System

Abstract

An object of a system according to an embodiment is to analyze a use situation and a surrounding situation of a public telephone and perform an appropriate response in an emergency.SOLUTION: A system according to an embodiment includes a usage situation analysis unit, a surrounding situation analysis unit, and an emergency handling unit. A use situation analysis part analyzes the use situation of the public telephone. The surrounding situation analysis part analyzes a situation around the public telephone on the basis of the use situation of the public telephone analyzed by the use situation analysis part. The emergency response unit performs an appropriate response in an emergency on the basis of the situation around the public telephone analyzed by the surrounding situation 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 has had the problem of making it difficult to properly grasp the usage status of public telephones and the surrounding situation, and to respond quickly and appropriately in an emergency.

[0005] The system according to the embodiment aims to analyze the usage status of public telephones and the surrounding situation, and to take appropriate action in an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes a usage analysis unit, a surrounding situation analysis unit, and an emergency response unit. The usage analysis unit analyzes the usage of the public telephone. The surrounding situation analysis unit analyzes the situation around the public telephone based on the usage of the public telephone analyzed by the usage analysis unit. The emergency response unit takes appropriate action in an emergency based on the situation around the public telephone analyzed by the surrounding situation analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the usage status of public telephones and the surrounding situation, and take appropriate action in an emergency. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 public telephone system according to an embodiment of the present invention is a system that uses a generation AI to use public telephones as switches so that many people can use them in an emergency. This system uses a generation AI to analyze the usage status of public telephones and the surrounding situation, and responds appropriately, enabling a quick and effective response in emergencies and disasters. This allows the public telephone system to respond quickly and effectively in emergencies and disasters.

[0029] A public telephone system according to an embodiment includes a usage analysis unit, a surrounding situation analysis unit, and an emergency response unit. The usage analysis unit analyzes the usage of the public telephone. For example, the usage analysis unit monitors whether the public telephone is in use. The usage analysis unit can also analyze how frequently the public telephone is used. The usage analysis unit can also analyze user attributes. For example, the usage analysis unit estimates user attributes based on the number of calls and call duration. The surrounding situation analysis unit analyzes the situation around the public telephone based on the usage of the public telephone analyzed by the usage analysis unit. For example, the surrounding situation analysis unit monitors whether people are gathering around the public telephone. The surrounding situation analysis unit can also analyze the surrounding noise level. The surrounding situation analysis unit can also analyze the surrounding lighting situation. For example, the surrounding situation analysis unit analyzes surrounding environmental sounds and evaluates the noise level. The emergency response unit takes appropriate action in an emergency based on the situation around the public telephone analyzed by the surrounding situation analysis unit. For example, the emergency response unit automatically contacts the police or fire department when an emergency call is required. The emergency response unit can also provide information on evacuation sites and evacuation routes in the event of a disaster. The emergency response unit can also activate a specific service. For example, the emergency response unit activates a service that provides information on evacuation sites. This allows the public telephone system according to the embodiment to respond quickly and effectively in the event of an emergency or disaster. For example, the emergency response unit enables a quick response by making an emergency call quickly. The emergency response unit also supports safe evacuation by providing information on evacuation sites and evacuation routes.

[0030] The usage analysis unit can detect abnormal usage patterns by comparing them with usage history. The usage analysis unit, for example, compares them with past usage history to detect abnormal usage patterns. For example, if the usage frequency differs from the normal usage frequency or if usage is concentrated during a specific time period, it is determined to be abnormal. The usage analysis unit can also detect abnormal usage patterns using a machine learning model. For example, it compares the usage pattern with normal usage patterns and sets an abnormal value. This makes it possible to prevent fraudulent use by detecting abnormal usage patterns.

[0031] The usage analysis unit can analyze the characteristics of a user's voice to estimate their age group and gender, and optimize responses to specific demographics. The usage analysis unit, for example, analyzes the characteristics of a user's voice to estimate their age group and gender. For example, it can estimate whether the user is young or elderly, male or female, based on the pitch of the voice or characteristics of the speaking style. The usage analysis unit can also analyze the frequency components and waveform of the voice. For example, it can estimate the age group and gender using a machine learning model based on the voice features. This makes it possible to optimize responses to specific demographics.

[0032] The usage analysis unit can work in conjunction with the usage status of other public facilities to optimize comprehensive public services. For example, when analyzing the usage status of public telephones, the usage analysis unit can work in conjunction with the usage status of other public facilities to optimize comprehensive public services. For example, it can work in conjunction with the usage status of bus stops to encourage the use of public telephones during busy times. The usage analysis unit can also analyze the frequency of use of public facilities and the attributes of users. For example, it can optimize public services based on the frequency of facility use. This makes it possible to optimize comprehensive public services.

[0033] The usage analysis unit can work in conjunction with a security camera system to help prevent crime. The usage analysis unit, for example, works in conjunction with a security camera system to use the results of an analysis of public telephone usage to help prevent crime. For example, if an abnormal usage pattern is detected, the unit checks security camera footage. The usage analysis unit can also share video data with the security camera system and perform real-time monitoring. For example, if an abnormal situation is detected, the unit checks security camera footage in real time. This helps prevent crime.

[0034] The surrounding situation analysis unit can detect signs of a disaster based on the surrounding situation data and compare it with past disaster data. The surrounding situation analysis unit, for example, can detect signs of a disaster based on the surrounding situation data and compare it with past disaster data. For example, it can detect abnormal conditions that are precursors to earthquakes or floods. The surrounding situation analysis unit can also detect signs of a disaster based on weather data and past disaster records. For example, it can analyze patterns of weather changes and detect signs of a disaster. This allows for the detection of signs of a disaster to enable a rapid response.

[0035] The surrounding situation analysis unit can detect abnormalities by analyzing changes in environmental sound and light in detail. The surrounding situation analysis unit can, for example, analyze changes in environmental sound and light in detail to detect abnormalities. For example, it can detect sudden loud sounds or changes in light. The surrounding situation analysis unit can also detect abnormalities based on sound frequency analysis or changes in light intensity. For example, it can compare these with normal environmental sound and light patterns and set abnormal values. This allows for early detection of abnormalities by analyzing changes in environmental sound and light in detail.

[0036] The surrounding situation analysis unit can cooperate with the surrounding situations of other public facilities to strengthen comprehensive safety measures. For example, when analyzing the surrounding situation of a public telephone, the surrounding situation analysis unit cooperates with the surrounding situations of other public facilities to strengthen comprehensive safety measures. For example, it cooperates with the surrounding situation of a bus stop or a park to strengthen safety measures. The surrounding situation analysis unit can also analyze people flow data and environmental data of the surrounding situation of a public facility. For example, safety measures can be strengthened based on people flow data around the facility. This makes it possible to strengthen comprehensive safety measures.

[0037] The surrounding situation analysis unit can optimize responses to weather changes based on the results of the analysis of the surrounding situation and in cooperation with local weather information. The surrounding situation analysis unit can optimize responses to weather changes based on, for example, the results of the analysis of the surrounding situation and in cooperation with local weather information. For example, if a sudden change in weather is predicted, it will provide information on evacuation sites. The surrounding situation analysis unit can also optimize responses to weather changes based on data from the Japan Meteorological Agency and local weather observation data. For example, it will provide evacuation guidance based on weather data. This makes it possible to optimize responses to weather changes.

[0038] The emergency response department can predict situation patterns for specific time periods and days of the week based on surrounding situation data and prepare responses in advance. The emergency response department, for example, predicts situation patterns for specific time periods and days of the week based on surrounding situation data and prepares responses in advance. For example, if people tend to gather on weekends, it prepares responses in advance. The emergency response department can also predict situation patterns by comparing with past data or using machine learning models. For example, it predicts situation patterns for specific time periods and days of the week based on past data and prepares responses. In this way, predicting situation patterns for specific time periods and days of the week and preparing responses in advance enables rapid response.

[0039] The emergency response department can cooperate with security camera systems based on the results of the analysis of the surrounding situation and use this information to help prevent crime. The emergency response department can cooperate with security camera systems based on the results of the analysis of the surrounding situation and use this information to help prevent crime. For example, if an abnormal situation is detected, the security camera footage will be checked. The emergency response department can also share video data with the security camera system and perform real-time monitoring. For example, if an abnormal situation is detected, the security camera footage will be checked in real-time. This will help prevent crime.

[0040] The emergency response department can strengthen comprehensive safety measures by coordinating with the surrounding conditions of other public facilities. For example, when analyzing the surrounding conditions of a public telephone, the emergency response department can strengthen comprehensive safety measures by coordinating with the surrounding conditions of other public facilities. For example, it can strengthen safety measures by coordinating with the surrounding conditions of a bus stop or a park. The emergency response department can also analyze people flow data and environmental data of the surrounding conditions of public facilities. For example, it can strengthen safety measures based on people flow data around the facility. This makes it possible to strengthen comprehensive safety measures.

[0041] The emergency response department can optimize transportation means in emergencies by linking with local traffic information based on the results of the analysis of the surrounding situation. The emergency response department can, for example, optimize transportation means in emergencies by linking with local traffic information based on the results of the analysis of the surrounding situation. For example, if traffic congestion occurs, it will provide guidance on alternative routes. The emergency response department can also optimize transportation means based on traffic volume data and traffic accident data. For example, it can suggest optimal routes and provide criteria for selecting transportation means. This makes it possible to optimize transportation means in emergencies.

[0042] The emergency response department can detect malfunctions or abnormalities in public telephones and automatically contact maintenance staff if repairs are necessary. The emergency response department, for example, uses sensors to monitor in real time to detect malfunctions or abnormalities in public telephones. For example, it can detect declines in call quality or hardware abnormalities. The emergency response department can also detect malfunctions or abnormalities based on equipment operation logs and abnormal sensor values. For example, if an abnormal value is detected, it will automatically contact maintenance staff. This makes it possible to quickly detect malfunctions or abnormalities in public telephones and take action to repair them.

[0043] The emergency response department can perform preventive maintenance based on data related to the maintenance of public telephones. The emergency response department can perform preventive maintenance based on data related to the maintenance of public telephones. For example, they can analyze past failure data and perform maintenance before a failure occurs. The emergency response department can also perform preventive maintenance based on regular inspection schedules and preventive maintenance implementation standards. For example, they can inspect equipment regularly and perform maintenance in advance if signs of failure are found. This makes it possible to prevent public telephone failures through preventive maintenance.

[0044] The emergency response department can optimize the schedules of maintenance staff based on the maintenance data of public telephones. The emergency response department can, for example, optimize the schedules of maintenance staff based on the maintenance data of public telephones. For example, by concentrating staff in areas where there are many malfunctions. The emergency response department can also optimize the schedules based on work priorities and efficient route suggestions. For example, by suggesting efficient routes and setting work priorities. In this way, efficient maintenance can be achieved by optimizing the schedules of maintenance staff.

[0045] The emergency response department can develop tools to improve maintenance efficiency based on data related to the maintenance of public telephones. The emergency response department can develop tools to improve maintenance efficiency based on data related to the maintenance of public telephones. For example, it can provide a diagnostic tool to identify the cause of a malfunction. The emergency response department can also improve maintenance efficiency by using a work support app or an efficiency algorithm. For example, it can use a work support app to guide work procedures and an efficiency algorithm to optimize work. In this way, developing tools to improve maintenance efficiency enables fast and efficient maintenance.

[0046] The emergency response department can formulate a strategy to reduce maintenance costs based on data related to the maintenance of public telephones. The emergency response department, for example, formulates a strategy to reduce maintenance costs based on data related to the maintenance of public telephones. For example, parts that frequently break down can be identified and preventive maintenance can be performed. The emergency response department can also formulate a strategy based on priorities for cost reduction and efficient resource allocation. For example, priorities can be set for cost reduction and efficient resource allocation can be performed. In this way, efficient cost management can be achieved by formulating a strategy to reduce maintenance costs.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The public telephone system can also be equipped with a health monitoring unit that monitors the user's health status. For example, when a user uses a public telephone, the health monitoring unit analyzes the user's voice and breathing patterns to estimate the user's health status. If the health monitoring unit detects an abnormal health status, it can notify the emergency response department and take appropriate action. This allows the user's health status to be monitored in real time, enabling a rapid response in the event of an emergency.

[0049] The public telephone system can also be equipped with a location information provider that acquires the user's location information and guides them to the nearest public facilities and services. For example, when a user uses a public telephone, the location information provider can guide them to the nearest hospital or police station based on their current location. The location information provider can also analyze the user's travel route and suggest the optimal route. This allows users to quickly find the public facilities and services they need.

[0050] The public telephone system can also be equipped with an advertisement serving unit that analyzes the characteristics of the user's voice and serves advertisements according to the user's preferences. For example, advertisements for the latest fashion and music can be served to young users, while advertisements for health foods and nursing care services can be served to elderly users. This makes it possible to serve advertisements according to the user's attributes and maximize the effectiveness of advertising.

[0051] The public telephone system can also be equipped with a multilingual support unit that analyzes the characteristics of the user's voice, automatically determines the user's language, and responds in the appropriate language. For example, if the user speaks English, guidance can be provided in English. If the user speaks Chinese, guidance can also be provided in Chinese. This makes it possible to respond appropriately to foreign users.

[0052] The public telephone system can also be equipped with a health advice provider that analyzes the characteristics of the user's voice, estimates the user's health condition, and provides health advice. For example, if the user's voice is hoarse, advice to drink more water can be provided. Also, if the user's breathing is rough, advice to take deep breaths can be provided. This makes it possible to provide appropriate advice according to the user's health condition and support health management.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The usage analysis unit analyzes the usage of public telephones. For example, it monitors whether public telephones are in use and analyzes how frequently they are used. It also estimates user attributes based on the number of calls and call duration. Step 2: The surrounding situation analysis unit analyzes the situation around the payphone based on the usage status of the payphone analyzed by the usage status analysis unit. For example, it monitors whether people are gathering around the payphone and analyzes the surrounding noise level and lighting conditions. Step 3: The emergency response unit takes appropriate action in the event of an emergency based on the situation around the pay phone analyzed by the surrounding situation analysis unit. For example, if an emergency call is required, the unit will automatically contact the police or fire department, and in the event of a disaster, the unit will provide information on evacuation sites and evacuation routes.

[0055] (Example 2) The public telephone system according to an embodiment of the present invention is a system that uses a generation AI to use public telephones as switches so that many people can use them in an emergency. This system uses a generation AI to analyze the usage status of public telephones and the surrounding situation, and responds appropriately, enabling a quick and effective response in emergencies and disasters. This allows the public telephone system to respond quickly and effectively in emergencies and disasters.

[0056] A public telephone system according to an embodiment includes a usage analysis unit, a surrounding situation analysis unit, and an emergency response unit. The usage analysis unit analyzes the usage of the public telephone. For example, the usage analysis unit monitors whether the public telephone is in use. The usage analysis unit can also analyze how frequently the public telephone is used. The usage analysis unit can also analyze user attributes. For example, the usage analysis unit estimates user attributes based on the number of calls and call duration. The surrounding situation analysis unit analyzes the situation around the public telephone based on the usage of the public telephone analyzed by the usage analysis unit. For example, the surrounding situation analysis unit monitors whether people are gathering around the public telephone. The surrounding situation analysis unit can also analyze the surrounding noise level. The surrounding situation analysis unit can also analyze the surrounding lighting situation. For example, the surrounding situation analysis unit analyzes surrounding environmental sounds and evaluates the noise level. The emergency response unit takes appropriate action in an emergency based on the situation around the public telephone analyzed by the surrounding situation analysis unit. For example, the emergency response unit automatically contacts the police or fire department when an emergency call is required. The emergency response unit can also provide information on evacuation sites and evacuation routes in the event of a disaster. The emergency response unit can also activate a specific service. For example, the emergency response unit activates a service that provides information on evacuation sites. This allows the public telephone system according to the embodiment to respond quickly and effectively in the event of an emergency or disaster. For example, the emergency response unit enables a quick response by making an emergency call quickly. The emergency response unit also supports safe evacuation by providing information on evacuation sites and evacuation routes.

[0057] The usage status analysis unit analyzes the user's tone of voice and speaking style to estimate their emotions, and prioritizes responses when they are under high stress or tension. The usage status analysis unit, for example, analyzes the user's tone of voice and speaking style to estimate their emotions. For example, if the user's voice is trembling or they are speaking quickly, it determines that the user is under high stress or tension and prioritizes responses. The usage status analysis unit can also estimate emotions using a voice analysis algorithm. For example, it analyzes the pitch and speed of the voice and assigns an emotion label. This enables a quick response according to the user's emotions.

[0058] The usage analysis unit can detect abnormal usage patterns by comparing them with usage history. The usage analysis unit, for example, compares them with past usage history to detect abnormal usage patterns. For example, if the usage frequency differs from the normal usage frequency or if usage is concentrated during a specific time period, it is determined to be abnormal. The usage analysis unit can also detect abnormal usage patterns using a machine learning model. For example, it compares the usage pattern with normal usage patterns and sets an abnormal value. This makes it possible to prevent fraudulent use by detecting abnormal usage patterns.

[0059] The usage analysis unit can analyze the characteristics of a user's voice to estimate their age group and gender, and optimize responses to specific demographics. The usage analysis unit, for example, analyzes the characteristics of a user's voice to estimate their age group and gender. For example, it can estimate whether the user is young or elderly, male or female, based on the pitch of the voice or characteristics of the speaking style. The usage analysis unit can also analyze the frequency components and waveform of the voice. For example, it can estimate the age group and gender using a machine learning model based on the voice features. This makes it possible to optimize responses to specific demographics.

[0060] The usage analysis unit can work in conjunction with the usage status of other public facilities to optimize comprehensive public services. For example, when analyzing the usage status of public telephones, the usage analysis unit can work in conjunction with the usage status of other public facilities to optimize comprehensive public services. For example, it can work in conjunction with the usage status of bus stops to encourage the use of public telephones during busy times. The usage analysis unit can also analyze the frequency of use of public facilities and the attributes of users. For example, it can optimize public services based on the frequency of facility use. This makes it possible to optimize comprehensive public services.

[0061] The usage analysis unit can work in conjunction with a security camera system to help prevent crime. The usage analysis unit, for example, works in conjunction with a security camera system to use the results of an analysis of public telephone usage to help prevent crime. For example, if an abnormal usage pattern is detected, the unit checks security camera footage. The usage analysis unit can also share video data with the security camera system and perform real-time monitoring. For example, if an abnormal situation is detected, the unit checks security camera footage in real time. This helps prevent crime.

[0062] The usage status analysis unit can use the emotion estimation function to detect a user's anxiety or confusion in real time and provide appropriate support. The usage status analysis unit, for example, analyzes the user's tone of voice and speaking style to detect anxiety or confusion. For example, anxiety or confusion is detected when the user's voice is trembling or the speaking style is unnatural. The usage status analysis unit can also analyze emotions in real time using an emotion estimation algorithm. For example, an emotion score can be calculated using a voice analysis algorithm to provide appropriate support. This makes it possible to provide appropriate support for the user's anxiety or confusion.

[0063] The surrounding situation analysis unit can estimate the emotions of people in the vicinity and raise the alert level if they are highly tense or anxious. The surrounding situation analysis unit, for example, analyzes the tone of voice and speaking style of people in the vicinity to estimate their emotions. For example, if their voices are trembling or they are speaking quickly, it can determine that they are highly tense or anxious and raise the alert level. The surrounding situation analysis unit can also estimate emotions using facial expression analysis and voice analysis. For example, it can estimate emotions based on changes in facial expression and voice characteristics. This makes it possible to adjust the alert level according to the emotions of people in the vicinity.

[0064] The surrounding situation analysis unit can detect signs of a disaster based on the surrounding situation data and compare it with past disaster data. The surrounding situation analysis unit, for example, can detect signs of a disaster based on the surrounding situation data and compare it with past disaster data. For example, it can detect abnormal conditions that are precursors to earthquakes or floods. The surrounding situation analysis unit can also detect signs of a disaster based on weather data and past disaster records. For example, it can analyze patterns of weather changes and detect signs of a disaster. This allows for the detection of signs of a disaster to enable a rapid response.

[0065] The surrounding situation analysis unit can detect abnormalities by analyzing changes in environmental sound and light in detail. The surrounding situation analysis unit can, for example, analyze changes in environmental sound and light in detail to detect abnormalities. For example, it can detect sudden loud sounds or changes in light. The surrounding situation analysis unit can also detect abnormalities based on sound frequency analysis or changes in light intensity. For example, it can compare these with normal environmental sound and light patterns and set abnormal values. This allows for early detection of abnormalities by analyzing changes in environmental sound and light in detail.

[0066] The surrounding situation analysis unit can cooperate with the surrounding situations of other public facilities to strengthen comprehensive safety measures. For example, when analyzing the surrounding situation of a public telephone, the surrounding situation analysis unit cooperates with the surrounding situations of other public facilities to strengthen comprehensive safety measures. For example, it cooperates with the surrounding situation of a bus stop or a park to strengthen safety measures. The surrounding situation analysis unit can also analyze people flow data and environmental data of the surrounding situation of a public facility. For example, safety measures can be strengthened based on people flow data around the facility. This makes it possible to strengthen comprehensive safety measures.

[0067] The surrounding situation analysis unit can optimize responses to weather changes based on the results of the analysis of the surrounding situation and in cooperation with local weather information. The surrounding situation analysis unit can optimize responses to weather changes based on, for example, the results of the analysis of the surrounding situation and in cooperation with local weather information. For example, if a sudden change in weather is predicted, it will provide information on evacuation sites. The surrounding situation analysis unit can also optimize responses to weather changes based on data from the Japan Meteorological Agency and local weather observation data. For example, it will provide evacuation guidance based on weather data. This makes it possible to optimize responses to weather changes.

[0068] The emergency response department can analyze the content of conversations between people in the vicinity and determine the level of urgency. The emergency response department, for example, analyzes the content of conversations between people in the vicinity and determines the level of urgency. For example, if an emergency call is required, the department immediately contacts the police or fire department. The emergency response department can also analyze the content of conversations using voice recognition technology or natural language processing technology. For example, the department scores the level of urgency based on the analysis results of the content of conversations. This enables a rapid response by determining the level of urgency based on the content of conversations between people in the vicinity.

[0069] The emergency response department can predict situation patterns for specific time periods and days of the week based on surrounding situation data and prepare responses in advance. The emergency response department, for example, predicts situation patterns for specific time periods and days of the week based on surrounding situation data and prepares responses in advance. For example, if people tend to gather on weekends, it prepares responses in advance. The emergency response department can also predict situation patterns by comparing with past data or using machine learning models. For example, it predicts situation patterns for specific time periods and days of the week based on past data and prepares responses. In this way, predicting situation patterns for specific time periods and days of the week and preparing responses in advance enables rapid response.

[0070] The emergency response department can cooperate with security camera systems based on the results of the analysis of the surrounding situation and use this information to help prevent crime. The emergency response department can cooperate with security camera systems based on the results of the analysis of the surrounding situation and use this information to help prevent crime. For example, if an abnormal situation is detected, the security camera footage will be checked. The emergency response department can also share video data with the security camera system and perform real-time monitoring. For example, if an abnormal situation is detected, the security camera footage will be checked in real-time. This will help prevent crime.

[0071] The emergency response department can strengthen comprehensive safety measures by coordinating with the surrounding conditions of other public facilities. For example, when analyzing the surrounding conditions of a public telephone, the emergency response department can strengthen comprehensive safety measures by coordinating with the surrounding conditions of other public facilities. For example, it can strengthen safety measures by coordinating with the surrounding conditions of a bus stop or a park. The emergency response department can also analyze people flow data and environmental data of the surrounding conditions of public facilities. For example, it can strengthen safety measures based on people flow data around the facility. This makes it possible to strengthen comprehensive safety measures.

[0072] The emergency response department can optimize transportation means in emergencies by linking with local traffic information based on the results of the analysis of the surrounding situation. The emergency response department can, for example, optimize transportation means in emergencies by linking with local traffic information based on the results of the analysis of the surrounding situation. For example, if traffic congestion occurs, it will provide guidance on alternative routes. The emergency response department can also optimize transportation means based on traffic volume data and traffic accident data. For example, it can suggest optimal routes and provide criteria for selecting transportation means. This makes it possible to optimize transportation means in emergencies.

[0073] The emergency response unit can use the emotion estimation function to analyze the emotional states of people around them in real time and provide appropriate responses. The emergency response unit, for example, analyzes the tone of voice and speaking style of people around them to analyze their emotional states in real time. For example, if a person's voice is trembling or their speaking style is unnatural, stress or anxiety can be detected. The emergency response unit can also analyze the emotional states using an emotion estimation algorithm. For example, a voice analysis algorithm can be used to calculate an emotion score and provide appropriate responses. This makes it possible to provide appropriate responses according to the emotional states of people around them.

[0074] The emergency response department can detect malfunctions or abnormalities in public telephones and automatically contact maintenance staff if repairs are necessary. The emergency response department, for example, uses sensors to monitor in real time to detect malfunctions or abnormalities in public telephones. For example, it can detect declines in call quality or hardware abnormalities. The emergency response department can also detect malfunctions or abnormalities based on equipment operation logs and abnormal sensor values. For example, if an abnormal value is detected, it will automatically contact maintenance staff. This makes it possible to quickly detect malfunctions or abnormalities in public telephones and take action to repair them.

[0075] The emergency response department can perform preventive maintenance based on data related to the maintenance of public telephones. The emergency response department can perform preventive maintenance based on data related to the maintenance of public telephones. For example, they can analyze past failure data and perform maintenance before a failure occurs. The emergency response department can also perform preventive maintenance based on regular inspection schedules and preventive maintenance implementation standards. For example, they can inspect equipment regularly and perform maintenance in advance if signs of failure are found. This makes it possible to prevent public telephone failures through preventive maintenance.

[0076] The emergency response department can optimize the schedules of maintenance staff based on the maintenance data of public telephones. The emergency response department can, for example, optimize the schedules of maintenance staff based on the maintenance data of public telephones. For example, by concentrating staff in areas where there are many malfunctions. The emergency response department can also optimize the schedules based on work priorities and efficient route suggestions. For example, by suggesting efficient routes and setting work priorities. In this way, efficient maintenance can be achieved by optimizing the schedules of maintenance staff.

[0077] The emergency response department can develop tools to improve maintenance efficiency based on data related to the maintenance of public telephones. The emergency response department can develop tools to improve maintenance efficiency based on data related to the maintenance of public telephones. For example, it can provide a diagnostic tool to identify the cause of a malfunction. The emergency response department can also improve maintenance efficiency by using a work support app or an efficiency algorithm. For example, it can use a work support app to guide work procedures and an efficiency algorithm to optimize work. In this way, developing tools to improve maintenance efficiency enables fast and efficient maintenance.

[0078] The emergency response department can formulate a strategy to reduce maintenance costs based on data related to the maintenance of public telephones. The emergency response department, for example, formulates a strategy to reduce maintenance costs based on data related to the maintenance of public telephones. For example, parts that frequently break down can be identified and preventive maintenance can be performed. The emergency response department can also formulate a strategy based on priorities for cost reduction and efficient resource allocation. For example, priorities can be set for cost reduction and efficient resource allocation can be performed. In this way, efficient cost management can be achieved by formulating a strategy to reduce maintenance costs.

[0079] The emergency response department can use the emotion estimation function to analyze the stress and fatigue of maintenance staff in real time and provide appropriate support. The emergency response department can, for example, analyze the tone of voice and speaking style of the maintenance staff to analyze stress and fatigue in real time. For example, stress and fatigue can be detected if the voice is trembling or the speaking style is unnatural. The emergency response department can also analyze stress and fatigue using an emotion estimation algorithm. For example, an emotion score can be calculated using a voice analysis algorithm and appropriate support can be provided. This makes it possible to manage the health of maintenance staff by analyzing their stress and fatigue in real time and providing appropriate support.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The public telephone system can also be equipped with a health monitoring unit that monitors the user's health status. For example, when a user uses a public telephone, the health monitoring unit analyzes the user's voice and breathing patterns to estimate the user's health status. If the health monitoring unit detects an abnormal health status, it can notify the emergency response department and take appropriate action. This allows the user's health status to be monitored in real time, enabling a rapid response in the event of an emergency.

[0082] The public telephone system can further include an entertainment provider that estimates the user's emotions and provides appropriate music and messages based on the estimated emotions. For example, if the user is feeling stressed, relaxing music can be provided. If the user is sad, an encouraging message can be provided. This makes it possible to provide entertainment that matches the user's emotions and improve the user's mood.

[0083] The public telephone system can also be equipped with a location information provider that acquires the user's location information and guides them to the nearest public facilities and services. For example, when a user uses a public telephone, the location information provider can guide them to the nearest hospital or police station based on their current location. The location information provider can also analyze the user's travel route and suggest the optimal route. This allows users to quickly find the public facilities and services they need.

[0084] The public telephone system can further include a counseling unit that estimates the user's emotions and provides appropriate advice and support based on the estimated emotions. For example, if the user is feeling anxious, the system can provide reassuring advice. If the user is feeling angry, the system can also suggest ways to calm down. This makes it possible to provide counseling tailored to the user's emotions and provide psychological support to the user.

[0085] The public telephone system can also be equipped with an advertisement serving unit that analyzes the characteristics of the user's voice and serves advertisements according to the user's preferences. For example, advertisements for the latest fashion and music can be served to young users, while advertisements for health foods and nursing care services can be served to elderly users. This makes it possible to serve advertisements according to the user's attributes and maximize the effectiveness of advertising.

[0086] The public telephone system can further include an emergency contact suggestion unit that estimates the user's emotions and suggests appropriate emergency contact information based on the estimated emotions. For example, if the user is in a panic, the system can suggest contact information for family and friends. If the user is calm, the system can also suggest contact information for the police and fire department. This makes it possible to suggest appropriate emergency contact information according to the user's emotions and support a quick response.

[0087] The public telephone system can also be equipped with a multilingual support unit that analyzes the characteristics of the user's voice, automatically determines the user's language, and responds in the appropriate language. For example, if the user speaks English, guidance can be provided in English. If the user speaks Chinese, guidance can also be provided in Chinese. This makes it possible to respond appropriately to foreign users.

[0088] The public telephone system can further include an emergency response unit that estimates the user's emotions and takes appropriate emergency measures based on the estimated emotions. For example, if the user feels scared, the system can automatically contact the police. Also, if the user is confused, the system can provide a message to calm the user. This allows for a quick emergency response that is tailored to the user's emotions.

[0089] The public telephone system can also be equipped with a health advice provider that analyzes the characteristics of the user's voice, estimates the user's health condition, and provides health advice. For example, if the user's voice is hoarse, advice to drink more water can be provided. Also, if the user's breathing is rough, advice to take deep breaths can be provided. This makes it possible to provide appropriate advice according to the user's health condition and support health management.

[0090] The public telephone system can further include a relaxation suggestion unit that estimates the user's emotions and suggests appropriate relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest deep breathing or meditation. Also, if the user is tired, the system can suggest taking a short break. This makes it possible to suggest relaxation methods that suit the user's emotions and help the user relax.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The usage analysis unit analyzes the usage of public telephones. For example, it monitors whether public telephones are in use and analyzes how frequently they are used. It also estimates user attributes based on the number of calls and call duration. Step 2: The surrounding situation analysis unit analyzes the situation around the payphone based on the usage status of the payphone analyzed by the usage status analysis unit. For example, it monitors whether people are gathering around the payphone and analyzes the surrounding noise level and lighting conditions. Step 3: The emergency response unit takes appropriate action in the event of an emergency based on the situation around the pay phone analyzed by the surrounding situation analysis unit. For example, if an emergency call is required, the unit will automatically contact the police or fire department, and in the event of a disaster, the unit will provide information on evacuation sites and evacuation routes.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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 AI 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a usage status analysis unit that analyzes the usage status of public telephones; a surrounding situation analysis unit that analyzes the situation around the public telephone based on the usage situation of the public telephone analyzed by the usage situation analysis unit; an emergency response unit that takes appropriate action in an emergency based on the situation around the public telephone analyzed by the surrounding situation analysis unit. A system characterized by:

2. The usage status analysis unit Analyzes the user's tone of voice and speaking style to estimate their emotions and prioritize responses when stress or tension is high 2. The system of claim 1.

3. The usage status analysis unit Linking with the above-mentioned usage status of other public facilities to optimize comprehensive public services 2. The system of claim 1.

4. The surrounding situation analysis unit Estimate the emotions of those around you and raise your alert level if you feel high levels of tension or anxiety 2. The system of claim 1.

5. The surrounding situation analysis unit Detailed analysis of environmental sounds and changes in light to detect abnormalities 2. The system of claim 1.

6. The emergency response department: Analyze the content of conversations between people in the vicinity and determine the level of urgency 2. The system of claim 1.

7. The emergency response department: Based on the results of the surrounding situation analysis, the system will link with local traffic information to optimize transportation options in the event of an emergency.

2. The system of claim 1.

8. The emergency response department: Analyze maintenance staff stress and fatigue in real time and provide appropriate support 2. The system of claim 1.

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