System

Abstract

An object of a system according to an embodiment is to quickly and effectively specify a dangerous place in a city and promote repair.SOLUTION: A system according to an embodiment includes a dangerous place specification unit, an information providing unit, a log collection unit, a prediction unit, and a voice guidance unit. The hot spot specification unit specifies a hot spot based on the button press. The information providing unit provides the local government with information on the dangerous place specified by the dangerous place specifying unit. The log collection unit collects a log of button pressing. The prediction unit predicts a hot spot by analyzing the log collected by the log collection unit. The voice guidance unit notifies the user of information on the dangerous place predicted by the prediction unit by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly and effectively identify and repair dangerous areas in urban areas.

[0005] The system according to the embodiment aims to quickly and effectively identify dangerous areas in a city and promote repairs. [Means for solving the problem]

[0006] The system according to the embodiment includes a danger spot identifying unit, an information providing unit, a log collecting unit, a prediction unit, and a voice guidance unit. The danger spot identifying unit identifies danger spots based on button presses. The information providing unit provides information about the danger spots identified by the danger spot identifying unit to a local government. The log collecting unit collects logs of button presses. The prediction unit predicts danger spots by analyzing the logs collected by the log collecting unit. The voice guidance unit conveys information about the danger spots predicted by the prediction unit to the user by voice. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and effectively identify dangerous areas in a city and promote repairs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The Silver Guide System according to an embodiment of the present invention is a system that allows Silver Guide users to press a button when they feel there is a dangerous spot in the city, identify the location, collect and analyze information, and provide it to the local government. This allows the Silver Guide System to quickly repair dangerous spots and ensure the safety of users.

[0029] The Silver Guide system according to the embodiment includes a hazardous location identification unit, an information providing unit, a log collecting unit, a prediction unit, and a voice guidance unit. The hazardous location identification unit identifies hazardous locations based on button presses. For example, by pressing a button when a steep step or damaged braille blocks is detected, the latitude and longitude of the location are recorded. The information providing unit provides information about hazardous locations identified by the hazardous location identification unit to local governments. For example, the information is provided to each local government in real time and identified as locations requiring repair. The log collecting unit collects logs of button presses. For example, all logs of button presses are collected and stored in a database. The prediction unit predicts hazardous locations by analyzing the logs collected by the log collecting unit. For example, it analyzes past data to identify locations where danger increases during certain times of day or under certain weather conditions. The voice guidance unit provides information about hazardous locations predicted by the prediction unit to the user via voice. For example, the voice guidance is provided in the form of, "Please be careful, there is a steep step ahead." As a result, the Silver Guide System according to the embodiment can identify dangerous areas, provide information, collect logs, make predictions, and provide voice guidance in a single flow. For example, it can quickly identify areas in a city that users perceive as dangerous and provide information to local governments to promote repairs.

[0030] The danger spot identification unit records surrounding environmental sounds when the button is pressed, and analyzes the audio data to improve the accuracy of identifying danger spots. For example, when the button is pressed, the danger spot identification unit automatically activates the Silver Guide's microphone to record surrounding environmental sounds. For example, by collecting and analyzing audio data such as car horns and people shouting, the accuracy of identifying danger spots is improved. In this way, the accuracy of identifying danger spots is improved by analyzing environmental sounds.

[0031] The danger spot identification unit uses sensors to detect the user's walking speed and direction when the button is pressed, and can record detailed information about the danger spot. For example, when the button is pressed, the danger spot identification unit uses an acceleration sensor and gyro sensor built into the Silver Guide to detect the user's walking speed and direction. For example, if the user suddenly stops or changes direction, this information is recorded. This makes it possible to record detailed information about the danger spot using data on walking speed and direction.

[0032] The danger spot identification unit can automatically start the smartphone camera when the button is pressed, and take and record photos of danger spots. For example, when the button is pressed, the danger spot identification unit automatically starts the camera of the smartphone connected to the Silver Guide and takes photos of the surroundings. For example, it records photos of steep steps or broken tactile paving blocks. This makes it possible to visually record danger spots by taking photos.

[0033] When the button is pressed, the danger spot identification unit sends a notification to nearby Silver Guide users to check whether similar danger spots exist. For example, when a button is pressed, the danger spot identification unit sends a notification to nearby Silver Guide users to check whether similar danger spots exist. For example, if multiple users press the button at the same location, the location is identified as a danger spot. This improves the accuracy of identifying danger spots through feedback from nearby users.

[0034] The log collection unit collects weather data and time-of-day data in addition to the log of button presses, allowing for detailed analysis of the conditions for the occurrence of dangerous locations. For example, when a button is pressed, the log collection unit automatically collects weather data and time-of-day data and records them in a database. For example, it identifies locations where buttons are pressed frequently during rainy weather or at night. This allows for detailed analysis of the conditions for the occurrence of dangerous locations by collecting weather data and time-of-day data.

[0035] The log collection unit tracks not only the frequency of button presses but also the user's behavior after pressing the button, making it possible to evaluate the impact of dangerous areas. For example, the log collection unit analyzes the Silver Guide's GPS data to track the user's behavior after pressing the button. For example, if the user selects a detour route, that information is recorded. This makes it possible to evaluate the impact of dangerous areas by tracking the user's behavior.

[0036] The log collection unit can compare the button press logs with other cities and regions to identify common danger spots and take measures. The log collection unit, for example, integrates databases to compare the button press logs with other cities and regions. For example, it can centrally manage data from multiple cities and identify common danger spots. This makes it possible to identify common danger spots and take measures by comparing with other cities and regions.

[0037] The log collection unit can integrate the button press logs with traffic accident data and crime data to comprehensively identify dangerous areas. The log collection unit, for example, integrates databases to integrate the button press logs with traffic accident data and crime data. For example, it identifies locations where traffic accidents frequently occur and locations where crimes frequently occur. This allows comprehensive identification of dangerous areas by integrating the traffic accident data and crime data.

[0038] The prediction unit uses the generation AI to learn patterns of dangerous spot occurrence from past data, making it possible to predict future dangerous spots with high accuracy. For example, the prediction unit inputs past button press data into the generation AI and has it learn patterns of dangerous spot occurrence. For example, it identifies patterns in which dangerous spots are likely to occur during specific time periods or weather conditions. This makes it possible to use the generation AI to predict future dangerous spots with high accuracy from past data.

[0039] The prediction unit inputs the user's individual walking pattern and behavior history into the generation AI, and can make individually optimized predictions of dangerous areas. The prediction unit, for example, inputs the user's walking pattern and behavior history into the generation AI, and makes individually optimized predictions of dangerous areas. For example, it predicts dangerous areas on routes that a specific user frequently takes. This makes it possible to make individually optimized predictions of dangerous areas based on the user's individual data.

[0040] The prediction unit uses the generation AI to integrate data from different cities and regions and predict dangerous areas over a wide area. For example, the prediction unit inputs data from different cities and regions into the generation AI and predicts dangerous areas over a wide area. For example, data from multiple cities is integrated to make a prediction. In this way, by integrating data from different cities and regions, dangerous areas can be predicted over a wide area.

[0041] The prediction unit can use the generation AI to predict dangerous areas according to the season or event. For example, the prediction unit inputs seasonal data and event data into the generation AI and predicts dangerous areas according to the season or event. For example, it predicts slippery areas in winter or areas that will be crowded during festivals. This makes it possible to predict dangerous areas according to the season or event.

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

[0043] The Silver Guide System can also be equipped with a health management unit that monitors the user's health condition. For example, it can measure heart rate and blood pressure, and if an abnormality is detected, it can send a notification to an emergency contact. This allows the user's health condition to be monitored in real time, enabling a prompt response in the event of an emergency. Furthermore, the health management unit can store the results of regular health checks in a database to support long-term health management. For example, it can analyze changes in health condition based on past data and provide appropriate advice.

[0044] The Silver Guide System can also be equipped with a tourist information section that provides tourist information based on the user's preferences and interests. For example, if a user is interested in historical buildings, it can provide information on nearby historical spots. This allows tourist information to be provided based on the user's interests, making walking around the city more enjoyable. Furthermore, the tourist information section can also suggest recommended spots based on the user's past visit history. For example, suggesting spots with a similar atmosphere to places previously visited can encourage new discoveries.

[0045] The Silver Guide System can also include a rhythmic music providing unit that analyzes the user's walking pattern and provides music that matches the walking rhythm. For example, if the user is walking at a fast pace, fast-tempo music can be played. This allows music to be provided that matches the user's walking rhythm, making travelling around town more enjoyable. Furthermore, the rhythmic music providing unit can also suggest recommended music based on the user's past walking patterns. For example, by suggesting music with a tempo similar to music played when the user previously walked at a fast pace, music that matches the user's preferences can be provided.

[0046] The Silver Guide System can also be equipped with a store guide unit that provides information on nearby stores and facilities based on the user's location information. For example, if a user is looking for a cafe, information on nearby cafes can be provided. This allows store guidance based on the user's location information, improving convenience in the city. Furthermore, the store guide unit can also suggest recommended stores based on the user's past visit history. For example, suggesting stores with a similar atmosphere to cafes the user has visited in the past can encourage new discoveries.

[0047] The Silver Guide System can also be equipped with a rhythmic exercise suggestion unit that analyzes the user's walking pattern and suggests exercises that match the rhythm of the user's walking. For example, if the user is walking at a fast pace, it can suggest stretching exercises that match the fast pace. This allows exercise suggestions to be made that match the user's walking rhythm, promoting health maintenance. Furthermore, the rhythmic exercise suggestion unit can also suggest recommended exercises based on the user's past walking patterns. For example, it can support the user's health management by suggesting exercises that have similar effects to exercises that were done when walking at a fast pace in the past.

[0048] The Silver Guide System can also be equipped with an event information section that provides information about events in the area based on the user's location information. For example, if a user is looking for events being held nearby, that information can be provided. This allows event information to be provided according to the user's location information, increasing the enjoyment of the city. Furthermore, the event information section can also suggest recommended events based on the user's past event participation history. For example, by suggesting events with content similar to events the user has previously attended, it is possible to provide events that match the user's interests.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The danger spot identification unit identifies danger spots based on button presses. For example, by pressing a button when a steep step or damaged braille blocks is detected, the latitude and longitude of the location are recorded. Step 2: The information providing unit provides information on the dangerous areas identified by the dangerous area identifying unit to local governments. For example, the information is provided to each local government in real time, and the areas are recognized as needing repairs. Step 3: The log collection unit collects logs of button presses. For example, all logs of button presses are collected and stored in a database. Step 4: The prediction unit analyzes the logs collected by the log collection unit to predict dangerous locations. For example, it analyzes past data to identify locations where danger increases during specific times of day or under specific weather conditions. Step 5: The voice guidance unit notifies the user by voice of the information about the dangerous areas predicted by the prediction unit. For example, the voice guidance may be something like, "There is a steep step ahead, so please be careful."

[0051] (Example 2) The Silver Guide System according to an embodiment of the present invention is a system that allows Silver Guide users to press a button when they feel there is a dangerous spot in the city, identify the location, collect and analyze information, and provide it to the local government. This allows the Silver Guide System to quickly repair dangerous spots and ensure the safety of users.

[0052] The Silver Guide system according to the embodiment includes a hazardous location identification unit, an information providing unit, a log collecting unit, a prediction unit, and a voice guidance unit. The hazardous location identification unit identifies hazardous locations based on button presses. For example, by pressing a button when a steep step or damaged braille blocks is detected, the latitude and longitude of the location are recorded. The information providing unit provides information about hazardous locations identified by the hazardous location identification unit to local governments. For example, the information is provided to each local government in real time and identified as locations requiring repair. The log collecting unit collects logs of button presses. For example, all logs of button presses are collected and stored in a database. The prediction unit predicts hazardous locations by analyzing the logs collected by the log collecting unit. For example, it analyzes past data to identify locations where danger increases during certain times of day or under certain weather conditions. The voice guidance unit provides information about hazardous locations predicted by the prediction unit to the user via voice. For example, the voice guidance is provided in the form of, "Please be careful, there is a steep step ahead." As a result, the Silver Guide System according to the embodiment can identify dangerous areas, provide information, collect logs, make predictions, and provide voice guidance in a single flow. For example, it can quickly identify areas in a city that users perceive as dangerous and provide information to local governments to promote repairs.

[0053] The danger spot identification unit records surrounding environmental sounds when the button is pressed, and analyzes the audio data to improve the accuracy of identifying danger spots. For example, when the button is pressed, the danger spot identification unit automatically activates the Silver Guide's microphone to record surrounding environmental sounds. For example, by collecting and analyzing audio data such as car horns and people shouting, the accuracy of identifying danger spots is improved. In this way, the accuracy of identifying danger spots is improved by analyzing environmental sounds.

[0054] The danger spot identification unit uses sensors to detect the user's walking speed and direction when the button is pressed, and can record detailed information about the danger spot. For example, when the button is pressed, the danger spot identification unit uses an acceleration sensor and gyro sensor built into the Silver Guide to detect the user's walking speed and direction. For example, if the user suddenly stops or changes direction, this information is recorded. This makes it possible to record detailed information about the danger spot using data on walking speed and direction.

[0055] The danger spot identification unit uses the emotion estimation function to analyze the user's emotion when the button is pressed, and can identify areas that are emotionally perceived as dangerous. For example, when a button is pressed, the danger spot identification unit uses the Silver Guide's camera and microphone to analyze the user's facial expression and tone of voice and estimate the emotion. For example, it detects facial expressions of surprise or fear. This makes it possible to identify areas that are emotionally perceived as dangerous through emotion analysis.

[0056] The danger spot identification unit can automatically start the smartphone camera when the button is pressed, and take and record photos of danger spots. For example, when the button is pressed, the danger spot identification unit automatically starts the camera of the smartphone connected to the Silver Guide and takes photos of the surroundings. For example, it records photos of steep steps or broken tactile paving blocks. This makes it possible to visually record danger spots by taking photos.

[0057] When the button is pressed, the danger spot identification unit sends a notification to nearby Silver Guide users to check whether similar danger spots exist. For example, when a button is pressed, the danger spot identification unit sends a notification to nearby Silver Guide users to check whether similar danger spots exist. For example, if multiple users press the button at the same location, the location is identified as a danger spot. This improves the accuracy of identifying danger spots through feedback from nearby users.

[0058] The danger spot identification unit can notify local governments in real time of the user's emotions when the button is pressed, prompting emergency response. For example, when the button is pressed, the danger spot identification unit uses the Silver Guide's camera and microphone to analyze the user's facial expressions and tone of voice and infer their emotions. For example, it can detect expressions of surprise or fear. This allows for real-time notification of emotional data, enabling rapid emergency response.

[0059] The log collection unit collects weather data and time-of-day data in addition to the log of button presses, allowing for detailed analysis of the conditions for the occurrence of dangerous locations. For example, when a button is pressed, the log collection unit automatically collects weather data and time-of-day data and records them in a database. For example, it identifies locations where buttons are pressed frequently during rainy weather or at night. This allows for detailed analysis of the conditions for the occurrence of dangerous locations by collecting weather data and time-of-day data.

[0060] The log collection unit tracks not only the frequency of button presses but also the user's behavior after pressing the button, making it possible to evaluate the impact of dangerous areas. For example, the log collection unit analyzes the Silver Guide's GPS data to track the user's behavior after pressing the button. For example, if the user selects a detour route, that information is recorded. This makes it possible to evaluate the impact of dangerous areas by tracking the user's behavior.

[0061] The log collection unit uses the emotion estimation function to collect data on the user's emotions when the button is pressed, and can analyze patterns of areas that are emotionally perceived as dangerous. For example, when a button is pressed, the log collection unit uses the Silver Guide's camera and microphone to analyze the user's facial expression and tone of voice and estimate the emotion. For example, it detects expressions of surprise or fear. This allows the collection of emotional data to analyze patterns of areas that are emotionally perceived as dangerous.

[0062] The log collection unit can compare the button press logs with other cities and regions to identify common danger spots and take measures. The log collection unit, for example, integrates databases to compare the button press logs with other cities and regions. For example, it can centrally manage data from multiple cities and identify common danger spots. This makes it possible to identify common danger spots and take measures by comparing with other cities and regions.

[0063] The log collection unit can integrate the button press logs with traffic accident data and crime data to comprehensively identify dangerous areas. The log collection unit, for example, integrates databases to integrate the button press logs with traffic accident data and crime data. For example, it identifies locations where traffic accidents frequently occur and locations where crimes frequently occur. This allows comprehensive identification of dangerous areas by integrating the traffic accident data and crime data.

[0064] The log collection unit uses the emotion estimation function to analyze the user's emotional data when the button is pressed, and can identify dangerous areas that are likely to be emotionally relatable. For example, when a button is pressed, the log collection unit uses the Silver Guide's camera and microphone to analyze the user's facial expression and tone of voice and estimate the emotion. For example, it detects facial expressions of surprise or fear. This makes it possible to identify dangerous areas that are likely to be emotionally relatable by analyzing the emotional data.

[0065] The prediction unit uses the generation AI to learn patterns of dangerous spot occurrence from past data, making it possible to predict future dangerous spots with high accuracy. For example, the prediction unit inputs past button press data into the generation AI and has it learn patterns of dangerous spot occurrence. For example, it identifies patterns in which dangerous spots are likely to occur during specific time periods or weather conditions. This makes it possible to use the generation AI to predict future dangerous spots with high accuracy from past data.

[0066] The prediction unit inputs the user's individual walking pattern and behavior history into the generation AI, and can make individually optimized predictions of dangerous areas. The prediction unit, for example, inputs the user's walking pattern and behavior history into the generation AI, and makes individually optimized predictions of dangerous areas. For example, it predicts dangerous areas on routes that a specific user frequently takes. This makes it possible to make individually optimized predictions of dangerous areas based on the user's individual data.

[0067] The prediction unit can use the emotion estimation function to input the user's emotion data into the generation AI and predict areas that will be emotionally perceived as dangerous. The prediction unit, for example, uses the emotion estimation function to collect the user's emotion data and input it into the generation AI. For example, it predicts dangerous areas based on emotion data such as surprise or fear. This makes it possible to predict areas that will be emotionally perceived as dangerous based on the emotion data.

[0068] The prediction unit uses the generation AI to integrate data from different cities and regions and predict dangerous areas over a wide area. For example, the prediction unit inputs data from different cities and regions into the generation AI and predicts dangerous areas over a wide area. For example, data from multiple cities is integrated to make a prediction. In this way, by integrating data from different cities and regions, dangerous areas can be predicted over a wide area.

[0069] The prediction unit can use the generation AI to predict dangerous areas according to the season or event. For example, the prediction unit inputs seasonal data and event data into the generation AI and predicts dangerous areas according to the season or event. For example, it predicts slippery areas in winter or areas that will be crowded during festivals. This makes it possible to predict dangerous areas according to the season or event.

[0070] The prediction unit uses the emotion estimation function to input the user's emotion data to the generation AI in real time, and can instantly predict dangerous areas and provide voice guidance. The prediction unit, for example, uses the emotion estimation function to collect the user's emotion data in real time and input it to the generation AI. For example, it can instantly predict dangerous areas based on emotion data such as surprise or fear. This allows for the real-time input of emotion data to instantly predict dangerous areas and provide voice guidance.

[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 Silver Guide System can also be equipped with a health management unit that monitors the user's health condition. For example, it can measure heart rate and blood pressure, and if an abnormality is detected, it can send a notification to an emergency contact. This allows the user's health condition to be monitored in real time, enabling a prompt response in the event of an emergency. Furthermore, the health management unit can store the results of regular health checks in a database to support long-term health management. For example, it can analyze changes in health condition based on past data and provide appropriate advice.

[0073] The Silver Guide System can also be equipped with a tourist information section that provides tourist information based on the user's preferences and interests. For example, if a user is interested in historical buildings, it can provide information on nearby historical spots. This allows tourist information to be provided based on the user's interests, making walking around the city more enjoyable. Furthermore, the tourist information section can also suggest recommended spots based on the user's past visit history. For example, suggesting spots with a similar atmosphere to places previously visited can encourage new discoveries.

[0074] The Silver Guide System can also include a music provider that estimates the user's emotions and provides appropriate music based on the estimated emotions. For example, if the user feels like relaxing, relaxing music can be played. This allows music to be provided according to the user's emotions, making travel around town more comfortable. Furthermore, the music provider can also suggest recommended songs based on the user's past music playback history. For example, by suggesting songs in a similar genre to songs played in the past, music that matches the user's preferences can be provided.

[0075] The Silver Guide System can also be equipped with an exercise suggestion unit that estimates the user's emotions and suggests appropriate exercises based on the estimated emotions. For example, if the user is feeling stressed, it can suggest stretching exercises that will help them relax. This allows the system to suggest exercises that are appropriate for the user's emotions, promoting health maintenance. Furthermore, the exercise suggestion unit can also suggest recommended exercises based on the user's past exercise history. For example, it can support the user's health management by suggesting exercises that have similar effects to exercises they have done in the past.

[0076] The Silver Guide System can also include a message sending unit that estimates the user's emotions and sends an appropriate message based on the estimated emotions. For example, if the user is feeling anxious, an encouraging message can be sent. This allows messages to be sent according to the user's emotions, providing psychological support. Furthermore, the message sending unit can also suggest recommended messages based on the user's past message history. For example, by suggesting messages with content similar to messages sent in the past, it is possible to support the user's mental care.

[0077] The Silver Guide System can also include an aroma providing unit that estimates the user's emotions and provides an appropriate aroma based on the estimated emotions. For example, if the user feels like relaxing, an aroma with a relaxing effect can be provided. This allows aromas to be provided according to the user's emotions, making travel around town more comfortable. Furthermore, the aroma providing unit can also suggest recommended aromas based on the user's past aroma usage history. For example, by suggesting aromas with effects similar to those used in the past, it is possible to provide aromas that suit the user's preferences.

[0078] The Silver Guide System can also include a rhythmic music providing unit that analyzes the user's walking pattern and provides music that matches the walking rhythm. For example, if the user is walking at a fast pace, fast-tempo music can be played. This allows music to be provided that matches the user's walking rhythm, making travelling around town more enjoyable. Furthermore, the rhythmic music providing unit can also suggest recommended music based on the user's past walking patterns. For example, by suggesting music with a tempo similar to music played when the user previously walked at a fast pace, music that matches the user's preferences can be provided.

[0079] The Silver Guide System can also be equipped with a store guide unit that provides information on nearby stores and facilities based on the user's location information. For example, if a user is looking for a cafe, information on nearby cafes can be provided. This allows store guidance based on the user's location information, improving convenience in the city. Furthermore, the store guide unit can also suggest recommended stores based on the user's past visit history. For example, suggesting stores with a similar atmosphere to cafes the user has visited in the past can encourage new discoveries.

[0080] The Silver Guide System can also be equipped with a rhythmic exercise suggestion unit that analyzes the user's walking pattern and suggests exercises that match the rhythm of the user's walking. For example, if the user is walking at a fast pace, it can suggest stretching exercises that match the fast pace. This allows exercise suggestions to be made that match the user's walking rhythm, promoting health maintenance. Furthermore, the rhythmic exercise suggestion unit can also suggest recommended exercises based on the user's past walking patterns. For example, it can support the user's health management by suggesting exercises that have similar effects to exercises that were done when walking at a fast pace in the past.

[0081] The Silver Guide System can also be equipped with an event information section that provides information about events in the area based on the user's location information. For example, if a user is looking for events being held nearby, that information can be provided. This allows event information to be provided according to the user's location information, increasing the enjoyment of the city. Furthermore, the event information section can also suggest recommended events based on the user's past event participation history. For example, by suggesting events with content similar to events the user has previously attended, it is possible to provide events that match the user's interests.

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

[0083] Step 1: The danger spot identification unit identifies danger spots based on button presses. For example, by pressing a button when a steep step or damaged braille blocks is detected, the latitude and longitude of the location are recorded. Step 2: The information providing unit provides information on the dangerous areas identified by the dangerous area identifying unit to local governments. For example, the information is provided to each local government in real time, and the areas are recognized as needing repairs. Step 3: The log collection unit collects logs of button presses. For example, all logs of button presses are collected and stored in a database. Step 4: The prediction unit analyzes the logs collected by the log collection unit to predict dangerous locations. For example, it analyzes past data to identify locations where danger increases during specific times of day or under specific weather conditions. Step 5: The voice guidance unit notifies the user by voice of the information about the dangerous areas predicted by the prediction unit. For example, the voice guidance may be something like, "There is a steep step ahead, so please be careful."

[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, in order to avoid confusion and to 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. a dangerous spot identifying unit that identifies a dangerous spot based on a button press; an information providing unit that provides information on the dangerous spot identified by the dangerous spot identifying unit to a local government; a log collection unit that collects a log of button presses; a prediction unit that analyzes the logs collected by the log collection unit and predicts dangerous locations; a voice guidance unit that conveys information about the dangerous locations predicted by the prediction unit to the user by voice. A system characterized by:

2. The dangerous spot identification unit When the button is pressed, the surrounding environmental sounds are recorded and the audio data is analyzed to improve the accuracy of identifying dangerous areas.

2. The system of claim 1.

3. The dangerous spot identification unit When the button is pressed, the smartphone camera automatically activates and takes and records a photo of the dangerous area.

2. The system of claim 1.

4. The log collection unit In addition to logging the button presses, weather and time-of-day data will be collected to analyze in detail the conditions under which dangerous areas occur.

2. The system of claim 1.

5. The prediction unit Generative AI is used to learn the occurrence patterns of these dangerous areas from past data and predict future dangerous areas with high accuracy.

2. The system of claim 1.

6. The dangerous spot identification unit Analyze the user's emotions when pressing the button and identify areas that are emotionally perceived as dangerous.

2. The system of claim 1.

Images