. For example, when an anomaly is detected, the sensor unit analyzes sensor data to identify the location of the anomaly. The sensor unit can also prioritize use of data from a specific sensor depending on the type of anomaly to identify the location of the anomaly. For example, the sensor unit prioritizes use of data from a specific sensor depending on the type of anomaly to identify the location of the anomaly. Furthermore, the sensor unit can integrate data from multiple sensors depending on the urgency of the anomaly to identify the exact location of the anomaly. For example, the sensor unit integrates data from multiple sensors depending on the urgency of the anomaly to identify the exact location of the anomaly. In this way, the location of the anomaly can be identified by using the sensor data. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, when an anomaly is detected, the sensor unit can input sensor data to a generation AI and cause the generation AI to identify the location of the anomaly.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past abnormal data. For example, the analysis unit refers to past abnormal data and proposes a countermeasure in the event that a similar abnormality occurs. For example, the analysis unit refers to past abnormal data and proposes a countermeasure in the event that a similar abnormality occurs. The analysis unit can also analyze abnormality occurrence patterns based on past abnormal data to improve prediction accuracy. For example, the analysis unit analyzes abnormality occurrence patterns based on past abnormal data to improve prediction accuracy. Furthermore, the analysis unit can use past abnormal data to identify the cause of the abnormality and propose measures to prevent recurrence. For example, the analysis unit uses past abnormal data to identify the cause of the abnormality and propose measures to prevent recurrence. In this way, by referring to past abnormal data, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past abnormal data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0041] During analysis, the analysis unit can apply different analysis methods to each type of anomaly. For example, in the case of a temperature anomaly, the analysis unit prioritizes analyzing data from a temperature sensor to identify the cause of the anomaly. For example, in the case of a temperature anomaly, the analysis unit prioritizes analyzing data from a temperature sensor to identify the cause of the anomaly. In addition, in the case of a humidity anomaly, the analysis unit can also analyze the cause of the anomaly using data from a humidity sensor. For example, in the case of a humidity anomaly, the analysis unit can analyze the cause of the anomaly using data from a humidity sensor. Furthermore, in the case of an anomaly based on camera footage, the analysis unit can also identify details of the anomaly using image analysis technology. For example, in the case of an anomaly based on camera footage, the analysis unit uses image analysis technology to identify details of the anomaly. This allows for improved analysis accuracy by applying an appropriate analysis method to each type of anomaly. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis methods for each type of anomaly into the generation AI and cause the generation AI to execute the analysis to improve accuracy.

[0042] During analysis, the analysis unit can determine the analysis priority based on the frequency of anomalies. For example, if the frequency of anomalies is high, the analysis unit prioritizes analyzing that anomaly. For example, if the frequency of anomalies is high, the analysis unit prioritizes analyzing that anomaly. Furthermore, if the frequency of anomalies is low, the analysis unit can prioritize analyzing other anomalies. For example, if the frequency of anomalies is low, the analysis unit prioritizes analyzing other anomalies. Furthermore, the analysis unit can optimally allocate analysis resources based on the frequency of anomalies. For example, the analysis unit optimally allocates analysis resources based on the frequency of anomalies. This enables efficient analysis by determining the analysis priority based on the frequency of anomalies. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input anomaly frequency data to a generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the anomaly-related data. For example, the analysis unit refers to the anomaly-related data and identifies the cause of the anomaly. For example, the analysis unit refers to the anomaly-related data and identifies the cause of the anomaly. The analysis unit can also analyze the pattern of anomaly occurrence based on the anomaly-related data. For example, the analysis unit analyzes the pattern of anomaly occurrence based on the anomaly-related data. Furthermore, the analysis unit can also use the anomaly-related data to propose measures to prevent the anomaly from recurring. For example, the analysis unit uses the anomaly-related data to propose measures to prevent the anomaly from recurring. In this way, by referring to the anomaly-related data, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the anomaly-related data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During adjustment, the adjustment unit can finely set the level of the warning sound based on the urgency of the abnormality. For example, when the urgency of the abnormality is high, the adjustment unit quickly increases the volume of the warning sound. For example, when the urgency of the abnormality is high, the adjustment unit quickly increases the volume of the warning sound. Furthermore, when the urgency of the abnormality is low, the adjustment unit can soften the volume of the warning sound and gradually increase the volume. For example, when the urgency of the abnormality is low, the adjustment unit soften the volume of the warning sound and gradually increase the volume. Furthermore, the adjustment unit can finely set the level of the warning sound based on the urgency of the abnormality and adjust the volume at an appropriate time. For example, the adjustment unit finely sets the level of the warning sound based on the urgency of the abnormality and adjusts the volume at an appropriate time. In this way, by finely setting the level of the warning sound based on the urgency of the abnormality, the warning sound can be adjusted at an appropriate time. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input urgency data of the abnormality to the generation AI and cause the generation AI to set the level of the warning sound.

[0045] During adjustment, the adjustment unit can set different warning sounds depending on the type of abnormality. For example, in the case of a temperature abnormality, the adjustment unit sets a specific warning sound and notifies the user. For example, in the case of a temperature abnormality, the adjustment unit sets a specific warning sound and notifies the user. The adjustment unit can also set a different warning sound in the case of a humidity abnormality and notify the user. For example, the adjustment unit sets a different warning sound in the case of a humidity abnormality and notifies the user. Furthermore, the adjustment unit can also set a specific warning sound in the case of an abnormality based on camera footage and notify the user. For example, the adjustment unit sets a specific warning sound in the case of an abnormality based on camera footage and notifies the user. In this way, by setting an appropriate warning sound depending on the type of abnormality, information can be effectively communicated to the user. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input abnormality type data to the generation AI and cause the generation AI to set the warning sound.

[0046] During adjustment, the adjustment unit can select multiple output devices for the warning sound and output them simultaneously. For example, when an abnormality is detected, the adjustment unit simultaneously outputs a warning sound from multiple speakers. For example, when an abnormality is detected, the adjustment unit simultaneously outputs a warning sound from multiple speakers. The adjustment unit can also output a warning sound from a specific device depending on the type of abnormality. For example, the adjustment unit outputs a warning sound from a specific device depending on the type of abnormality. Furthermore, the adjustment unit can simultaneously output a warning sound from multiple devices to attract attention depending on the urgency of the abnormality. For example, the adjustment unit simultaneously outputs a warning sound from multiple devices depending on the urgency of the abnormality to attract attention. In this way, by selecting multiple output devices and outputting the warning sound simultaneously, information can be effectively conveyed to the user. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input the selection of an output device for the warning sound to the generation AI and cause the generation AI to simultaneously output the warning sound from multiple devices.

[0047] During adjustment, the adjustment unit can adjust the output timing of the warning sound according to the occurrence of the abnormality. For example, the adjustment unit outputs a warning sound immediately after the occurrence of the abnormality to promptly notify the user. For example, the adjustment unit outputs a warning sound immediately after the occurrence of the abnormality to promptly notify the user. The adjustment unit can also adjust the output timing of the warning sound according to the occurrence of the abnormality and notify the user at an appropriate timing. For example, the adjustment unit adjusts the output timing of the warning sound according to the occurrence of the abnormality and notifies the user at an appropriate timing. Furthermore, the adjustment unit can fine-tune the output timing of the warning sound according to the urgency of the abnormality and adjust the volume at an appropriate timing. For example, the adjustment unit fine-tunes the output timing of the warning sound according to the urgency of the abnormality and adjusts the volume at an appropriate timing. In this way, the output timing of the warning sound can be adjusted according to the occurrence of the abnormality, thereby providing the warning sound at an appropriate timing. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input abnormality occurrence status data to the generation AI and cause the generation AI to adjust the output timing of the warning sound.

[0048] The output unit can adjust the volume of the warning sound based on the user's hearing characteristics when outputting the warning sound. The output unit, for example, outputs the warning sound at an appropriate volume based on the user's hearing characteristics. For example, the output unit outputs the warning sound at an appropriate volume based on the user's hearing characteristics. The output unit can also output the warning sound by emphasizing a specific frequency band in consideration of the user's hearing characteristics. For example, the output unit outputs the warning sound by emphasizing a specific frequency band in consideration of the user's hearing characteristics. Furthermore, the output unit can automatically adjust the volume in accordance with the user's hearing characteristics to provide an optimal warning sound. For example, the output unit automatically adjusts the volume in accordance with the user's hearing characteristics to provide an optimal warning sound. In this way, the optimal warning sound can be provided by adjusting the volume of the warning sound based on the user's hearing characteristics. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's hearing characteristic data to a generation AI and cause the generation AI to adjust the volume of the warning sound.

[0049] The output unit can customize the type of warning sound according to the user's preferences when outputting the warning sound. The output unit customizes the warning sound based on, for example, the user's preferred music genre. For example, the output unit customizes the warning sound based on the user's preferred music genre. The output unit can also provide an optimal warning sound by referring to the type of warning sound previously selected by the user. For example, the output unit provides an optimal warning sound by referring to the type of warning sound previously selected by the user. Furthermore, the output unit can also customize the tone and rhythm of the warning sound according to the user's preferences. For example, the output unit customizes the tone and rhythm of the warning sound according to the user's preferences. In this way, by customizing the type of warning sound according to the user's preferences, a more effective warning sound can be provided. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input user preference data to a generation AI and cause the generation AI to customize the warning sound.

[0050] The output unit can output the audible warning in conjunction with a visual warning. For example, the output unit displays the visual warning on a display simultaneously with the audible warning. For example, the output unit displays the visual warning on a display simultaneously with the audible warning. The output unit can also display different visual warnings depending on the type of audible warning. For example, the output unit displays different visual warnings depending on the type of audible warning. Furthermore, the output unit can adjust the intensity of the visual warning depending on the urgency of the audible warning. For example, the output unit adjusts the intensity of the visual warning depending on the urgency of the audible warning. In this way, by linking the audible warning and the visual warning, information can be effectively conveyed to the user. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input data of the audible warning and the visual warning to a generation AI and cause the generation AI to execute the linked output.

[0051] The output unit can output an alarm sound in cooperation with other home appliances when outputting. For example, when an abnormality is detected, the output unit outputs an alarm sound in cooperation with other home appliances. For example, when an abnormality is detected, the output unit outputs an alarm sound in cooperation with other home appliances. The output unit can also output an alarm sound in cooperation with a specific home appliance depending on the type of abnormality. For example, the output unit outputs an alarm sound in cooperation with a specific home appliance depending on the type of abnormality. Furthermore, the output unit can output an alarm sound in cooperation with multiple home appliances to attract attention depending on the urgency of the abnormality. For example, the output unit outputs an alarm sound in cooperation with multiple home appliances depending on the urgency of the abnormality to attract attention. In this way, by outputting an alarm sound in cooperation with other home appliances, information can be effectively conveyed to the user. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input coordination data with other home appliances to a generation AI and cause the generation AI to execute the coordinated output.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The warning sound adjustment system may further include a location information acquisition unit that acquires user location information and adjusts the output location of the warning sound. For example, the location information acquisition unit may identify the user's current location using GPS or Wi-Fi signals. The location information acquisition unit may also detect which room in the house the user is in and output the warning sound from the speaker closest to that room. Furthermore, if the user is moving, the location information acquisition unit may dynamically change the output location of the warning sound in accordance with the user's movement. This allows the user to receive the warning sound from an appropriate location no matter where they are.

[0054] The warning sound adjustment system may further include a display unit that displays different warning messages depending on the type of abnormality. For example, the display unit may display "Temperature is too high" in the case of a temperature abnormality, and "Humidity is too high" in the case of a humidity abnormality. The display unit may also change the color and font size of the message depending on the urgency of the abnormality. Furthermore, the display unit may detect whether the user has acknowledged the message and continue to display the message until the user acknowledges it. This allows the user to be effectively notified of the abnormality through a visual warning message.

[0055] The warning sound adjustment system can further include a history management unit that records the history of abnormalities and notifies the user. For example, the history management unit records the type and date and time of past abnormalities so that the user can check them at any time. The history management unit can also analyze the frequency and pattern of abnormalities and suggest preventive measures. Furthermore, if an abnormality recurs, the history management unit can support a quick response by referring to past countermeasures. In this way, by managing the history of abnormalities, the user can take appropriate measures.

[0056] The warning sound adjustment system may further include a communication unit that automatically sends a notification to an emergency contact when an abnormality occurs. For example, when an abnormality is detected, the communication unit sends a notification by SMS or email to a pre-registered emergency contact. The communication unit may also customize the content of the notification depending on the type and urgency of the abnormality. Furthermore, the communication unit may track whether the user has acknowledged the notification and send the notification again if the user has not acknowledged it. This provides a means of communication for prompt response when an abnormality occurs.

[0057] The warning sound adjustment system can further include a control unit that automatically stops the operation of home appliances when an abnormality occurs. For example, the control unit automatically turns off the power of the corresponding home appliance when an abnormality is detected. The control unit can also select home appliances to stop depending on the type of abnormality and the urgency of the abnormality. Furthermore, the control unit can continue to stop the operation of the home appliance until the user manually restarts the home appliance. This makes it possible to ensure safety by automatically controlling the operation of home appliances when an abnormality occurs.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The sensor unit is a component for detecting abnormalities. The sensor unit includes, for example, a temperature sensor, a humidity sensor, a camera, etc. The temperature sensor measures temperature using, for example, a thermistor, an RTD (resistance temperature detector), a thermocouple, etc. The humidity sensor measures humidity using, for example, a capacitance type, a resistance type, or a thermal conduction type. The camera detects abnormalities using, for example, a high-resolution camera, an infrared camera, a wide-angle camera, etc. Step 2: The analysis unit analyzes the data from the sensor unit and determines the type of abnormality and the level of urgency. The analysis unit analyzes the data using, for example, statistical analysis, machine learning algorithms, etc. Step 3: The adjustment unit adjusts the volume and type of the warning sound in stages based on the analysis results. For example, the adjustment unit adjusts the volume in decibels (dB) and selects the type of sound, such as a beep, chime, or alarm. Step 4: The output unit outputs the adjusted warning sound to the user. The output unit outputs the warning sound using a device such as a speaker or headphones.

[0060] (Example 2) An alarm sound adjustment system according to an embodiment of the present invention uses a generation AI to assess the situation and gradually increase the intensity of the alarm sound to prevent the alarm sound emitted by a home appliance from annoying the user. When the home appliance detects an abnormality, the system uses sensors and cameras to analyze the type and urgency of the abnormality. Next, the generation AI gradually adjusts the volume and type of the alarm sound based on the analysis results. The alarm initially issues a gentle sound and gradually increases the volume if the user does not respond. Furthermore, the generation AI monitors the user's reaction using voice recognition and motion detection and stops the alarm sound at the appropriate time. This mechanism allows the alarm sound emitted by the home appliance to effectively communicate information without annoying the user. For example, when a home appliance detects an abnormality, the system uses temperature sensors, humidity sensors, and cameras to analyze the type and urgency of the abnormality. The generation AI gradually adjusts the volume and type of the alarm sound based on the analysis results. The alarm initially issues a gentle sound and gradually increases the volume if the user does not respond. The generation AI monitors the user's reaction using voice recognition and motion detection and stops the alarm sound at the appropriate time. This mechanism allows the warning sounds emitted by home appliances to effectively communicate information without causing discomfort to users.As a result, the warning sound adjustment system allows the warning sounds emitted by home appliances to effectively communicate information without causing discomfort to users.

[0061] The warning sound adjustment system according to the embodiment includes a sensor unit, an analysis unit, an adjustment unit, and an output unit. The sensor unit is a component for detecting abnormalities. The sensor unit includes, for example, a temperature sensor, a humidity sensor, and a camera. The temperature sensor measures temperature using, for example, a thermistor, an RTD (resistance temperature detector), or a thermocouple. The humidity sensor measures humidity using, for example, a capacitance type, a resistance type, or a thermal conduction type. The camera detects abnormalities using, for example, a high-resolution camera, an infrared camera, or a wide-angle camera. The analysis unit analyzes data from the sensor unit and determines the type and urgency of the abnormality. The analysis unit analyzes the data using, for example, statistical analysis or a machine learning algorithm. The adjustment unit gradually adjusts the volume and type of sound of the warning sound based on the analysis results. The adjustment unit adjusts the volume in decibels (dB) and selects the type of sound, such as a beep, a chime, or an alarm. The output unit outputs the adjusted warning sound to a user. The output unit outputs the warning sound using a device such as a speaker, headphones, etc. As a result, the warning sound adjustment system according to the embodiment can effectively convey information without causing discomfort to the user when the warning sound emitted by the home appliance is used.

[0062] The sensor unit may include a temperature sensor, a humidity sensor, and a camera. The temperature sensor measures temperature using, for example, a thermistor, an RTD (resistance temperature detector), or a thermocouple. The humidity sensor measures humidity using, for example, a capacitance type, a resistance type, or a thermal conduction type. The camera detects abnormalities using, for example, a high-resolution camera, an infrared camera, or a wide-angle camera. This allows for more accurate detection of the type and urgency of the abnormality. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit may input data obtained from the temperature sensor, humidity sensor, or camera into the generation AI, and have the generation AI analyze the type and urgency of the abnormality.

[0063] The analysis unit can analyze data from the sensor unit and determine the type and urgency of the abnormality. The analysis unit analyzes the data using, for example, statistical analysis, machine learning algorithms, etc. For example, the analysis unit statistically analyzes temperature data from the sensor unit and determines the type and urgency of the abnormality. The analysis unit can also analyze humidity data from the sensor unit using machine learning algorithms and determine the type and urgency of the abnormality. Furthermore, the analysis unit can analyze video data from a camera using image analysis technology and determine the type and urgency of the abnormality. This allows for accurate determination of the type and urgency of the abnormality. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from the sensor unit to a generation AI and have the generation AI determine the type and urgency of the abnormality.

[0064] The adjustment unit can adjust the volume and type of the warning sound in a specific manner based on the analysis results. For example, the adjustment unit adjusts the volume in decibel (dB) units and selects the type of sound, such as a beep, chime, or alarm. For example, the adjustment unit adjusts the volume of the warning sound within a range of 10 dB to 50 dB based on the analysis results. The adjustment unit can also change the type of warning sound from a beep to a chime based on the analysis results. Furthermore, the adjustment unit can gradually increase the volume of the warning sound based on the analysis results. For example, the warning may be initially issued with a gentle sound, and if the user does not respond, the volume may be gradually increased. In this way, by gradually adjusting the warning sound, information can be conveyed without causing discomfort to the user. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the analysis results to a generation AI and cause the generation AI to adjust the volume and type of the warning sound.

[0065] The output unit can output the adjusted warning sound to the user. The output unit outputs the warning sound using a device such as a speaker or headphones. For example, the output unit outputs the adjusted warning sound from a speaker to notify the user. The output unit can also output the adjusted warning sound from headphones. Furthermore, the output unit can simultaneously output the adjusted warning sound from multiple devices. For example, the output unit simultaneously outputs the warning sound from both a speaker and headphones. In this way, information can be effectively conveyed by outputting the adjusted warning sound to the user. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit inputs data of the adjusted warning sound to a generation AI and causes the generation AI to output the warning sound.

[0066] The analysis unit can monitor the user's response using voice recognition or motion detection. The analysis unit can monitor the user's response using, for example, voice recognition technology. For example, the analysis unit can recognize the user's voice command and monitor the response. The analysis unit can also monitor the user's response using motion detection technology. For example, the analysis unit can detect the user's motion using an acceleration sensor or a gyro sensor and monitor the response. The analysis unit can also monitor the user's response by combining both voice recognition and motion detection. For example, the analysis unit can simultaneously monitor the user's voice command and motion and analyze the response. By monitoring the user's response, the warning sound can be stopped at an appropriate time. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's voice data and motion data into the generation AI and cause the generation AI to monitor the user's response.

[0067] The adjustment unit can stop the warning sound in a specific manner based on the user's reaction. The adjustment unit, for example, stops the warning sound based on the user's voice command. For example, the adjustment unit stops the warning sound when the user says "stop." The adjustment unit can also stop the warning sound based on the user's action. For example, the adjustment unit stops the warning sound when the user raises their hand. The adjustment unit can also stop the warning sound based on both the user's voice command and their action. For example, the adjustment unit stops the warning sound when the user raises their hand while saying "stop." In this way, by stopping the warning sound based on the user's reaction, information can be conveyed without causing discomfort to the user. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's voice data and action data into the generation AI and cause the generation AI to stop the warning sound.

[0068] The warning sound adjustment system further includes a sensor unit that estimates a user's emotion and adjusts the sensitivity of the sensor based on the estimated user's emotion. The sensor unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the sensor unit may analyze the user's facial expression captured by a camera to estimate the emotion. The sensor unit may also estimate the user's emotion using voice analysis technology. For example, the sensor unit may analyze the tone and speed of the user's voice to estimate the emotion. The sensor unit may also adjust the sensitivity of the sensor based on the user's emotion. For example, if the user is stressed, the sensor sensitivity may be set low to avoid excessive warnings. If the user is relaxed, the sensor sensitivity may be set high to detect even minor abnormalities. If the user is in a hurry, the sensor sensitivity may be set medium to detect only important abnormalities. In this way, excessive warnings can be avoided by adjusting the sensitivity of the sensor according to the user's emotion. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without AI. For example, the sensor unit can input the user's emotional data into the generation AI and have the generation AI adjust the sensitivity of the sensor.

[0069] The warning sound adjustment system further includes a sensor unit that, upon detection of an abnormality, dynamically changes the placement of sensors to identify details of the abnormality. For example, when an abnormality is detected, the sensor unit automatically adjusts the placement of the sensors to identify the source of the abnormality. For example, the sensor unit prioritizes placement of specific sensors depending on the type of abnormality to collect detailed data. The sensor unit can also cooperate with multiple sensors to acquire detailed information depending on the urgency of the abnormality. For example, when an abnormality is detected, the sensor unit automatically adjusts the placement of the sensors to identify the source of the abnormality. The sensor unit can prioritize placement of specific sensors depending on the type of abnormality to collect detailed data. Furthermore, the sensor unit can also cooperate with multiple sensors to acquire detailed information depending on the urgency of the abnormality. By dynamically changing the placement of the sensors, the details of the abnormality can be identified. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, when an abnormality is detected, the sensor unit inputs the placement of the sensors to a generation AI and causes the generation AI to identify the details of the abnormality.

[0070] Furthermore, the warning sound adjustment system includes a sensor unit that, when an abnormality is detected, integrates data from multiple sensors to improve the accuracy of the abnormality detection. The sensor unit, for example, integrates data from a temperature sensor and a humidity sensor to identify the cause of the abnormality. For example, the sensor unit integrates data from a temperature sensor and a humidity sensor to identify the cause of the abnormality. The sensor unit can also combine video data from a camera with sensor data to analyze the details of the abnormality. For example, the sensor unit combines video data from a camera with sensor data to analyze the details of the abnormality. Furthermore, the sensor unit can also integrate data from multiple sensors in real time to improve the accuracy of the abnormality detection. For example, the sensor unit integrates data from multiple sensors in real time to improve the accuracy of the abnormality detection. In this way, the accuracy of the abnormality detection can be improved by integrating data from multiple sensors. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input data from multiple sensors to a generation AI and cause the generation AI to improve the accuracy of the abnormality detection.

[0071] The warning sound adjustment system further includes a sensor unit that estimates a user's emotion and selects a type of sensor based on the estimated user's emotion. The sensor unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the sensor unit analyzes the user's facial expression captured by a camera to estimate the emotion. The sensor unit can also estimate the user's emotion using voice analysis technology. For example, the sensor unit analyzes the tone and speed of the user's voice to estimate the emotion. The sensor unit can also select a type of sensor based on the user's emotion. For example, if the user is stressed, the sensor unit can prioritize the use of a voice sensor to maintain a quiet environment. If the user is relaxed, the sensor unit can use a temperature sensor or humidity sensor to maintain a comfortable environment. If the user is in a hurry, the sensor unit can use a camera sensor to quickly detect an abnormality. This allows for appropriate abnormality detection by selecting a type of sensor based on the user's emotion. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without AI. For example, the sensor unit can input the user's emotional data into the generation AI and have the generation AI select the type of sensor.

[0072] When an anomaly is detected, the sensor unit can transmit sensor data to the cloud to share it with other devices. For example, when an anomaly is detected, the sensor unit transmits sensor data to the cloud and shares it with other devices in real time. For example, when an anomaly is detected, the sensor unit transmits sensor data to the cloud and shares it with other devices in real time. The sensor unit can also transmit data to a specific device depending on the type of anomaly, enabling a rapid response. For example, the sensor unit transmits data to a specific device depending on the type of anomaly, enabling a rapid response. Furthermore, the sensor unit can transmit data to multiple devices depending on the urgency of the anomaly, allowing them to work together to address the anomaly. For example, the sensor unit transmits data to multiple devices depending on the urgency of the anomaly, allowing them to work together to address the anomaly. In this way, by transmitting the sensor data to the cloud, the data can be shared with other devices in real time. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, when an anomaly is detected, the sensor unit can input sensor data to a generation AI and have the generation AI transmit the data to the cloud.

[0073] When an anomaly is detected, the sensor unit can identify the location of the anomaly using sensor data. For example, when an anomaly is detected, the sensor unit analyzes the sensor data and identifies the location of the anomaly. For example, when an anomaly is detected, the sensor unit analyzes the sensor data and identifies the location of the anomaly. The sensor unit can also prioritize the use of data from a specific sensor depending on the type of anomaly to identify the location of the anomaly. For example, the sensor unit prioritizes the use of data from a specific sensor depending on the type of anomaly to identify the location of the anomaly. Furthermore, the sensor unit can integrate data from multiple sensors depending on the urgency of the anomaly to identify the exact location of the anomaly. For example, the sensor unit integrates data from multiple sensors depending on the urgency of the anomaly to identify the exact location of the anomaly. In this way, the location of the anomaly can be identified by using the sensor data. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, when an anomaly is detected, the sensor unit can input sensor data to a generation AI and cause the generation AI to identify the location of the anomaly.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can analyze the user's facial expressions captured by a camera to estimate emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate emotions. Furthermore, the analysis unit can adjust the analysis algorithm based on the user's emotions. For example, if the user is stressed, the analysis algorithm can be simplified to provide quick results. Alternatively, if the user is relaxed, a detailed analysis can be performed to provide highly accurate results. Furthermore, if the user is in a hurry, an algorithm can be used that prioritizes analysis of only important anomalies. This enables fast and accurate analysis by adjusting the analysis algorithm according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into a generation AI and have the generation AI adjust the analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past abnormal data. For example, the analysis unit refers to past abnormal data and proposes a countermeasure in the event that a similar abnormality occurs. For example, the analysis unit refers to past abnormal data and proposes a countermeasure in the event that a similar abnormality occurs. The analysis unit can also analyze abnormality occurrence patterns based on past abnormal data to improve prediction accuracy. For example, the analysis unit analyzes abnormality occurrence patterns based on past abnormal data to improve prediction accuracy. Furthermore, the analysis unit can use past abnormal data to identify the cause of the abnormality and propose measures to prevent recurrence. For example, the analysis unit uses past abnormal data to identify the cause of the abnormality and propose measures to prevent recurrence. In this way, by referring to past abnormal data, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past abnormal data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0076] During analysis, the analysis unit can apply different analysis methods to each type of anomaly. For example, in the case of a temperature anomaly, the analysis unit prioritizes analyzing data from a temperature sensor to identify the cause of the anomaly. For example, in the case of a temperature anomaly, the analysis unit prioritizes analyzing data from a temperature sensor to identify the cause of the anomaly. In addition, in the case of a humidity anomaly, the analysis unit can also analyze the cause of the anomaly using data from a humidity sensor. For example, in the case of a humidity anomaly, the analysis unit can analyze the cause of the anomaly using data from a humidity sensor. Furthermore, in the case of an anomaly based on camera footage, the analysis unit can also identify details of the anomaly using image analysis technology. For example, in the case of an anomaly based on camera footage, the analysis unit uses image analysis technology to identify details of the anomaly. This allows for improved analysis accuracy by applying an appropriate analysis method to each type of anomaly. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis methods for each type of anomaly into the generation AI and cause the generation AI to execute the analysis to improve accuracy.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can analyze the user's facial expressions captured with a camera to estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit can adjust the display method of the analysis results based on the user's emotions. For example, if the user is stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the analysis results to be adjusted according to the user's emotions, enabling a highly visible display. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into a generation AI and have the generation AI adjust the display method of the analysis results.

[0078] During analysis, the analysis unit can determine the analysis priority based on the frequency of anomalies. For example, if the frequency of anomalies is high, the analysis unit prioritizes analyzing that anomaly. For example, if the frequency of anomalies is high, the analysis unit prioritizes analyzing that anomaly. Furthermore, if the frequency of anomalies is low, the analysis unit can prioritize analyzing other anomalies. For example, if the frequency of anomalies is low, the analysis unit prioritizes analyzing other anomalies. Furthermore, the analysis unit can optimally allocate analysis resources based on the frequency of anomalies. For example, the analysis unit optimally allocates analysis resources based on the frequency of anomalies. This enables efficient analysis by determining the analysis priority based on the frequency of anomalies. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input anomaly frequency data to a generation AI and have the generation AI determine the analysis priority.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the anomaly-related data. For example, the analysis unit refers to the anomaly-related data and identifies the cause of the anomaly. For example, the analysis unit refers to the anomaly-related data and identifies the cause of the anomaly. The analysis unit can also analyze the pattern of anomaly occurrence based on the anomaly-related data. For example, the analysis unit analyzes the pattern of anomaly occurrence based on the anomaly-related data. Furthermore, the analysis unit can also use the anomaly-related data to propose measures to prevent the anomaly from recurring. For example, the analysis unit uses the anomaly-related data to propose measures to prevent the anomaly from recurring. In this way, by referring to the anomaly-related data, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the anomaly-related data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The warning sound adjustment system further includes an adjustment unit that estimates a user's emotion and changes the warning sound adjustment method based on the estimated user's emotion. The adjustment unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the adjustment unit analyzes the user's facial expression captured by a camera to estimate the emotion. The adjustment unit can also estimate the user's emotion using voice analysis technology. For example, the adjustment unit analyzes the tone and speed of the user's voice to estimate the emotion. The adjustment unit can also change the warning sound adjustment method based on the user's emotion. For example, if the user is stressed, the warning sound can be made softer and the volume can be gradually increased. Alternatively, if the user is relaxed, the warning sound can be made softer and the volume can be adjusted as needed. Furthermore, if the user is in a hurry, the warning sound can be quickly increased in volume to attract the user's attention. As a result, by changing the warning sound adjustment method according to the user's emotion, a more appropriate warning sound can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input user emotion data into the generation AI and cause the generation AI to change the method of adjusting the warning sound.

[0081] During adjustment, the adjustment unit can finely set the level of the warning sound based on the urgency of the abnormality. For example, when the urgency of the abnormality is high, the adjustment unit quickly increases the volume of the warning sound. For example, when the urgency of the abnormality is high, the adjustment unit quickly increases the volume of the warning sound. Furthermore, when the urgency of the abnormality is low, the adjustment unit can soften the volume of the warning sound and gradually increase the volume. For example, when the urgency of the abnormality is low, the adjustment unit soften the volume of the warning sound and gradually increase the volume. Furthermore, the adjustment unit can finely set the level of the warning sound based on the urgency of the abnormality and adjust the volume at an appropriate time. For example, the adjustment unit finely sets the level of the warning sound based on the urgency of the abnormality and adjusts the volume at an appropriate time. In this way, by finely setting the level of the warning sound based on the urgency of the abnormality, the warning sound can be adjusted at an appropriate time. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input urgency data of the abnormality to the generation AI and cause the generation AI to set the level of the warning sound.

[0082] During adjustment, the adjustment unit can set different warning sounds depending on the type of abnormality. For example, in the case of a temperature abnormality, the adjustment unit sets a specific warning sound and notifies the user. For example, in the case of a temperature abnormality, the adjustment unit sets a specific warning sound and notifies the user. The adjustment unit can also set a different warning sound in the case of a humidity abnormality and notify the user. For example, the adjustment unit sets a different warning sound in the case of a humidity abnormality and notifies the user. Furthermore, the adjustment unit can also set a specific warning sound in the case of an abnormality based on camera footage and notify the user. For example, the adjustment unit sets a specific warning sound in the case of an abnormality based on camera footage and notifies the user. In this way, by setting an appropriate warning sound depending on the type of abnormality, information can be effectively communicated to the user. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input abnormality type data to the generation AI and cause the generation AI to set the warning sound.

[0083] The warning sound adjustment system further includes an adjustment unit that estimates a user's emotion and changes the tone of the warning sound based on the estimated user's emotion. The adjustment unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the adjustment unit analyzes the user's facial expression captured by a camera to estimate the emotion. The adjustment unit can also estimate the user's emotion using voice analysis technology. For example, the adjustment unit analyzes the tone and speed of the user's voice to estimate the emotion. The adjustment unit can also change the tone of the warning sound based on the user's emotion. For example, if the user is stressed, the adjustment unit can set the warning sound to a calm tone. If the user is relaxed, the adjustment unit can set the warning sound to a bright tone. If the user is in a hurry, the adjustment unit can set the warning sound to an attention-grabbing tone. This allows the adjustment unit to provide a more appropriate warning sound by changing the tone of the warning sound according to the user's emotion. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input user emotion data into a generation AI and cause the generation AI to change the tone of the warning sound.

[0084] During adjustment, the adjustment unit can select multiple output devices for the warning sound and output them simultaneously. For example, when an abnormality is detected, the adjustment unit simultaneously outputs a warning sound from multiple speakers. For example, when an abnormality is detected, the adjustment unit simultaneously outputs a warning sound from multiple speakers. The adjustment unit can also output a warning sound from a specific device depending on the type of abnormality. For example, the adjustment unit outputs a warning sound from a specific device depending on the type of abnormality. Furthermore, the adjustment unit can simultaneously output a warning sound from multiple devices to attract attention depending on the urgency of the abnormality. For example, the adjustment unit simultaneously outputs a warning sound from multiple devices depending on the urgency of the abnormality to attract attention. In this way, by selecting multiple output devices and outputting the warning sound simultaneously, information can be effectively conveyed to the user. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input the selection of an output device for the warning sound to the generation AI and cause the generation AI to simultaneously output the warning sound from multiple devices.

[0085] During adjustment, the adjustment unit can adjust the output timing of the warning sound according to the occurrence of the abnormality. For example, the adjustment unit outputs a warning sound immediately after the occurrence of the abnormality to promptly notify the user. For example, the adjustment unit outputs a warning sound immediately after the occurrence of the abnormality to promptly notify the user. The adjustment unit can also adjust the output timing of the warning sound according to the occurrence of the abnormality and notify the user at an appropriate timing. For example, the adjustment unit adjusts the output timing of the warning sound according to the occurrence of the abnormality and notifies the user at an appropriate timing. Furthermore, the adjustment unit can fine-tune the output timing of the warning sound according to the urgency of the abnormality and adjust the volume at an appropriate timing. For example, the adjustment unit fine-tunes the output timing of the warning sound according to the urgency of the abnormality and adjusts the volume at an appropriate timing. In this way, the output timing of the warning sound can be adjusted according to the occurrence of the abnormality, thereby providing the warning sound at an appropriate timing. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input abnormality occurrence status data to the generation AI and cause the generation AI to adjust the output timing of the warning sound.

[0086] The warning sound adjustment system further includes an output unit that estimates a user's emotion and adjusts the output method of the warning sound based on the estimated user's emotion. The output unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the output unit analyzes the user's facial expression captured by a camera to estimate the emotion. The output unit can also estimate the user's emotion using voice analysis technology. For example, the output unit analyzes the tone and speed of the user's voice to estimate the emotion. The output unit can also adjust the output method of the warning sound based on the user's emotion. For example, if the user is stressed, the warning sound can be output at a gentle volume. If the user is relaxed, the warning sound can be output at a bright volume. If the user is in a hurry, the warning sound can be output quickly at a loud volume. This allows for a more appropriate warning sound to be provided by adjusting the output method of the warning sound according to the user's emotion. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or without AI. For example, the output unit may input user emotion data to a generation AI and cause the generation AI to adjust the output method of the warning sound.

[0087] The output unit can adjust the volume of the warning sound based on the user's hearing characteristics when outputting the warning sound. The output unit, for example, outputs the warning sound at an appropriate volume based on the user's hearing characteristics. For example, the output unit outputs the warning sound at an appropriate volume based on the user's hearing characteristics. The output unit can also output the warning sound by emphasizing a specific frequency band in consideration of the user's hearing characteristics. For example, the output unit outputs the warning sound by emphasizing a specific frequency band in consideration of the user's hearing characteristics. Furthermore, the output unit can automatically adjust the volume in accordance with the user's hearing characteristics to provide an optimal warning sound. For example, the output unit automatically adjusts the volume in accordance with the user's hearing characteristics to provide an optimal warning sound. In this way, the optimal warning sound can be provided by adjusting the volume of the warning sound based on the user's hearing characteristics. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's hearing characteristic data to a generation AI and cause the generation AI to adjust the volume of the warning sound.

[0088] The output unit can customize the type of warning sound according to the user's preferences when outputting the warning sound. The output unit customizes the warning sound based on, for example, the user's preferred music genre. For example, the output unit customizes the warning sound based on the user's preferred music genre. The output unit can also provide an optimal warning sound by referring to the type of warning sound previously selected by the user. For example, the output unit provides an optimal warning sound by referring to the type of warning sound previously selected by the user. Furthermore, the output unit can also customize the tone and rhythm of the warning sound according to the user's preferences. For example, the output unit customizes the tone and rhythm of the warning sound according to the user's preferences. In this way, by customizing the type of warning sound according to the user's preferences, a more effective warning sound can be provided. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input user preference data to a generation AI and cause the generation AI to customize the warning sound.

[0089] The warning sound adjustment system further includes an output unit that estimates a user's emotion and determines the output order of warning sounds based on the estimated user's emotion. The output unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the output unit analyzes the user's facial expression captured by a camera to estimate the emotion. The output unit can also estimate the user's emotion using voice analysis technology. For example, the output unit analyzes the tone and speed of the user's voice to estimate the emotion. The output unit can also determine the output order of warning sounds based on the user's emotion. For example, if the user is stressed, the output order can be gentle warning sounds. Alternatively, if the user is relaxed, the output order can be bright warning sounds. Alternatively, if the user is in a hurry, the output order can be quick and important warning sounds. This allows the output order of warning sounds to be determined according to the user's emotion, thereby providing more appropriate warning sounds. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or without AI. For example, the output unit may input user emotion data to a generation AI and have the generation AI determine the output order of warning sounds.

[0090] The output unit can output the audible warning in conjunction with a visual warning. For example, the output unit displays the visual warning on a display simultaneously with the audible warning. For example, the output unit displays the visual warning on a display simultaneously with the audible warning. The output unit can also display different visual warnings depending on the type of audible warning. For example, the output unit displays different visual warnings depending on the type of audible warning. Furthermore, the output unit can adjust the intensity of the visual warning depending on the urgency of the audible warning. For example, the output unit adjusts the intensity of the visual warning depending on the urgency of the audible warning. In this way, by linking the audible warning and the visual warning, information can be effectively conveyed to the user. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input data of the audible warning and the visual warning to a generation AI and cause the generation AI to execute the linked output.

[0091] The output unit can output an alarm sound in cooperation with other home appliances when outputting. For example, when an abnormality is detected, the output unit outputs an alarm sound in cooperation with other home appliances. For example, when an abnormality is detected, the output unit outputs an alarm sound in cooperation with other home appliances. The output unit can also output an alarm sound in cooperation with a specific home appliance depending on the type of abnormality. For example, the output unit outputs an alarm sound in cooperation with a specific home appliance depending on the type of abnormality. Furthermore, the output unit can output an alarm sound in cooperation with multiple home appliances to attract attention depending on the urgency of the abnormality. For example, the output unit outputs an alarm sound in cooperation with multiple home appliances depending on the urgency of the abnormality to attract attention. In this way, by outputting an alarm sound in cooperation with other home appliances, information can be effectively conveyed to the user. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input coordination data with other home appliances to a generation AI and cause the generation AI to execute the coordinated output. === Hard Collateral 1-1 === Each of the multiple elements including the sensor unit, analysis unit, adjustment unit, and output unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sensor unit detects abnormalities using the camera 42, temperature sensor, and humidity sensor of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data from the sensor unit to determine the type and urgency of the abnormality. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and gradually adjusts the volume and type of the warning sound based on the analysis results. The output unit outputs the adjusted warning sound to the user using the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the sensor unit, analysis unit, adjustment unit, and output unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensor unit detects abnormalities using the camera 42, temperature sensor, and humidity sensor of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data from the sensor unit to determine the type and urgency of the abnormality. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and gradually adjusts the volume and type of the warning sound based on the analysis results. The output unit outputs the adjusted warning sound to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the sensor unit, analysis unit, adjustment unit, and output unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the sensor unit detects abnormalities using the camera 42, temperature sensor, and humidity sensor of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data from the sensor unit to determine the type of abnormality and its urgency. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and gradually adjusts the volume and type of sound of the warning sound based on the analysis results. The output unit outputs the adjusted warning sound to the user using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the sensor unit, analysis unit, adjustment unit, and output unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit detects abnormalities using the camera 42, temperature sensor, and humidity sensor of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data from the sensor unit to determine the type and urgency of the abnormality. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and gradually adjusts the volume and type of the warning sound based on the analysis results. The output unit outputs the adjusted warning sound to the user using the speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The warning sound adjustment system may further include a location information acquisition unit that acquires user location information and adjusts the output location of the warning sound. For example, the location information acquisition unit may identify the user's current location using GPS or Wi-Fi signals. The location information acquisition unit may also detect which room in the house the user is in and output the warning sound from the speaker closest to that room. Furthermore, if the user is moving, the location information acquisition unit may dynamically change the output location of the warning sound in accordance with the user's movement. This allows the user to receive the warning sound from an appropriate location no matter where they are.

[0094] The warning sound adjustment system may further include a display unit that displays different warning messages depending on the type of abnormality. For example, the display unit may display "Temperature is too high" in the case of a temperature abnormality, and "Humidity is too high" in the case of a humidity abnormality. The display unit may also change the color and font size of the message depending on the urgency of the abnormality. Furthermore, the display unit may detect whether the user has acknowledged the message and continue to display the message until the user acknowledges it. This allows the user to be effectively notified of the abnormality through a visual warning message.

[0095] The warning sound adjustment system can further include a history management unit that records the history of abnormalities and notifies the user. For example, the history management unit records the type and date and time of past abnormalities so that the user can check them at any time. The history management unit can also analyze the frequency and pattern of abnormalities and suggest preventive measures. Furthermore, if an abnormality recurs, the history management unit can support a quick response by referring to past countermeasures. In this way, by managing the history of abnormalities, the user can take appropriate measures.

[0096] The warning sound adjustment system may further include a communication unit that automatically sends a notification to an emergency contact when an abnormality occurs. For example, when an abnormality is detected, the communication unit sends a notification by SMS or email to a pre-registered emergency contact. The communication unit may also customize the content of the notification depending on the type and urgency of the abnormality. Furthermore, the communication unit may track whether the user has acknowledged the notification and send the notification again if the user has not acknowledged it. This provides a means of communication for prompt response when an abnormality occurs.

[0097] The warning sound adjustment system can further include a control unit that automatically stops the operation of home appliances when an abnormality occurs. For example, the control unit automatically turns off the power of the corresponding home appliance when an abnormality is detected. The control unit can also select home appliances to stop depending on the type of abnormality and the urgency of the abnormality. Furthermore, the control unit can continue to stop the operation of the home appliance until the user manually restarts the home appliance. This makes it possible to ensure safety by automatically controlling the operation of home appliances when an abnormality occurs.

[0098] The warning sound adjustment system may further include an adjustment unit that estimates the user's emotion and adjusts the volume of the warning sound based on the estimated user's emotion. For example, the adjustment unit may set the volume of the warning sound low when the user is stressed. Alternatively, the adjustment unit may set the volume of the warning sound to a normal level when the user is relaxed. Alternatively, the adjustment unit may set the volume of the warning sound high when the user is in a hurry to attract attention. In this way, by adjusting the volume of the warning sound according to the user's emotion, a more appropriate warning sound can be provided.

[0099] The warning sound adjustment system may further include an adjustment unit that estimates the user's emotion and changes the type of warning sound based on the estimated user's emotion. For example, the adjustment unit may set a gentle warning sound when the user is stressed, or a bright warning sound when the user is relaxed. Furthermore, the adjustment unit may set an attention-grabbing warning sound when the user is in a hurry. In this way, by changing the type of warning sound according to the user's emotion, it is possible to provide a more appropriate warning sound.

[0100] The warning sound adjustment system may further include an output unit that estimates a user's emotion and selects an output device for the warning sound based on the estimated user's emotion. For example, the output unit may output the warning sound from headphones when the user is stressed, or from speakers when the user is relaxed. Furthermore, if the user is in a hurry, the output unit may output the warning sound from multiple devices simultaneously. In this way, by selecting an output device for the warning sound according to the user's emotion, it is possible to provide a more appropriate warning sound.

[0101] The warning sound adjustment system may further include an output unit that estimates the user's emotion and adjusts the output timing of the warning sound based on the estimated user emotion. For example, the output unit may delay the output timing of the warning sound when the user is stressed. Alternatively, the output unit may set the output timing of the warning sound to the normal level when the user is relaxed. Alternatively, the output timing of the warning sound may be advanced when the user is in a hurry. This allows the output timing of the warning sound to be adjusted according to the user's emotion, thereby providing a more appropriate warning sound.

[0102] The warning sound adjustment system may further include an output unit that estimates the user's emotion and determines the output order of warning sounds based on the estimated user's emotion. For example, if the user is feeling stressed, the output unit may output warning sounds in order starting with gentle ones. Alternatively, if the user is relaxed, the output unit may output warning sounds in order starting with bright ones. Furthermore, if the user is in a hurry, the output unit may output warning sounds in order starting with important ones quickly. In this way, by determining the output order of warning sounds according to the user's emotion, more appropriate warning sounds can be provided.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The sensor unit is a component for detecting abnormalities. The sensor unit includes, for example, a temperature sensor, a humidity sensor, and a camera. The temperature sensor measures temperature using, for example, a thermistor, an RTD (resistance temperature detector), or a thermocouple. The humidity sensor measures humidity using, for example, capacitance, resistance, or thermal conduction. The camera detects abnormalities using, for example, a high-resolution camera, an infrared camera, or a wide-angle camera. Step 2: The analysis unit analyzes the data from the sensor unit and determines the type of abnormality and the urgency of the abnormality. The analysis unit analyzes the data using, for example, statistical analysis, machine learning algorithms, etc. Step 3: The adjustment unit adjusts the volume and type of the warning sound in stages based on the analysis results. For example, the adjustment unit adjusts the volume in decibels (dB) and selects the type of sound, such as a beep, chime, or alarm. Step 4: The output unit outputs the adjusted warning sound to the user. The output unit outputs the warning sound using a device such as a speaker or headphones.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sensor unit that detects abnormalities; an analysis unit that analyzes the type and urgency of the abnormality detected by the sensor unit; an adjustment unit that adjusts the warning sound in a specific manner based on the analysis result obtained by the analysis unit; an output unit that outputs the warning sound adjusted by the adjustment unit; Equipped with A system characterized by:

2. The sensor unit Equipped with a temperature or humidity sensor and a camera 2. The system of claim 1.

3. The analysis unit Analyzes data from the sensor unit and determines the type of abnormality and the level of urgency 2. The system of claim 1.

4. The adjustment unit Adjust the volume and type of warning sounds in specific ways based on the analysis results 2. The system of claim 1.

5. The output unit Outputs a tuned warning sound to the user 2. The system of claim 1.

6. The analysis unit Monitor user responses using voice recognition or motion detection 2. The system of claim 1.

7. The adjustment unit Stopping the alert in a specific way based on the user's response 2. The system of claim 1.

8. The sensor unit Inferring a user's emotion and adjusting the sensitivity of the sensor in a specific way based on the estimated user's emotion 2. The system of claim 1.

9. The sensor unit Upon anomaly detection, dynamically change the sensor placement in a specific way to identify the details of the anomaly.

2. The system of claim 1.

10. The sensor unit During anomaly detection, data from multiple sensors is integrated in a specific way to improve the accuracy of anomaly detection.

2. The system of claim 1.

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