System

Abstract

An object of the system according to the embodiment is to prevent lonely death and fraud damage of elderly people by analyzing ambient sound.SOLUTION: A system includes an environmental sound acquisition unit, an analysis unit, and a warning unit. The environmental sound acquisition unit acquires ambient sound. The analysis unit analyzes the audio data acquired by the environmental sound acquisition unit. The warning unit issues a warning when an abnormality is detected by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide sufficient effective monitoring to prevent elderly people from dying alone or becoming victims of fraud, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent elderly people from dying alone or becoming victims of fraud by analyzing surrounding sounds. [Means for solving the problem]

[0006] The system according to the embodiment includes an environmental sound acquisition unit, an analysis unit, and a warning unit. The environmental sound acquisition unit acquires ambient sounds. The analysis unit analyzes the audio data acquired by the environmental sound acquisition unit. The warning unit issues a warning when an abnormality is detected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze surrounding sounds to prevent elderly people from dying alone or becoming victims of fraud. [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) The monitoring system according to an embodiment of the present invention uses a smartphone to sense ambient sounds and prevent elderly people from dying alone or becoming victims of fraud. This system constantly monitors surrounding sounds and issues an alert if it detects an abnormal or specific sound. It also uses a generative AI to analyze the audio data and take appropriate action as necessary. This allows the monitoring system to prevent elderly people from dying alone or becoming victims of fraud.

[0029] A monitoring system according to an embodiment includes an environmental sound acquisition unit, an analysis unit, and a warning unit. The environmental sound acquisition unit acquires ambient sounds. For example, the environmental sound acquisition unit constantly monitors the surrounding environmental sounds using a smartphone microphone. The environmental sound acquisition unit can detect everyday sounds, conversations, television sounds, and the like. The analysis unit analyzes the audio data acquired by the environmental sound acquisition unit. For example, a generation AI analyzes the audio data and detects abnormal or specific sounds. The generation AI can analyze the audio data using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI performs analysis based on prompts containing instructions for detecting abnormal or specific sounds. The warning unit issues a warning when an abnormality is detected by the analysis unit. For example, if a prolonged period of silence, a voice calling for help, or the sound of breaking glass is detected, the warning unit determines this to be an abnormality and issues a warning. This enables the monitoring system according to an embodiment to efficiently monitor elderly people. For example, if an abnormality is detected, the warning unit notifies a family member or a caregiver. The notification is sent via a smartphone app. It can also encourage appropriate action as needed.

[0030] The environmental sound acquisition unit can use multiple microphones in the smartphone to identify the direction of a sound source and identify the location where an abnormal sound is occurring. The environmental sound acquisition unit, for example, incorporates multiple microphones in the smartphone and introduces technology for identifying the direction of a sound source. For example, microphones are placed above, below, left, and right of the smartphone, and the direction of the sound source is calculated using the difference in sound arrival time. The environmental sound acquisition unit also uses the smartphone's microphone array to build a system that identifies the direction of a sound source in real time. For example, when an abnormal sound occurs, the direction of the sound source is identified and displayed on a map. The environmental sound acquisition unit also implements an algorithm for identifying the direction of a sound source in the smartphone and identifies the location where the abnormal sound is occurring. For example, the environmental sound acquisition unit analyzes audio data obtained from multiple microphones and calculates the direction of the sound source. This allows the location of the abnormal sound to be identified, enabling a rapid response.

[0031] The analysis unit can introduce an algorithm for learning environmental sound patterns and distinguishing between ordinary sounds and abnormal sounds. The analysis unit, for example, introduces a machine learning algorithm for learning environmental sound patterns and distinguishes between ordinary sounds and abnormal sounds. For example, it uses ordinary sounds of daily life and conversation as training data to detect abnormal sounds. The analysis unit also adds a function for learning environmental sound patterns to the smartphone and builds a system that detects abnormal sounds with high accuracy. For example, it uses sample data of abnormal sounds to perform training and detects abnormal sounds in real time. The analysis unit also implements a deep learning algorithm for learning environmental sound patterns in the smartphone and distinguishes between abnormal sounds with high accuracy. For example, it extracts the characteristics of abnormal sounds and builds a model for detecting abnormal sounds. This allows for high-accuracy distinction between ordinary sounds and abnormal sounds, thereby reducing false positives.

[0032] The environmental sound acquisition unit can also use the smartphone's camera to detect abnormalities by combining sound and video. The environmental sound acquisition unit, for example, also uses the smartphone's camera to build a system that detects abnormalities by combining sound and video. For example, when an abnormal sound is detected, the camera video is analyzed to identify the cause of the abnormality. The environmental sound acquisition unit also implements an algorithm in the smartphone for detecting abnormalities by combining sound and video, and analyzes the camera video when an abnormal sound occurs. For example, the location where the abnormal sound is occurring is confirmed using video. The environmental sound acquisition unit also links the smartphone's camera and microphone to develop a system that detects abnormalities by combining sound and video. For example, when an abnormal sound is detected, the camera video is analyzed in real time to identify the cause of the abnormality. In this way, by combining sound and video, the accuracy of abnormality detection is improved.

[0033] The environmental sound acquisition unit can use the smartphone's location information to display the location where an abnormal sound occurs on a map. The environmental sound acquisition unit, for example, uses the smartphone's location information to build a system that displays the location where an abnormal sound occurs on a map. For example, when an abnormal sound is detected, the location is mapped on a map. The environmental sound acquisition unit also uses the smartphone's GPS function to identify the location where the abnormal sound occurred and displays it on a map. For example, the location where the abnormal sound occurred is displayed on a map in real time and family members or caregivers are notified. The environmental sound acquisition unit also develops an algorithm that uses the smartphone's location information to display the location where the abnormal sound occurred on a map. For example, when an abnormal sound is detected, the location is pinpointed on a map. This allows the location of the abnormal sound to be displayed on a map, enabling a prompt response.

[0034] The analysis unit uses the generation AI to analyze audio data collected by the smartphone and detect abnormal or specific sounds. For example, when the generation AI detects an abnormal sound, the analysis unit introduces an algorithm that performs detailed analysis of the type and pattern of the sound to identify the cause of the abnormality. For example, it analyzes the frequency and amplitude of the abnormal sound to identify the type of abnormality. In addition, when an abnormal sound is detected, the analysis unit implements a deep learning model in the generation AI to perform detailed analysis of the sound pattern to identify the cause of the abnormality. For example, it extracts the characteristics of the abnormal sound and classifies the type of abnormality. In addition, when the generation AI detects an abnormal sound, the analysis unit builds a system that performs detailed analysis of the type and pattern of the sound to identify the cause of the abnormality. For example, it performs spectral analysis of the abnormal sound to identify the type of abnormality. This enables the generation AI to detect abnormal or specific sounds with high accuracy.

[0035] The analysis unit can use the generation AI to develop an algorithm that not only detects abnormal sounds but also analyzes audio data before the occurrence of abnormal sounds and detects signs of abnormal sounds. The analysis unit, for example, analyzes audio data before abnormal sounds occur and develops an algorithm for detecting signs of abnormal sounds. For example, it detects subtle changes in sound before abnormal sounds occur and identifies the signs. The analysis unit also adds a function to the generation AI to detect signs of abnormal sounds and builds a system that analyzes audio data before abnormal sounds occur. For example, it learns sound patterns that are signs of abnormal sounds and detects the signs. The analysis unit also implements a deep learning model in the generation AI to analyze audio data before abnormal sounds occur and detect signs of abnormal sounds. For example, it extracts sound characteristics that are signs of abnormal sounds and identifies the signs. This makes it possible to detect signs of abnormal sounds and take action in advance.

[0036] The analysis unit can build a database of abnormal sounds by storing the results of abnormal sound detection in the cloud and sharing them with other users. The analysis unit, for example, builds a system that stores the results of abnormal sound detection in the cloud and shares them with other users. For example, it builds a database of abnormal sounds on the cloud and shares abnormal sound patterns. The analysis unit also develops a platform for storing data of abnormal sounds detected by the generation AI in the cloud and sharing it with other users. For example, it uploads sample data of abnormal sounds to the cloud so that other users can access it. The analysis unit also develops a system that builds a database of abnormal sounds by storing the results of abnormal sound detection in the cloud and sharing it with other users. For example, it saves the characteristics of abnormal sounds on the cloud so that other users can use them as a reference when detecting abnormal sounds. In this way, the database of abnormal sounds is built, thereby improving the accuracy of abnormal sound detection.

[0037] The analysis unit can link the abnormal sound detection results with smart home devices and take action such as automatically turning on lights. For example, the analysis unit will build a system that links the abnormal sound detection results with smart home devices and automatically turns on lights. For example, turning on a smart light when an abnormal sound is detected. The analysis unit will also link the abnormal sound data detected by the generation AI with smart home devices and develop a system that automatically takes action. For example, sounding an alarm from a smart speaker when an abnormal sound is detected. The analysis unit will also link the abnormal sound detection results with smart home devices and develop a platform for automatic action. For example, activating a smart camera when an abnormal sound is detected. This will enable rapid action by automatically taking action based on the abnormal sound detection results.

[0038] The analysis unit can use the generation AI to analyze phone calls and conversations with visitors to detect potential fraud. For example, the analysis unit trains the generation AI to learn fraud methods and patterns, building a system that detects fraud with high accuracy when analyzing conversation content. For example, typical fraud phrases and expressions are used as training data. The analysis unit also implements a deep learning model in the generation AI to learn fraud methods and patterns, allowing it to detect fraud with high accuracy when analyzing conversation content. For example, it extracts fraud characteristics and determines the possibility of fraud. The analysis unit also introduces an algorithm that learns fraud methods and patterns when the generation AI analyzes conversation content, allowing it to detect fraud with high accuracy. For example, it uses fraud case data to learn and identify potential fraud. This allows it to detect potential fraud with high accuracy, thereby protecting the elderly from fraud.

[0039] When the analysis unit detects a conversation that may be fraudulent, it can analyze the context and background information of that conversation and assess the risk of fraud. For example, when the analysis unit detects a conversation that may be fraudulent, it builds a system that analyzes the context and background information of that conversation and assesses the risk of fraud. For example, it analyzes the content before and after the conversation and related information. The analysis unit also adds a context analysis function to the generative AI and analyzes the context and background information when a conversation that may be fraudulent is detected. For example, it assesses the risk of fraud based on the flow of the conversation and related information. When the analysis unit detects a conversation that may be fraudulent, it implements a deep learning model in the generative AI to analyze the context and background information of that conversation and assess the risk of fraud. For example, it analyzes the content before and after the conversation and related information. This allows for highly accurate assessment of the risk of fraud, thereby protecting elderly people from fraud.

[0040] When the analysis unit detects a conversation that may be fraudulent, it can compare it with past fraud cases and evaluate the similarity. For example, when the analysis unit detects a conversation that may be fraudulent, it builds a system that compares it with past fraud cases and evaluates the similarity. For example, it refers to a database of fraud cases and calculates the similarity. The analysis unit also adds a function to the generation AI that compares it with past fraud cases, and evaluates the similarity when it detects a conversation that may be fraudulent. For example, it calculates the similarity based on fraud case data and evaluates the risk of fraud. When the analysis unit detects a conversation that may be fraudulent, it implements a deep learning model in the generation AI to compare it with past fraud cases and evaluates the similarity. For example, it calculates the similarity using fraud case data. This makes it possible to compare it with past fraud cases and evaluate the risk of fraud with high accuracy.

[0041] The analysis unit enables the generation AI to automatically start recording when it detects a conversation that may be fraudulent and save it as evidence. For example, the analysis unit builds a system in which the generation AI automatically starts recording when it detects a conversation that may be fraudulent and saves it as evidence. For example, recording starts when a phrase that may be fraudulent is detected. The analysis unit also adds an automatic recording function to the generation AI and starts recording when it detects a conversation that may be fraudulent. For example, if a conversation with a high risk of fraud is detected, recording is automatically started and saved as evidence. The analysis unit also develops an algorithm in which the generation AI automatically starts recording when it detects a conversation that may be fraudulent and saves it as evidence. For example, recording starts when a phrase that may be fraudulent is detected. In this way, conversations that may be fraudulent can be automatically recorded and saved as evidence, making it possible to review them later.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit uses the generation AI to analyze audio data collected by the smartphone and detect abnormal or specific sounds. For example, when the generation AI detects an abnormal sound, it introduces an algorithm that performs detailed analysis of the type and pattern of the sound to identify the cause of the abnormality. For example, it analyzes the frequency and amplitude of the abnormal sound to identify the type of abnormality. In addition, when an abnormal sound is detected, the analysis unit implements a deep learning model in the generation AI to perform detailed analysis of the sound pattern to identify the cause of the abnormality. For example, it extracts the characteristics of the abnormal sound and classifies the type of abnormality. In addition, when the generation AI detects an abnormal sound, the analysis unit builds a system that performs detailed analysis of the type and pattern of the sound to identify the cause of the abnormality. For example, it performs spectral analysis of the abnormal sound to identify the type of abnormality. This enables the generation AI to detect abnormal or specific sounds with high accuracy.

[0044] The analysis unit can build a database of abnormal sounds by storing the results of abnormal sound detection in the cloud and sharing them with other users. For example, a system is built to store the results of abnormal sound detection in the cloud and share them with other users. For example, a database of abnormal sounds is built on the cloud and abnormal sound patterns are shared. The analysis unit also develops a platform to store the data of abnormal sounds detected by the generation AI in the cloud and share it with other users. For example, sample data of abnormal sounds is uploaded to the cloud so that other users can access it. The analysis unit also develops a system to build a database of abnormal sounds by storing the results of abnormal sound detection in the cloud and sharing it with other users. For example, the characteristics of abnormal sounds are saved on the cloud so that other users can use them as a reference when detecting abnormal sounds. In this way, a database of abnormal sounds is built and the accuracy of abnormal sound detection is improved.

[0045] When the analysis unit detects a conversation that may be fraudulent, it can analyze the context and background information of that conversation to assess the risk of fraud. For example, a system can be built to analyze the context and background information of a conversation when a conversation that may be fraudulent is detected, and the risk of fraud can be assessed. For example, the content before and after the conversation and related information can be analyzed. The analysis unit can also add a context analysis function to the generative AI, and analyze the context and background information when a conversation that may be fraudulent is detected. For example, the risk of fraud can be assessed based on the flow of the conversation and related information. The analysis unit can also implement a deep learning model in the generative AI to analyze the context and background information of that conversation when a conversation that may be fraudulent is detected, and assess the risk of fraud. For example, the content before and after the conversation and related information can be analyzed. This can accurately assess the risk of fraud, making it possible to protect elderly people from fraud.

[0046] When the analysis unit detects a conversation that may be fraudulent, it can compare it with past fraud cases and evaluate the similarity. For example, when a conversation that may be fraudulent is detected, it can build a system that compares it with past fraud cases and evaluate the similarity. For example, it can refer to a database of fraud cases and calculate the similarity. The analysis unit can also add a function to the generation AI that compares it with past fraud cases and evaluate the similarity when a conversation that may be fraudulent is detected. For example, it can calculate the similarity based on fraud case data and evaluate the risk of fraud. The analysis unit can also implement a deep learning model in the generation AI to compare it with past fraud cases and evaluate the similarity when a conversation that may be fraudulent is detected. For example, it can calculate the similarity using fraud case data. This makes it possible to compare it with past fraud cases and evaluate the risk of fraud with high accuracy.

[0047] The analysis unit enables the generation AI to automatically start recording when it detects a conversation that may be fraudulent, and save the recording as evidence. For example, a system can be built in which the generation AI automatically starts recording when it detects a conversation that may be fraudulent, and save the recording as evidence. For example, recording starts when a phrase that may be fraudulent is detected. The analysis unit also adds an automatic recording function to the generation AI, and starts recording when it detects a conversation that may be fraudulent. For example, if a conversation with a high risk of fraud is detected, recording is automatically started and saved as evidence. The analysis unit also develops an algorithm to enable the generation AI to automatically start recording when it detects a conversation that may be fraudulent, and save the recording as evidence. For example, recording starts when a phrase that may be fraudulent is detected. This makes it possible to automatically record conversations that may be fraudulent and save them as evidence, allowing for later verification.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The environmental sound acquisition unit acquires ambient sounds. For example, it can constantly monitor the surrounding environmental sounds using a smartphone microphone. It can also detect everyday sounds, conversation sounds, television sounds, etc. Step 2: The analysis unit analyzes the audio data acquired by the environmental sound acquisition unit. For example, the generation AI analyzes the audio data and detects abnormal or specific sounds. The generation AI can analyze the audio data using text generation AI (e.g., LLM) or multimodal generation AI. The generation AI performs analysis based on prompts containing instructions for detecting abnormal or specific sounds. Step 3: The warning unit issues a warning if the analysis unit detects an abnormality. For example, if a long period of silence, a voice calling for help, or the sound of glass breaking is detected, the warning unit determines this to be an abnormality and issues a warning. This allows the monitoring system according to the embodiment to efficiently monitor elderly people. For example, if an abnormality is detected, the warning unit notifies family members or caregivers. The notification is sent via a smartphone app. It can also prompt appropriate action as necessary.

[0050] (Example 2) The monitoring system according to an embodiment of the present invention uses a smartphone to sense ambient sounds and prevent elderly people from dying alone or becoming victims of fraud. This system constantly monitors surrounding sounds and issues an alert if it detects an abnormal or specific sound. It also uses a generative AI to analyze the audio data and take appropriate action as necessary. This allows the monitoring system to prevent elderly people from dying alone or becoming victims of fraud.

[0051] A monitoring system according to an embodiment includes an environmental sound acquisition unit, an analysis unit, and a warning unit. The environmental sound acquisition unit acquires ambient sounds. For example, the environmental sound acquisition unit constantly monitors the surrounding environmental sounds using a smartphone microphone. The environmental sound acquisition unit can detect everyday sounds, conversations, television sounds, and the like. The analysis unit analyzes the audio data acquired by the environmental sound acquisition unit. For example, a generation AI analyzes the audio data and detects abnormal or specific sounds. The generation AI can analyze the audio data using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI performs analysis based on prompts containing instructions for detecting abnormal or specific sounds. The warning unit issues a warning when an abnormality is detected by the analysis unit. For example, if a prolonged period of silence, a voice calling for help, or the sound of breaking glass is detected, the warning unit determines this to be an abnormality and issues a warning. This enables the monitoring system according to an embodiment to efficiently monitor elderly people. For example, if an abnormality is detected, the warning unit notifies a family member or a caregiver. The notification is sent via a smartphone app. It can also encourage appropriate action as needed.

[0052] The environmental sound acquisition unit can use multiple microphones in the smartphone to identify the direction of a sound source and identify the location where an abnormal sound is occurring. The environmental sound acquisition unit, for example, incorporates multiple microphones in the smartphone and introduces technology for identifying the direction of a sound source. For example, microphones are placed above, below, left, and right of the smartphone, and the direction of the sound source is calculated using the difference in sound arrival time. The environmental sound acquisition unit also uses the smartphone's microphone array to build a system that identifies the direction of a sound source in real time. For example, when an abnormal sound occurs, the direction of the sound source is identified and displayed on a map. The environmental sound acquisition unit also implements an algorithm for identifying the direction of a sound source in the smartphone and identifies the location where the abnormal sound is occurring. For example, the environmental sound acquisition unit analyzes audio data obtained from multiple microphones and calculates the direction of the sound source. This allows the location of the abnormal sound to be identified, enabling a rapid response.

[0053] The analysis unit can introduce an algorithm for learning environmental sound patterns and distinguishing between ordinary sounds and abnormal sounds. The analysis unit, for example, introduces a machine learning algorithm for learning environmental sound patterns and distinguishes between ordinary sounds and abnormal sounds. For example, it uses ordinary sounds of daily life and conversation as training data to detect abnormal sounds. The analysis unit also adds a function for learning environmental sound patterns to the smartphone and builds a system that detects abnormal sounds with high accuracy. For example, it uses sample data of abnormal sounds to perform training and detects abnormal sounds in real time. The analysis unit also implements a deep learning algorithm for learning environmental sound patterns in the smartphone and distinguishes between abnormal sounds with high accuracy. For example, it extracts the characteristics of abnormal sounds and builds a model for detecting abnormal sounds. This allows for high-accuracy distinction between ordinary sounds and abnormal sounds, thereby reducing false positives.

[0054] The analysis unit can use the emotion estimation function to estimate the emotional state of the elderly person from environmental sounds and issue a warning if stress or anxiety is increasing. The analysis unit, for example, introduces an emotion estimation algorithm to estimate the emotional state of the elderly person from environmental sounds and issues a warning if stress or anxiety is increasing. For example, it analyzes the tone and rhythm of voice to estimate the emotional state. The analysis unit also adds an emotion estimation function to a smartphone and builds a system that monitors the emotional state of the elderly person in real time from environmental sounds. For example, it notifies family members or caregivers if the emotional state worsens. The analysis unit also implements a deep learning model on the smartphone to estimate the emotional state of the elderly person from environmental sounds and issues a warning if stress or anxiety is increasing. For example, it analyzes voice data to estimate the emotional state. This makes it possible to monitor the emotional state of the elderly person and respond quickly if stress or anxiety is increasing.

[0055] The environmental sound acquisition unit can also use the smartphone's camera to detect abnormalities by combining sound and video. The environmental sound acquisition unit, for example, also uses the smartphone's camera to build a system that detects abnormalities by combining sound and video. For example, when an abnormal sound is detected, the camera video is analyzed to identify the cause of the abnormality. The environmental sound acquisition unit also implements an algorithm in the smartphone for detecting abnormalities by combining sound and video, and analyzes the camera video when an abnormal sound occurs. For example, the location where the abnormal sound is occurring is confirmed using video. The environmental sound acquisition unit also links the smartphone's camera and microphone to develop a system that detects abnormalities by combining sound and video. For example, when an abnormal sound is detected, the camera video is analyzed in real time to identify the cause of the abnormality. In this way, by combining sound and video, the accuracy of abnormality detection is improved.

[0056] The environmental sound acquisition unit can use the smartphone's location information to display the location where an abnormal sound occurs on a map. The environmental sound acquisition unit, for example, uses the smartphone's location information to build a system that displays the location where an abnormal sound occurs on a map. For example, when an abnormal sound is detected, the location is mapped on a map. The environmental sound acquisition unit also uses the smartphone's GPS function to identify the location where the abnormal sound occurred and displays it on a map. For example, the location where the abnormal sound occurred is displayed on a map in real time and family members or caregivers are notified. The environmental sound acquisition unit also develops an algorithm that uses the smartphone's location information to display the location where the abnormal sound occurred on a map. For example, when an abnormal sound is detected, the location is pinpointed on a map. This allows the location of the abnormal sound to be displayed on a map, enabling a prompt response.

[0057] The analysis unit can use the emotion estimation function to monitor the emotional reactions of elderly people to environmental sounds in real time and notify family members if an abnormality is detected. For example, the analysis unit introduces an emotion estimation function for monitoring the emotional reactions of elderly people to environmental sounds in real time and notifies family members if an abnormality is detected. For example, a notification is automatically sent if the emotional state worsens. The analysis unit also adds an emotion estimation function to a smartphone and builds a system for monitoring the emotional reactions of elderly people to environmental sounds in real time. For example, when an abnormal sound is detected, the emotional state is analyzed and a notification is sent to family members. The analysis unit also implements a deep learning model on the smartphone for monitoring the emotional reactions of elderly people to environmental sounds in real time and notifies family members if an abnormality is detected. For example, a notification is automatically sent if the emotional state worsens. This makes it possible to monitor the emotional reactions of elderly people in real time and quickly notify family members if an abnormality is detected.

[0058] The analysis unit uses the generation AI to analyze audio data collected by the smartphone and detect abnormal or specific sounds. For example, when the generation AI detects an abnormal sound, the analysis unit introduces an algorithm that performs detailed analysis of the type and pattern of the sound to identify the cause of the abnormality. For example, it analyzes the frequency and amplitude of the abnormal sound to identify the type of abnormality. In addition, when an abnormal sound is detected, the analysis unit implements a deep learning model in the generation AI to perform detailed analysis of the sound pattern to identify the cause of the abnormality. For example, it extracts the characteristics of the abnormal sound and classifies the type of abnormality. In addition, when the generation AI detects an abnormal sound, the analysis unit builds a system that performs detailed analysis of the type and pattern of the sound to identify the cause of the abnormality. For example, it performs spectral analysis of the abnormal sound to identify the type of abnormality. This enables the generation AI to detect abnormal or specific sounds with high accuracy.

[0059] The analysis unit can use the generation AI to develop an algorithm that not only detects abnormal sounds but also analyzes audio data before the occurrence of abnormal sounds and detects signs of abnormal sounds. The analysis unit, for example, analyzes audio data before abnormal sounds occur and develops an algorithm for detecting signs of abnormal sounds. For example, it detects subtle changes in sound before abnormal sounds occur and identifies the signs. The analysis unit also adds a function to the generation AI to detect signs of abnormal sounds and builds a system that analyzes audio data before abnormal sounds occur. For example, it learns sound patterns that are signs of abnormal sounds and detects the signs. The analysis unit also implements a deep learning model in the generation AI to analyze audio data before abnormal sounds occur and detect signs of abnormal sounds. For example, it extracts sound characteristics that are signs of abnormal sounds and identifies the signs. This makes it possible to detect signs of abnormal sounds and take action in advance.

[0060] The analysis unit can use the emotion estimation function to analyze the emotional state of the elderly person when an abnormal sound is generated and determine an abnormality based on changes in emotion. For example, the analysis unit uses the emotion estimation function to build a system that analyzes the emotional state of the elderly person when an abnormal sound is generated and determine an abnormality based on changes in emotion. For example, if the emotional state worsens when an abnormal sound is generated, it determines an abnormality. The analysis unit also adds an emotion estimation function to the generation AI and analyzes the emotional state of the elderly person when an abnormal sound is generated in real time. For example, it monitors the emotional state when an abnormal sound is generated and determines an abnormality. The analysis unit also uses the emotion estimation function to implement a deep learning model in the generation AI for analyzing the emotional state of the elderly person when an abnormal sound is generated and determines an abnormality based on changes in emotion. For example, it determines an abnormality if the emotional state worsens when an abnormal sound is generated. This improves the accuracy of anomaly detection by analyzing the emotional state of the elderly person.

[0061] The analysis unit can build a database of abnormal sounds by storing the results of abnormal sound detection in the cloud and sharing them with other users. The analysis unit, for example, builds a system that stores the results of abnormal sound detection in the cloud and shares them with other users. For example, it builds a database of abnormal sounds on the cloud and shares abnormal sound patterns. The analysis unit also develops a platform for storing data of abnormal sounds detected by the generation AI in the cloud and sharing it with other users. For example, it uploads sample data of abnormal sounds to the cloud so that other users can access it. The analysis unit also develops a system that builds a database of abnormal sounds by storing the results of abnormal sound detection in the cloud and sharing it with other users. For example, it saves the characteristics of abnormal sounds on the cloud so that other users can use them as a reference when detecting abnormal sounds. In this way, the database of abnormal sounds is built, thereby improving the accuracy of abnormal sound detection.

[0062] The analysis unit can link the abnormal sound detection results with smart home devices and take action such as automatically turning on lights. For example, the analysis unit will build a system that links the abnormal sound detection results with smart home devices and automatically turns on lights. For example, turning on a smart light when an abnormal sound is detected. The analysis unit will also link the abnormal sound data detected by the generation AI with smart home devices and develop a system that automatically takes action. For example, sounding an alarm from a smart speaker when an abnormal sound is detected. The analysis unit will also link the abnormal sound detection results with smart home devices and develop a platform for automatic action. For example, activating a smart camera when an abnormal sound is detected. This will enable rapid action by automatically taking action based on the abnormal sound detection results.

[0063] The analysis unit can use the emotion estimation function to record the emotional reactions of elderly people when abnormal sounds are generated and monitor long-term changes in their emotions. For example, the analysis unit uses the emotion estimation function to build a system that records the emotional reactions of elderly people when abnormal sounds are generated and monitors long-term changes in their emotions. For example, the analysis unit records an emotion score when an abnormal sound is generated and tracks changes over time. The analysis unit also adds an emotion estimation function to the generation AI and records the emotional reactions of elderly people when abnormal sounds are generated in real time. For example, the analysis unit monitors the emotional state when an abnormal sound is generated and analyzes long-term changes in their emotions. The analysis unit also uses the emotion estimation function to implement a deep learning model in the generation AI for recording the emotional reactions of elderly people when abnormal sounds are generated and monitors long-term changes in their emotions. For example, the analysis unit records the emotional state when an abnormal sound is generated and analyzes long-term changes. This enables early detection of abnormalities by recording the emotional reactions of elderly people and monitoring long-term changes in their emotions.

[0064] The analysis unit can use the generation AI to analyze phone calls and conversations with visitors to detect potential fraud. For example, the analysis unit trains the generation AI to learn fraud methods and patterns, building a system that detects fraud with high accuracy when analyzing conversation content. For example, typical fraud phrases and expressions are used as training data. The analysis unit also implements a deep learning model in the generation AI to learn fraud methods and patterns, allowing it to detect fraud with high accuracy when analyzing conversation content. For example, it extracts fraud characteristics and determines the possibility of fraud. The analysis unit also introduces an algorithm that learns fraud methods and patterns when the generation AI analyzes conversation content, allowing it to detect fraud with high accuracy. For example, it uses fraud case data to learn and identify potential fraud. This allows it to detect potential fraud with high accuracy, thereby protecting the elderly from fraud.

[0065] When the analysis unit detects a conversation that may be fraudulent, it can analyze the context and background information of that conversation and assess the risk of fraud. For example, when the analysis unit detects a conversation that may be fraudulent, it builds a system that analyzes the context and background information of that conversation and assesses the risk of fraud. For example, it analyzes the content before and after the conversation and related information. The analysis unit also adds a context analysis function to the generative AI and analyzes the context and background information when a conversation that may be fraudulent is detected. For example, it assesses the risk of fraud based on the flow of the conversation and related information. When the analysis unit detects a conversation that may be fraudulent, it implements a deep learning model in the generative AI to analyze the context and background information of that conversation and assess the risk of fraud. For example, it analyzes the content before and after the conversation and related information. This allows for highly accurate assessment of the risk of fraud, thereby protecting elderly people from fraud.

[0066] The analysis unit can use the emotion estimation function to analyze the emotional state of elderly people during conversations that may involve fraud, and issue a warning if anxiety or fear is increasing. For example, the analysis unit can use the emotion estimation function to build a system that analyzes the emotional state of elderly people during conversations that may involve fraud, and issue a warning if anxiety or fear is increasing. For example, the warning can be automatically issued if the emotion score is high. The analysis unit also adds an emotion estimation function to the generation AI to analyze the emotional state of elderly people during conversations that may involve fraud in real time. For example, the emotional state can be monitored during the conversation, and a warning can be issued if anxiety or fear is increasing. The analysis unit also implements a deep learning model in the generation AI using the emotion estimation function to analyze the emotional state of elderly people during conversations that may involve fraud, and issue a warning if anxiety or fear is increasing. For example, the warning can be automatically issued if the emotion score is high. This makes it possible to detect the risk of fraud early by analyzing the emotional state of elderly people and issue a warning.

[0067] When the analysis unit detects a conversation that may be fraudulent, it can compare it with past fraud cases and evaluate the similarity. For example, when the analysis unit detects a conversation that may be fraudulent, it builds a system that compares it with past fraud cases and evaluates the similarity. For example, it refers to a database of fraud cases and calculates the similarity. The analysis unit also adds a function to the generation AI that compares it with past fraud cases, and evaluates the similarity when it detects a conversation that may be fraudulent. For example, it calculates the similarity based on fraud case data and evaluates the risk of fraud. When the analysis unit detects a conversation that may be fraudulent, it implements a deep learning model in the generation AI to compare it with past fraud cases and evaluates the similarity. For example, it calculates the similarity using fraud case data. This makes it possible to compare it with past fraud cases and evaluate the risk of fraud with high accuracy.

[0068] The analysis unit enables the generation AI to automatically start recording when it detects a conversation that may be fraudulent and save it as evidence. For example, the analysis unit builds a system in which the generation AI automatically starts recording when it detects a conversation that may be fraudulent and saves it as evidence. For example, recording starts when a phrase that may be fraudulent is detected. The analysis unit also adds an automatic recording function to the generation AI and starts recording when it detects a conversation that may be fraudulent. For example, if a conversation with a high risk of fraud is detected, recording is automatically started and saved as evidence. The analysis unit also develops an algorithm in which the generation AI automatically starts recording when it detects a conversation that may be fraudulent and saves it as evidence. For example, recording starts when a phrase that may be fraudulent is detected. In this way, conversations that may be fraudulent can be automatically recorded and saved as evidence, making it possible to review them later.

[0069] The analysis unit can use the emotion estimation function to monitor the emotional reactions of elderly people during conversations that may involve fraud in real time, and notify family members if an abnormality is detected. For example, the analysis unit can use the emotion estimation function to build a system that monitors the emotional reactions of elderly people during conversations that may involve fraud in real time, and notify family members if an abnormality is detected. For example, a notification can be automatically sent if the emotion score is high. The analysis unit also adds an emotion estimation function to the generation AI to monitor the emotional reactions of elderly people during conversations that may involve fraud in real time. For example, the emotional state during the conversation can be monitored, and family members can be notified if an abnormality is detected. The analysis unit also implements a deep learning model in the generation AI using the emotion estimation function to monitor the emotional reactions of elderly people during conversations that may involve fraud in real time, and notify family members if an abnormality is detected. For example, a notification can be automatically sent if the emotion score is high. This makes it possible to monitor the emotional reactions of elderly people in real time, and quickly notify family members if an abnormality is detected.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The analysis unit uses the emotion estimation function to estimate the emotional state of the elderly from environmental sounds and can issue a warning if stress or anxiety is increasing. For example, it analyzes the tone and rhythm of the voice to estimate the emotional state. The analysis unit also adds the emotion estimation function to smartphones to build systems that monitor the emotional state of the elderly from environmental sounds in real time. For example, it notifies family members or caregivers if the emotional state worsens. The analysis unit also implements a deep learning model on smartphones to estimate the emotional state of the elderly from environmental sounds and issues a warning if stress or anxiety is increasing. For example, it analyzes voice data to estimate the emotional state. This makes it possible to monitor the emotional state of the elderly and respond quickly if stress or anxiety is increasing.

[0072] The analysis unit uses the generation AI to analyze audio data collected by the smartphone and detect abnormal or specific sounds. For example, when the generation AI detects an abnormal sound, it introduces an algorithm that performs detailed analysis of the type and pattern of the sound to identify the cause of the abnormality. For example, it analyzes the frequency and amplitude of the abnormal sound to identify the type of abnormality. In addition, when an abnormal sound is detected, the analysis unit implements a deep learning model in the generation AI to perform detailed analysis of the sound pattern to identify the cause of the abnormality. For example, it extracts the characteristics of the abnormal sound and classifies the type of abnormality. In addition, when the generation AI detects an abnormal sound, the analysis unit builds a system that performs detailed analysis of the type and pattern of the sound to identify the cause of the abnormality. For example, it performs spectral analysis of the abnormal sound to identify the type of abnormality. This enables the generation AI to detect abnormal or specific sounds with high accuracy.

[0073] The analysis unit can use the emotion estimation function to analyze the emotional state of the elderly person when an abnormal sound is generated and determine an abnormality based on changes in emotion. For example, the emotion estimation function can be used to build a system that analyzes the emotional state of the elderly person when an abnormal sound is generated, and determine an abnormality based on changes in emotion. For example, if the emotional state worsens when an abnormal sound is generated, an abnormality is determined. The analysis unit also adds an emotion estimation function to the generation AI to analyze the emotional state of the elderly person when an abnormal sound is generated in real time. For example, the emotional state is monitored when an abnormal sound is generated and an abnormality is determined. The analysis unit also uses the emotion estimation function to implement a deep learning model in the generation AI to analyze the emotional state of the elderly person when an abnormal sound is generated, and determine an abnormality based on changes in emotion. For example, if the emotional state worsens when an abnormal sound is generated, an abnormality is determined. This improves the accuracy of anomaly detection by analyzing the emotional state of the elderly person.

[0074] The analysis unit can build a database of abnormal sounds by storing the results of abnormal sound detection in the cloud and sharing them with other users. For example, a system is built to store the results of abnormal sound detection in the cloud and share them with other users. For example, a database of abnormal sounds is built on the cloud and abnormal sound patterns are shared. The analysis unit also develops a platform to store the data of abnormal sounds detected by the generation AI in the cloud and share it with other users. For example, sample data of abnormal sounds is uploaded to the cloud so that other users can access it. The analysis unit also develops a system to build a database of abnormal sounds by storing the results of abnormal sound detection in the cloud and sharing it with other users. For example, the characteristics of abnormal sounds are saved on the cloud so that other users can use them as a reference when detecting abnormal sounds. In this way, a database of abnormal sounds is built and the accuracy of abnormal sound detection is improved.

[0075] The analysis unit uses the emotion estimation function to analyze the emotional state of elderly people during conversations that may involve fraud, and can issue a warning if anxiety or fear is increasing. For example, the emotion estimation function can be used to build a system that analyzes the emotional state of elderly people during conversations that may involve fraud, and can issue a warning if anxiety or fear is increasing. For example, a warning can be automatically issued if the emotion score is high. The analysis unit also adds an emotion estimation function to the generation AI to analyze the emotional state of elderly people during conversations that may involve fraud in real time. For example, the emotional state can be monitored during the conversation, and a warning can be issued if anxiety or fear is increasing. The analysis unit also implements a deep learning model in the generation AI using the emotion estimation function to analyze the emotional state of elderly people during conversations that may involve fraud, and can issue a warning if anxiety or fear is increasing. For example, a warning can be automatically issued if the emotion score is high. This makes it possible to detect the risk of fraud early by analyzing the emotional state of elderly people and issue a warning.

[0076] When the analysis unit detects a conversation that may be fraudulent, it can analyze the context and background information of that conversation to assess the risk of fraud. For example, a system can be built to analyze the context and background information of a conversation when a conversation that may be fraudulent is detected, and the risk of fraud can be assessed. For example, the content before and after the conversation and related information can be analyzed. The analysis unit can also add a context analysis function to the generative AI, and analyze the context and background information when a conversation that may be fraudulent is detected. For example, the risk of fraud can be assessed based on the flow of the conversation and related information. The analysis unit can also implement a deep learning model in the generative AI to analyze the context and background information of that conversation when a conversation that may be fraudulent is detected, and assess the risk of fraud. For example, the content before and after the conversation and related information can be analyzed. This can accurately assess the risk of fraud, making it possible to protect elderly people from fraud.

[0077] The analysis unit uses the emotion estimation function to monitor the emotional reactions of elderly people during potentially fraudulent conversations in real time, and can notify family members if an abnormality is detected. For example, the emotion estimation function can be used to build a system that monitors the emotional reactions of elderly people during potentially fraudulent conversations in real time, and notifies family members if an abnormality is detected. For example, a notification can be automatically sent if the emotion score is high. The analysis unit also adds an emotion estimation function to the generation AI to monitor the emotional reactions of elderly people during potentially fraudulent conversations in real time. For example, the emotional state during the conversation can be monitored, and family members can be notified if an abnormality is detected. The analysis unit also implements a deep learning model in the generation AI using the emotion estimation function to monitor the emotional reactions of elderly people during potentially fraudulent conversations in real time, and notifies family members if an abnormality is detected. For example, a notification can be automatically sent if the emotion score is high. This makes it possible to monitor the emotional reactions of elderly people in real time, and quickly notify family members if an abnormality is detected.

[0078] When the analysis unit detects a conversation that may be fraudulent, it can compare it with past fraud cases and evaluate the similarity. For example, when a conversation that may be fraudulent is detected, it can build a system that compares it with past fraud cases and evaluate the similarity. For example, it can refer to a database of fraud cases and calculate the similarity. The analysis unit can also add a function to the generation AI that compares it with past fraud cases and evaluate the similarity when a conversation that may be fraudulent is detected. For example, it can calculate the similarity based on fraud case data and evaluate the risk of fraud. The analysis unit can also implement a deep learning model in the generation AI to compare it with past fraud cases and evaluate the similarity when a conversation that may be fraudulent is detected. For example, it can calculate the similarity using fraud case data. This makes it possible to compare it with past fraud cases and evaluate the risk of fraud with high accuracy.

[0079] The analysis unit enables the generation AI to automatically start recording when it detects a conversation that may be fraudulent, and save the recording as evidence. For example, a system can be built in which the generation AI automatically starts recording when it detects a conversation that may be fraudulent, and save the recording as evidence. For example, recording starts when a phrase that may be fraudulent is detected. The analysis unit also adds an automatic recording function to the generation AI, and starts recording when it detects a conversation that may be fraudulent. For example, if a conversation with a high risk of fraud is detected, recording is automatically started and saved as evidence. The analysis unit also develops an algorithm to enable the generation AI to automatically start recording when it detects a conversation that may be fraudulent, and save the recording as evidence. For example, recording starts when a phrase that may be fraudulent is detected. This makes it possible to automatically record conversations that may be fraudulent and save them as evidence, allowing for later verification.

[0080] The analysis unit can use the emotion estimation function to record the emotional reactions of elderly people when abnormal sounds are detected and monitor long-term changes in their emotions. For example, the emotion estimation function can be used to build a system that records the emotional reactions of elderly people when abnormal sounds are detected and monitor long-term changes in their emotions. For example, the emotion score can be recorded when abnormal sounds are detected and changes can be tracked over time. The analysis unit also adds an emotion estimation function to the generation AI to record the emotional reactions of elderly people when abnormal sounds are detected in real time. For example, the emotional state can be monitored when abnormal sounds are detected and long-term changes in emotions can be analyzed. The analysis unit also uses the emotion estimation function to implement a deep learning model in the generation AI to record the emotional reactions of elderly people when abnormal sounds are detected and monitor long-term changes in emotions. For example, the emotional state can be recorded when abnormal sounds are detected and long-term changes can be analyzed. This makes it possible to record the emotional reactions of elderly people and monitor long-term changes in emotions, thereby enabling early detection of abnormalities.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The environmental sound acquisition unit acquires ambient sounds. For example, it can constantly monitor the surrounding environmental sounds using a smartphone microphone. It can also detect everyday sounds, conversation sounds, television sounds, etc. Step 2: The analysis unit analyzes the audio data acquired by the environmental sound acquisition unit. For example, the generation AI analyzes the audio data and detects abnormal or specific sounds. The generation AI can analyze the audio data using text generation AI (e.g., LLM) or multimodal generation AI. The generation AI performs analysis based on prompts containing instructions for detecting abnormal or specific sounds. Step 3: The warning unit issues a warning if the analysis unit detects an abnormality. For example, if a long period of silence, a voice calling for help, or the sound of glass breaking is detected, the warning unit determines this to be an abnormality and issues a warning. This allows the monitoring system according to the embodiment to efficiently monitor elderly people. For example, if an abnormality is detected, the warning unit notifies family members or caregivers. The notification is sent via a smartphone app. It can also prompt appropriate action as necessary.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, 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 robot 414 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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. an environmental sound acquisition unit that acquires surrounding sounds; an analysis unit that analyzes the audio data acquired by the environmental sound acquisition unit; a warning unit that issues a warning when an abnormality is detected by the analysis unit. A system characterized by:

2. The environmental sound acquisition unit Using the multiple microphones on the smartphone, the direction of the sound source is identified and the location of the abnormal sound is identified.

2. The system of claim 1.

3. The analysis unit Introducing an algorithm to learn environmental sound patterns and distinguish between normal and abnormal sounds.

2. The system of claim 1.

4. The analysis unit The system estimates the emotional state of elderly people from environmental sounds and issues a warning when stress or anxiety increases.

2. The system of claim 1.

5. The environmental sound acquisition unit The smartphone camera is also used to detect the abnormality by combining the sound and video.

2. The system of claim 1.

Images