System

Abstract

An object of the system according to the embodiment is to collect, analyze, and provide information on schools, hospitals, supermarkets, restaurants, signals, pedestrian crossings, road conditions, and the like in real time.SOLUTION: A system includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects information on schools, hospitals, supermarkets, restaurants, signals, pedestrian crossings, and road conditions in real time. The analysis unit analyzes the information collected by the information collection unit. The providing unit provides the user with the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately collect, analyze, and provide real-time information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, road conditions, etc., and there is room for improvement.

[0005] The system according to the embodiment aims to collect, analyze, and provide information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, road conditions, and the like in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, and road conditions in real time. The analysis unit analyzes the information collected by the information collection unit. The provision unit provides the information analyzed by the analysis unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can collect, analyze, and provide information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, road conditions, and the like in real time. [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) A real-time information providing system according to an embodiment of the present invention is a system that updates information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, road conditions, etc. in real time and provides it as an application. As a result, the real-time information providing system can provide support from various angles to address national challenges such as a declining birthrate and aging society, rising prices, food shortages due to climate change, and COVID-19.

[0029] A real-time information provision system according to an embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, and road conditions in real time. For example, the information collection unit collects information on traffic conditions, traffic light changes, and road congestion in real time. The information collection unit can also collect information on medical institutions and supermarkets in real time. Furthermore, the information collection unit can collect traffic information and disaster information in cooperation with government agencies and local governments. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit analyzes locations and time periods with a high risk of traffic accidents and issues a warning to the user. The analysis unit can also perform emotion analysis on data collected from each information source using a generative AI and dynamically change the priority of information based on the user's emotions. Furthermore, the analysis unit can use an emotion estimation function to monitor the user's emotions when receiving information in real time and provide information designed to elicit positive emotions. The provision unit provides the user with the information analyzed by the analysis unit. For example, the provision unit proposes services and support measures for the elderly and provides measures to combat rising prices. The providing unit can also quickly provide necessary medical services and food supplies. Furthermore, the providing unit can collect information from different regions and countries and provide information from a global perspective. As a result, the real-time information providing system according to the embodiment can provide support from multiple angles to address national issues such as a declining birthrate and aging society, rising prices, food shortages due to climate change, and COVID-19. For example, it can reduce the risk of traffic accidents and ensure the safety of the elderly and children. It can also provide measures to address rising prices and food shortages and support the lives of residents. Furthermore, it can support a rapid response in the event of a disaster, ensuring the safety and security of disaster victims.

[0030] The information collection unit can collect traffic conditions, traffic light changes, and road congestion conditions in real time and provide them to the user. The information collection unit, for example, collects traffic conditions in real time and provides them to the user. For example, it collects traffic volume, congestion information, accident information, etc. The information collection unit also collects traffic light changes in real time and provides them to the user. For example, it collects changes in traffic light color and timing. The information collection unit also collects road congestion conditions in real time and provides them to the user. For example, it collects vehicle density, average speed, length of congestion, etc. In this way, traffic conditions, traffic light changes, and road congestion conditions can be grasped in real time and provided to the user, thereby reducing the risk of traffic accidents.

[0031] The analysis unit can analyze locations or time periods where the risk of traffic accidents is high and issue a warning to the user. The analysis unit, for example, analyzes locations where the risk of traffic accidents is high and issues a warning to the user. For example, the analysis unit identifies risks based on past accident data, traffic volume, weather conditions, etc. The analysis unit also analyzes time periods where the risk of traffic accidents is high and issues a warning to the user. For example, the analysis unit analyzes the accident occurrence rate during a specific time period. The analysis unit also analyzes a combination of locations and time periods where the risk of traffic accidents is high and issues a warning to the user. For example, the analysis unit identifies risks at specific intersections and issues a warning to the user. In this way, by analyzing locations and time periods where the risk of traffic accidents is high and issuing a warning to the user, accidents can be prevented from occurring.

[0032] The provision unit can propose services or support measures for the elderly and provide measures to combat rising prices. The provision unit, for example, proposes medical services for the elderly. For example, it provides information on specific medical departments and medical institutions. The provision unit also proposes shopping support for the elderly. For example, it provides information on specific supermarkets and products. The provision unit also proposes transportation support for the elderly. For example, it provides information on specific transportation methods and routes. The provision unit also provides measures to combat rising prices. For example, it provides discount information, price comparisons, money-saving tips, etc. In this way, by proposing services and support measures for the elderly and providing measures to combat rising prices, the quality of life in a society with a declining birthrate and an aging population can be improved.

[0033] The information collection unit collects information on medical institutions or supermarkets in real time, and can provide necessary medical services or food promptly. The information collection unit, for example, collects information on medical institutions in real time, and provides necessary medical services promptly. For example, it collects information such as consultation hours, medical specialties, and waiting times. The information collection unit also collects information on supermarkets in real time, and provides necessary food promptly. For example, it collects information such as stock status, business hours, and sale information. The information collection unit also combines and collects information on medical institutions and supermarkets, and provides necessary medical services and food promptly. For example, it collects information on medical institutions and supermarkets in a specific area and provides it to the user. This improves life support services by collecting information on medical institutions and supermarkets in real time and providing necessary medical services and food promptly.

[0034] The information collection unit can work with government agencies or local governments to collect and analyze traffic or disaster information and provide it to users. For example, the information collection unit works with government agencies to collect traffic information and provide it to users. For example, it collects information on traffic congestion, accidents, road construction, etc. The information collection unit also works with local governments to collect disaster information and provide it to users. For example, it collects earthquake alerts, flood warnings, evacuation shelter information, etc. The information collection unit also works with both government agencies and local governments to collect traffic and disaster information and provide it to users. For example, it collects a combination of traffic and disaster information for a specific area and provides it to users. In this way, by working with government agencies and local governments to collect and analyze traffic and disaster information and provide it to users, the system aims to achieve risk prediction and zero accidents through public-private partnerships.

[0035] The information collection unit can use drones or autonomous vehicles to collect on-site video in real time, and AI can analyze and update the information. For example, the information collection unit can use drones to collect video of traffic conditions and disaster sites in real time, and AI can analyze and update the information. For example, traffic congestion and the progression of a disaster can be grasped in real time. The information collection unit can also use autonomous vehicles to collect road congestion and traffic light changes in real time, and AI can analyze and update the information. For example, it can identify areas with a high risk of traffic accidents. The information collection unit can also use AI to analyze video data collected from drones or autonomous vehicles, and update the information provided to users in real time. For example, it can quickly provide evacuation routes in the event of a disaster. In this way, by collecting on-site video in real time using drones or autonomous vehicles, and AI can analyze and update the information, it is possible to quickly grasp the local situation and take appropriate measures.

[0036] The information collection unit can use IoT devices to collect environmental data from homes or public facilities in real time and provide it to users. For example, the information collection unit uses IoT devices to collect environmental data such as temperature, humidity, and air quality in the home in real time, and AI analyzes the data and provides it to users. For example, the information collection unit provides advice on maintaining a comfortable indoor environment. The information collection unit also collects environmental data from IoT devices installed in public facilities, analyzes it using AI, and updates the information in real time. For example, the information collection unit monitors the air quality in libraries and hospitals and proposes appropriate measures. The information collection unit also uses IoT devices to collect environmental data from homes and public facilities, and AI analyzes the data and provides it to users. For example, it proposes comfortable environmental settings in response to changes in temperature and humidity. As a result, the information collection unit can use IoT devices to collect environmental data from homes and public facilities in real time and provide it to users, thereby providing advice on maintaining a comfortable environment.

[0037] The information collection unit can collect weather or natural disaster forecast information in real time, and have AI analyze the information and provide it to the user. For example, the information collection unit collects weather information in real time, and AI analyzes it and provides it to the user. For example, advice is given on how to respond to sudden weather changes. The information collection unit also collects natural disaster forecast information in real time, and AI analyzes it and provides it to the user. For example, it predicts the risk of earthquakes and floods and issues evacuation instructions. The information collection unit also collects weather and natural disaster forecast information, and AI analyzes it and updates the information in real time. For example, it provides a typhoon path forecast and encourages evacuation preparations. In this way, weather and natural disaster forecast information can be collected in real time, and AI analyzes it and provides it to the user, allowing for quick responses to sudden weather changes and natural disasters.

[0038] The information collection unit can collect information from different regions or countries and provide information from a global perspective. For example, the information collection unit collects traffic information from different regions or countries in real time, which AI analyzes and provides to users. For example, it provides local traffic conditions for overseas travelers. The information collection unit also collects disaster information from different regions or countries from a global perspective, which AI analyzes and provides to users. For example, it predicts the risk of natural disasters overseas and issues warnings to travelers. The information collection unit also collects environmental data from different regions or countries, which AI analyzes and updates the information in real time. For example, it monitors the impact of international climate change and proposes countermeasures. In this way, by collecting information from different regions or countries and providing information from a global perspective, it is possible to provide useful information to users.

[0039] The analysis unit can use the generation AI to combine past accident data with real-time information to predict future accident risks and issue warnings to users. The analysis unit, for example, uses the generation AI to combine past traffic accident data with real-time traffic information to predict future accident risks. For example, it predicts the risk of an accident at a specific intersection and issues a warning to the user. The analysis unit also analyzes past accident data and real-time road conditions, and the generation AI predicts future accident risks. For example, it predicts the risk of an accident during a specific time period and issues a warning to the user. The analysis unit also uses the generation AI to combine past accident data with real-time environmental data to predict future accident risks. For example, it predicts the risk of an accident during bad weather and issues a warning to the user. In this way, accidents can be prevented from occurring by using the generation AI to combine past accident data with real-time information to predict future accident risks and issue a warning to the user.

[0040] The analysis unit uses AI to control traffic signals in real time, optimizing traffic flow and reducing the risk of accidents. For example, the analysis unit uses AI to control traffic signals in real time and optimize traffic flow. For example, it adjusts the timing of signals according to traffic volume to alleviate congestion. The analysis unit also uses AI to control traffic signals in real time and reduce the risk of accidents. For example, it adjusts the timing of signals at intersections to reduce the risk of traffic accidents. The analysis unit also builds a system in which AI controls traffic signals in real time and optimizes traffic flow. For example, it adjusts signals to give priority to emergency vehicles. In this way, it uses AI to control traffic signals in real time, optimizes traffic flow, and reduces the risk of accidents, thereby preventing traffic accidents.

[0041] The analysis unit uses AI to analyze the behavioral patterns of pedestrians or drivers, detect dangerous behavior, and issue a warning. The analysis unit, for example, uses AI to analyze the behavioral patterns of pedestrians and drivers and detect dangerous behavior. For example, it detects ignoring traffic lights or sudden lane changes and issues a warning to the user. The analysis unit also builds a system in which AI analyzes the behavioral patterns of pedestrians and drivers in real time and detects dangerous behavior. For example, it detects pedestrians walking while looking at their smartphones and issues a warning. The analysis unit also uses AI to analyze the behavioral patterns of pedestrians and drivers, detect dangerous behavior, and issue a warning. For example, it detects speeding or sudden braking and issues a warning to the driver. In this way, accidents can be prevented by analyzing the behavioral patterns of pedestrians and drivers, detecting dangerous behavior, and issuing a warning.

[0042] The analysis unit can expand the scope of risk prediction to also predict natural disasters or crime risks. The analysis unit, for example, uses AI to predict the risk of natural disasters and issue warnings to users. For example, it predicts the risk of earthquakes or floods and issues evacuation instructions. The analysis unit also builds a system in which AI predicts crime risks and issues warnings to users. For example, it predicts the risk of crime in a specific area and issues a warning. The analysis unit also uses AI to predict natural disasters and crime risks and issues a warning to users. For example, it provides information on typhoon path predictions and high-crime areas. This expands the scope of risk prediction to also predict natural disasters and crime risks, ensuring the safety of users.

[0043] The analysis unit can predict danger for different modes of transportation (bicycles, electric scooters, etc.) and provide comprehensive safety measures. For example, the analysis unit uses AI to predict danger and issue warnings to users of bicycles and electric scooters. For example, it predicts danger on roads with heavy traffic and issues warnings to users. The analysis unit also builds a system in which AI predicts danger for different modes of transportation and provides comprehensive safety measures. For example, it predicts congestion in bicycle lanes and issues warnings to users. The analysis unit also uses AI to predict danger for users of bicycles and electric scooters and provides comprehensive safety measures. For example, it issues warnings when using the vehicles at night. In this way, by predicting danger for different modes of transportation and providing comprehensive safety measures, safety is ensured for all modes of transportation.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The real-time information providing system can further include a health management unit that monitors the user's health condition. For example, the health management unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and issues an alert if an abnormality is detected. The health management unit can also monitor the user's exercise level and sleep status and provide advice on maintaining health. Furthermore, the health management unit can record the user's diet and make suggestions for improving nutritional balance. This allows the real-time information providing system to comprehensively manage the user's health condition and support health maintenance.

[0046] The real-time information providing system may further include an entertainment unit that provides entertainment information. For example, the entertainment unit may collect movie, music, and event information and provide it to the user. The entertainment unit may also suggest recommended content based on the user's preferences. Furthermore, the entertainment unit may provide information on online events and live streaming that are held in real time. In this way, the real-time information providing system can provide entertainment information that enriches the user's life.

[0047] The real-time information provision system may further include a behavior analysis unit that analyzes a user's behavior history and provides optimal information based on the user's behavior patterns. For example, the behavior analysis unit may analyze the user's past travel history and usage history to suggest optimal routes and recommended spots. The behavior analysis unit may also provide advice on how to avoid crowds based on the user's behavior patterns. Furthermore, the behavior analysis unit may predict future behavior based on the user's behavior history and provide necessary information in advance. This allows the real-time information provision system to provide optimal information based on the user's behavior.

[0048] The real-time information providing system may further include a personalization unit that provides information customized based on the user's hobbies and interests. For example, the personalization unit may analyze the user's past search history and usage history to suggest events and news that the user may be interested in. The personalization unit may also suggest related products and services based on the user's hobbies. Furthermore, the personalization unit may provide customized travel plans and leisure information according to the user's interests. This allows the real-time information providing system to provide customized information based on the user's hobbies and interests, thereby improving user satisfaction.

[0049] The real-time information providing system can further include a location information unit that utilizes the user's location information to provide location-based information. For example, the location information unit can provide information about nearby restaurants and tourist attractions based on the user's current location. The location information unit can also suggest optimal routes and transportation methods based on the user's movement history. Furthermore, the location information unit can provide information about nearby events and activities based on the user's location information. This allows the real-time information providing system to provide information based on the user's location information, improving user convenience.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information gathering unit collects information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, and road conditions in real time. For example, it collects information on traffic conditions, traffic light changes, and road congestion in real time. It can also collect information on medical institutions and supermarkets in real time. It can also work with government agencies and local governments to collect traffic and disaster information. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it analyzes locations and time periods with a high risk of traffic accidents and issues a warning to the user. It can also use generative AI to perform sentiment analysis on the data collected from each information source and dynamically change the priority of information based on the user's emotions. Furthermore, it can use emotion estimation functionality to monitor the emotions of the user when receiving information in real time and provide information that elicits positive emotions. Step 3: The provider provides the user with the information analyzed by the analyzer. For example, it can propose services and support measures for the elderly and provide countermeasures against rising prices. It can also quickly provide necessary medical services and food. It can also collect information from different regions and countries and provide information from a global perspective.

[0052] (Example 2) A real-time information providing system according to an embodiment of the present invention is a system that updates information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, road conditions, etc. in real time and provides it as an application. As a result, the real-time information providing system can provide support from various angles to address national challenges such as a declining birthrate and aging society, rising prices, food shortages due to climate change, and COVID-19.

[0053] A real-time information provision system according to an embodiment includes an information collection unit, an analysis unit, and a provision unit. The information collection unit collects information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, and road conditions in real time. For example, the information collection unit collects information on traffic conditions, traffic light changes, and road congestion in real time. The information collection unit can also collect information on medical institutions and supermarkets in real time. Furthermore, the information collection unit can collect traffic information and disaster information in cooperation with government agencies and local governments. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit analyzes locations and time periods with a high risk of traffic accidents and issues a warning to the user. The analysis unit can also perform emotion analysis on data collected from each information source using a generative AI and dynamically change the priority of information based on the user's emotions. Furthermore, the analysis unit can use an emotion estimation function to monitor the user's emotions when receiving information in real time and provide information designed to elicit positive emotions. The provision unit provides the user with the information analyzed by the analysis unit. For example, the provision unit proposes services and support measures for the elderly and provides measures to combat rising prices. The providing unit can also quickly provide necessary medical services and food supplies. Furthermore, the providing unit can collect information from different regions and countries and provide information from a global perspective. As a result, the real-time information providing system according to the embodiment can provide support from multiple angles to address national issues such as a declining birthrate and aging society, rising prices, food shortages due to climate change, and COVID-19. For example, it can reduce the risk of traffic accidents and ensure the safety of the elderly and children. It can also provide measures to address rising prices and food shortages and support the lives of residents. Furthermore, it can support a rapid response in the event of a disaster, ensuring the safety and security of disaster victims.

[0054] The information collection unit can collect traffic conditions, traffic light changes, and road congestion conditions in real time and provide them to the user. The information collection unit, for example, collects traffic conditions in real time and provides them to the user. For example, it collects traffic volume, congestion information, accident information, etc. The information collection unit also collects traffic light changes in real time and provides them to the user. For example, it collects changes in traffic light color and timing. The information collection unit also collects road congestion conditions in real time and provides them to the user. For example, it collects vehicle density, average speed, length of congestion, etc. In this way, traffic conditions, traffic light changes, and road congestion conditions can be grasped in real time and provided to the user, thereby reducing the risk of traffic accidents.

[0055] The analysis unit can analyze locations or time periods where the risk of traffic accidents is high and issue a warning to the user. The analysis unit, for example, analyzes locations where the risk of traffic accidents is high and issues a warning to the user. For example, the analysis unit identifies risks based on past accident data, traffic volume, weather conditions, etc. The analysis unit also analyzes time periods where the risk of traffic accidents is high and issues a warning to the user. For example, the analysis unit analyzes the accident occurrence rate during a specific time period. The analysis unit also analyzes a combination of locations and time periods where the risk of traffic accidents is high and issues a warning to the user. For example, the analysis unit identifies risks at specific intersections and issues a warning to the user. In this way, by analyzing locations and time periods where the risk of traffic accidents is high and issuing a warning to the user, accidents can be prevented from occurring.

[0056] The provision unit can propose services or support measures for the elderly and provide measures to combat rising prices. The provision unit, for example, proposes medical services for the elderly. For example, it provides information on specific medical departments and medical institutions. The provision unit also proposes shopping support for the elderly. For example, it provides information on specific supermarkets and products. The provision unit also proposes transportation support for the elderly. For example, it provides information on specific transportation methods and routes. The provision unit also provides measures to combat rising prices. For example, it provides discount information, price comparisons, money-saving tips, etc. In this way, by proposing services and support measures for the elderly and providing measures to combat rising prices, the quality of life in a society with a declining birthrate and an aging population can be improved.

[0057] The information collection unit collects information on medical institutions or supermarkets in real time, and can provide necessary medical services or food promptly. The information collection unit, for example, collects information on medical institutions in real time, and provides necessary medical services promptly. For example, it collects information such as consultation hours, medical specialties, and waiting times. The information collection unit also collects information on supermarkets in real time, and provides necessary food promptly. For example, it collects information such as stock status, business hours, and sale information. The information collection unit also combines and collects information on medical institutions and supermarkets, and provides necessary medical services and food promptly. For example, it collects information on medical institutions and supermarkets in a specific area and provides it to the user. This improves life support services by collecting information on medical institutions and supermarkets in real time and providing necessary medical services and food promptly.

[0058] The information collection unit can work with government agencies or local governments to collect and analyze traffic or disaster information and provide it to users. For example, the information collection unit works with government agencies to collect traffic information and provide it to users. For example, it collects information on traffic congestion, accidents, road construction, etc. The information collection unit also works with local governments to collect disaster information and provide it to users. For example, it collects earthquake alerts, flood warnings, evacuation shelter information, etc. The information collection unit also works with both government agencies and local governments to collect traffic and disaster information and provide it to users. For example, it collects a combination of traffic and disaster information for a specific area and provides it to users. In this way, by working with government agencies and local governments to collect and analyze traffic and disaster information and provide it to users, the system aims to achieve risk prediction and zero accidents through public-private partnerships.

[0059] The analysis unit uses a generation AI to perform sentiment analysis on the data collected from each information source and dynamically change the priority of information based on the user's emotions. For example, the analysis unit uses a generation AI to perform sentiment analysis on the data collected from each information source and dynamically change the priority of information based on the user's emotions. For example, if the user is feeling stressed, it prioritizes providing information that helps the user relax. The analysis unit also uses a generation AI to calculate a sentiment score for the collected data and change the display order of information according to the user's emotional state. For example, it prioritizes displaying information that evokes positive emotions. The analysis unit also performs sentiment analysis and filters information based on the user's emotions. For example, it eliminates information that evokes negative emotions and displays only positive information. In this way, the analysis unit uses a generation AI to perform sentiment analysis on the data collected from each information source and dynamically changes the priority of information based on the user's emotions, thereby providing the user with the most appropriate information.

[0060] The information collection unit can use drones or autonomous vehicles to collect on-site video in real time, and AI can analyze and update the information. For example, the information collection unit can use drones to collect video of traffic conditions and disaster sites in real time, and AI can analyze and update the information. For example, traffic congestion and the progression of a disaster can be grasped in real time. The information collection unit can also use autonomous vehicles to collect road congestion and traffic light changes in real time, and AI can analyze and update the information. For example, it can identify areas with a high risk of traffic accidents. The information collection unit can also use AI to analyze video data collected from drones or autonomous vehicles, and update the information provided to users in real time. For example, it can quickly provide evacuation routes in the event of a disaster. In this way, by collecting on-site video in real time using drones or autonomous vehicles, and AI can analyze and update the information, it is possible to quickly grasp the local situation and take appropriate measures.

[0061] The information collection unit can use IoT devices to collect environmental data from homes or public facilities in real time and provide it to users. For example, the information collection unit uses IoT devices to collect environmental data such as temperature, humidity, and air quality in the home in real time, and AI analyzes the data and provides it to users. For example, the information collection unit provides advice on maintaining a comfortable indoor environment. The information collection unit also collects environmental data from IoT devices installed in public facilities, analyzes it using AI, and updates the information in real time. For example, the information collection unit monitors the air quality in libraries and hospitals and proposes appropriate measures. The information collection unit also uses IoT devices to collect environmental data from homes and public facilities, and AI analyzes the data and provides it to users. For example, it proposes comfortable environmental settings in response to changes in temperature and humidity. As a result, the information collection unit can use IoT devices to collect environmental data from homes and public facilities in real time and provide it to users, thereby providing advice on maintaining a comfortable environment.

[0062] The information collection unit can collect weather or natural disaster forecast information in real time, and have AI analyze the information and provide it to the user. For example, the information collection unit collects weather information in real time, and AI analyzes it and provides it to the user. For example, advice is given on how to respond to sudden weather changes. The information collection unit also collects natural disaster forecast information in real time, and AI analyzes it and provides it to the user. For example, it predicts the risk of earthquakes and floods and issues evacuation instructions. The information collection unit also collects weather and natural disaster forecast information, and AI analyzes it and updates the information in real time. For example, it provides a typhoon path forecast and encourages evacuation preparations. In this way, weather and natural disaster forecast information can be collected in real time, and AI analyzes it and provides it to the user, allowing for quick responses to sudden weather changes and natural disasters.

[0063] The information collection unit can collect information from different regions or countries and provide information from a global perspective. For example, the information collection unit collects traffic information from different regions or countries in real time, which AI analyzes and provides to users. For example, it provides local traffic conditions for overseas travelers. The information collection unit also collects disaster information from different regions or countries from a global perspective, which AI analyzes and provides to users. For example, it predicts the risk of natural disasters overseas and issues warnings to travelers. The information collection unit also collects environmental data from different regions or countries, which AI analyzes and updates the information in real time. For example, it monitors the impact of international climate change and proposes countermeasures. In this way, by collecting information from different regions or countries and providing information from a global perspective, it is possible to provide useful information to users.

[0064] The analysis unit can use the emotion estimation function to monitor the emotions of a user when receiving information in real time and provide information that elicits positive emotions. For example, the analysis unit uses the emotion estimation function to monitor the emotions of a user when receiving information in real time and provide information that elicits positive emotions. For example, if the user is feeling stressed, the analysis unit provides information that will help the user relax. The analysis unit also analyzes the user's emotional state in real time and provides information that elicits positive emotions. For example, if the emotion score is low, an encouraging message is displayed. The analysis unit also uses the emotion estimation function to build a system that provides information according to the user's emotions. For example, news or entertainment information that elicits positive emotions is provided. In this way, the emotion estimation function is used to monitor the emotions of a user when receiving information in real time and provide information that elicits positive emotions, thereby reducing the user's stress.

[0065] The analysis unit can use the generation AI to combine past accident data with real-time information to predict future accident risks and issue warnings to users. The analysis unit, for example, uses the generation AI to combine past traffic accident data with real-time traffic information to predict future accident risks. For example, it predicts the risk of an accident at a specific intersection and issues a warning to the user. The analysis unit also analyzes past accident data and real-time road conditions, and the generation AI predicts future accident risks. For example, it predicts the risk of an accident during a specific time period and issues a warning to the user. The analysis unit also uses the generation AI to combine past accident data with real-time environmental data to predict future accident risks. For example, it predicts the risk of an accident during bad weather and issues a warning to the user. In this way, accidents can be prevented from occurring by using the generation AI to combine past accident data with real-time information to predict future accident risks and issue a warning to the user.

[0066] The analysis unit uses AI to control traffic signals in real time, optimizing traffic flow and reducing the risk of accidents. For example, the analysis unit uses AI to control traffic signals in real time and optimize traffic flow. For example, it adjusts the timing of signals according to traffic volume to alleviate congestion. The analysis unit also uses AI to control traffic signals in real time and reduce the risk of accidents. For example, it adjusts the timing of signals at intersections to reduce the risk of traffic accidents. The analysis unit also builds a system in which AI controls traffic signals in real time and optimizes traffic flow. For example, it adjusts signals to give priority to emergency vehicles. In this way, it uses AI to control traffic signals in real time, optimizes traffic flow, and reduces the risk of accidents, thereby preventing traffic accidents.

[0067] The analysis unit uses AI to analyze the behavioral patterns of pedestrians or drivers, detect dangerous behavior, and issue a warning. The analysis unit, for example, uses AI to analyze the behavioral patterns of pedestrians and drivers and detect dangerous behavior. For example, it detects ignoring traffic lights or sudden lane changes and issues a warning to the user. The analysis unit also builds a system in which AI analyzes the behavioral patterns of pedestrians and drivers in real time and detects dangerous behavior. For example, it detects pedestrians walking while looking at their smartphones and issues a warning. The analysis unit also uses AI to analyze the behavioral patterns of pedestrians and drivers, detect dangerous behavior, and issue a warning. For example, it detects speeding or sudden braking and issues a warning to the driver. In this way, accidents can be prevented by analyzing the behavioral patterns of pedestrians and drivers, detecting dangerous behavior, and issuing a warning.

[0068] The analysis unit can expand the scope of risk prediction to also predict natural disasters or crime risks. The analysis unit, for example, uses AI to predict the risk of natural disasters and issue warnings to users. For example, it predicts the risk of earthquakes or floods and issues evacuation instructions. The analysis unit also builds a system in which AI predicts crime risks and issues warnings to users. For example, it predicts the risk of crime in a specific area and issues a warning. The analysis unit also uses AI to predict natural disasters and crime risks and issues a warning to users. For example, it provides information on typhoon path predictions and high-crime areas. This expands the scope of risk prediction to also predict natural disasters and crime risks, ensuring the safety of users.

[0069] The analysis unit can predict danger for different modes of transportation (bicycles, electric scooters, etc.) and provide comprehensive safety measures. For example, the analysis unit uses AI to predict danger and issue warnings to users of bicycles and electric scooters. For example, it predicts danger on roads with heavy traffic and issues warnings to users. The analysis unit also builds a system in which AI predicts danger for different modes of transportation and provides comprehensive safety measures. For example, it predicts congestion in bicycle lanes and issues warnings to users. The analysis unit also uses AI to predict danger for users of bicycles and electric scooters and provides comprehensive safety measures. For example, it issues warnings when using the vehicles at night. In this way, by predicting danger for different modes of transportation and providing comprehensive safety measures, safety is ensured for all modes of transportation.

[0070] The analysis unit can use the emotion estimation function to analyze the emotions of the user when receiving danger prediction information and provide information to reduce stress. For example, the analysis unit uses the emotion estimation function to analyze the emotions of the user when receiving danger prediction information in real time and provide information to reduce stress. For example, the analysis unit provides advice to help the user relax. The analysis unit also builds a system that analyzes the user's emotional state in real time and provides information to reduce stress. For example, it provides relaxing music when the emotion score is high. The analysis unit also uses the emotion estimation function to provide information according to the user's emotions. For example, it displays a message to elicit positive emotions when receiving danger prediction information. In this way, the emotion estimation function is used to analyze the emotions of the user when receiving danger prediction information and provide information to reduce stress, thereby reducing the user's psychological burden.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The real-time information providing system can further include a health management unit that monitors the user's health condition. For example, the health management unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and issues an alert if an abnormality is detected. The health management unit can also monitor the user's exercise level and sleep status and provide advice on maintaining health. Furthermore, the health management unit can record the user's diet and make suggestions for improving nutritional balance. This allows the real-time information providing system to comprehensively manage the user's health condition and support health maintenance.

[0073] The real-time information providing system may further include an entertainment unit that provides entertainment information. For example, the entertainment unit may collect movie, music, and event information and provide it to the user. The entertainment unit may also suggest recommended content based on the user's preferences. Furthermore, the entertainment unit may provide information on online events and live streaming that are held in real time. In this way, the real-time information providing system can provide entertainment information that enriches the user's life.

[0074] The real-time information providing system may further include a feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, the feedback unit may provide relaxation advice when the user is feeling stressed. Furthermore, the feedback unit may provide information to maintain positive emotions when the user is feeling positive. Furthermore, the feedback unit may provide encouraging messages or positive news according to the user's emotional state. This allows the real-time information providing system to provide appropriate feedback according to the user's emotions and support the user's psychological health.

[0075] The real-time information provision system may further include a behavior analysis unit that analyzes a user's behavior history and provides optimal information based on the user's behavior patterns. For example, the behavior analysis unit may analyze the user's past travel history and usage history to suggest optimal routes and recommended spots. The behavior analysis unit may also provide advice on how to avoid crowds based on the user's behavior patterns. Furthermore, the behavior analysis unit may predict future behavior based on the user's behavior history and provide necessary information in advance. This allows the real-time information provision system to provide optimal information based on the user's behavior.

[0076] The real-time information providing system may further include an entertainment unit that estimates the user's emotions and provides appropriate entertainment information based on the estimated emotions. For example, the entertainment unit may suggest relaxing movies or music when the user is feeling stressed. The entertainment unit may also provide event information to further enhance positive emotions when the user is feeling positive. Furthermore, the entertainment unit may provide information on online events and live streaming events being held in real time according to the user's emotional state. In this way, the real-time information providing system can provide entertainment information according to the user's emotions and enrich the user's life.

[0077] The real-time information providing system may further include a health management unit that estimates the user's emotions and provides appropriate health advice based on the estimated emotions. For example, the health management unit may provide advice on exercise and diet to help the user relax when the user is feeling stressed. The health management unit may also suggest health maintenance measures to maintain positive emotions when the user is feeling positive. Furthermore, the health management unit may provide appropriate advice on sleep and rest depending on the user's emotional state. This allows the real-time information providing system to provide health advice based on the user's emotions and comprehensively support the user's health.

[0078] The real-time information providing system may further include a disaster information unit that estimates the user's emotions and provides appropriate disaster information based on the estimated emotions. For example, the disaster information unit may provide reassuring evacuation information and support measures when the user is feeling anxious. The disaster information unit may also provide detailed disaster information and measures when the user is calm. Furthermore, the disaster information unit may issue evacuation instructions and warnings at appropriate times depending on the user's emotional state. This allows the real-time information providing system to provide disaster information that corresponds to the user's emotions and ensure the user's safety.

[0079] The real-time information providing system may further include a personalization unit that provides information customized based on the user's hobbies and interests. For example, the personalization unit may analyze the user's past search history and usage history to suggest events and news that the user may be interested in. The personalization unit may also suggest related products and services based on the user's hobbies. Furthermore, the personalization unit may provide customized travel plans and leisure information according to the user's interests. This allows the real-time information providing system to provide customized information based on the user's hobbies and interests, thereby improving user satisfaction.

[0080] The real-time information providing system may further include an action suggestion unit that estimates the user's emotions and suggests appropriate actions based on the estimated emotions. For example, the action suggestion unit may suggest places or activities where the user can relax when the user is feeling stressed. Also, the action suggestion unit may suggest events or activities that will further enhance the user's positive emotions when the user is feeling positive. Furthermore, the action suggestion unit may suggest actions at appropriate times depending on the user's emotional state. In this way, the real-time information providing system may suggest actions based on the user's emotions, thereby enriching the user's life.

[0081] The real-time information providing system can further include a location information unit that utilizes the user's location information to provide location-based information. For example, the location information unit can provide information about nearby restaurants and tourist attractions based on the user's current location. The location information unit can also suggest optimal routes and transportation methods based on the user's movement history. Furthermore, the location information unit can provide information about nearby events and activities based on the user's location information. This allows the real-time information providing system to provide information based on the user's location information, improving user convenience.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The information gathering unit collects information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, and road conditions in real time. For example, it collects information on traffic conditions, traffic light changes, and road congestion in real time. It can also collect information on medical institutions and supermarkets in real time. It can also work with government agencies and local governments to collect traffic and disaster information. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it analyzes locations and time periods with a high risk of traffic accidents and issues a warning to the user. It can also use generative AI to perform sentiment analysis on the data collected from each information source and dynamically change the priority of information based on the user's emotions. Furthermore, it can use emotion estimation functionality to monitor the emotions of the user when receiving information in real time and provide information that elicits positive emotions. Step 3: The provider provides the user with the information analyzed by the analyzer. For example, it can propose services and support measures for the elderly and provide countermeasures against rising prices. It can also quickly provide necessary medical services and food. It can also collect information from different regions and countries and provide information from a global perspective.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information gathering department that collects information on schools, hospitals, supermarkets, restaurants, traffic lights, crosswalks, and road conditions in real time; an analysis unit that analyzes the information collected by the information collection unit; a providing unit that provides the information analyzed by the analyzing unit to a user. A system characterized by:

2. The information collecting unit Collecting traffic conditions, changes in traffic signals, and road congestion information in real time and providing it to users 2. The system of claim 1.

3. The analysis unit Analyze locations or times when there is a high risk of traffic accidents and issue a warning to users 2. The system of claim 1.

4. The providing unit Propose services or support measures for the elderly and provide solutions to rising prices 2. The system of claim 1.

5. The information collecting unit Collecting information on medical institutions or supermarkets in real time and quickly providing necessary medical services or groceries 2. The system of claim 1.

6. The information collecting unit In cooperation with government agencies or local governments, collect and analyze traffic or disaster information and provide it to users.

2. The system of claim 1.

7. The analysis unit Using generative AI, past accident data and real-time information are combined to predict future accident risks and issue warnings to users.

2. The system of claim 1.

8. The analysis unit Analyzes the user's emotions when receiving danger prediction information and provides information to reduce stress 2. The system of claim 1.

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