System

Abstract

An object of a system according to an embodiment is to integrally provide determination of a suspicious person, route guidance, and sensing of danger.SOLUTION: A system according to an embodiment includes a suspicious person determination unit, a route guidance unit, and a danger sensing unit. The suspicious person discrimination unit discriminates a suspicious person using a behavior pattern and a face recognition technique. The route guidance unit performs route guidance in an interactive manner. The danger sensing unit senses a danger such as a traffic signal, a car, or a bicycle and notifies the user of the danger 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 provide multiple integrated functions, such as identifying suspicious individuals, providing route guidance for lost children, and detecting danger.

[0005] The system according to the embodiment aims to provide an integrated service for identifying suspicious individuals, providing route guidance, and detecting danger. [Means for solving the problem]

[0006] The system according to the embodiment includes a suspicious person determination unit, a route guidance unit, and a danger detection unit. The suspicious person determination unit determines suspicious persons using behavioral patterns and face recognition technology. The route guidance unit provides route guidance in a conversational format. The danger detection unit detects dangers such as traffic lights, cars, and bicycles and notifies the user by voice. [Effects of the Invention]

[0007] The system according to the embodiment can provide an integrated service for identifying suspicious individuals, providing route guidance, and detecting danger. [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 monitoring tool according to an embodiment of the present invention is a system that watches over loved ones nearby and provides assistance through the voices of family members. This system has the functions of identifying suspicious individuals using behavioral patterns and face recognition technology, calling out to them using the voices of family members, providing conversational route guidance, and detecting dangers such as traffic lights, cars, and bicycles and providing voice alerts. This allows the monitoring tool to watch over loved ones nearby and provide assistance through the voices of family members.

[0029] The monitoring tool according to the embodiment includes a suspicious person determination unit, a route guidance unit, and a danger detection unit. The suspicious person determination unit determines suspicious persons using behavioral patterns and facial recognition technology. For example, the suspicious person determination unit uses a generation AI to collect data from cameras and sensors and analyze behavioral patterns and facial features. The suspicious person determination unit can also detect abnormal behavior by comparing the data with past data. The suspicious person determination unit can also analyze audio data and detect intimidating behavior. For example, the generation AI can detect behavior such as repeatedly traveling to a specific area or abnormally approaching a specific person. The generation AI can also analyze changes in voice volume and tone to identify intimidating behavior. The route guidance unit provides route guidance in a conversational format. For example, the route guidance unit uses the generation AI to collect information about the user's destination and calculate the optimal route. The route guidance unit can also analyze the user's past travel history and prioritize frequently visited locations. The route guidance unit can also provide information about surrounding landmarks and stores to help users understand their current location. For example, the AI ​​can ask questions such as "Where do you want to go?" If the user answers "I want to go to the station," it can calculate the optimal route and provide guidance such as "Go straight and turn right at the next corner." The AI ​​can also collect GPS data to identify places frequently visited by the user. The AI ​​can also analyze map data to identify important landmarks and stores. The danger detection unit can detect dangers such as traffic lights, cars, and bicycles and provide audio alerts. For example, the AI ​​can collect data from cameras and sensors and analyze the movement of traffic lights, cars, and bicycles. The AI ​​can also analyze environmental data such as ambient temperature and humidity to notify users of abnormal weather or disaster risks. The AI ​​can also automatically send the user's location information to family members and emergency contacts. For example, when a traffic light turns red or a car is approaching, the generative AI will issue a voice warning such as "The traffic light is red. Please stop" or "A car is approaching. Be careful." The generative AI also collects data from sensors and detects sudden increases in temperature or changes in humidity.The generative AI also collects GPS data to identify location information. This allows the monitoring tool to keep an eye on loved ones nearby and provide support through voice calls from family members. For example, the output unit displays the grading results to students and teachers via a web or mobile application. If students or teachers prefer paper feedback, they can print the results using a printer. Sending the results via email provides quick feedback by sending the results directly to students and parents.

[0030] The suspicious person discrimination unit can analyze behavioral patterns in real time and compare them with past data to detect abnormal behavior. In the suspicious person discrimination unit, for example, the generation AI collects data from cameras and sensors and analyzes behavioral patterns in real time. For example, the generation AI detects behavior such as repeatedly moving back and forth to a specific area or abnormally approaching a specific person. In addition, the suspicious person discrimination unit detects abnormal behavior by comparing it with past behavioral data. For example, the generation AI evaluates the degree of deviation from normal behavior patterns and identifies abnormal behavior. This makes it possible to analyze the behavior of suspicious people in real time and detect abnormal behavior.

[0031] The suspicious person detection unit analyzes the tone of voice and speaking style to detect intimidating behavior. For example, the generation AI collects audio data and analyzes the tone of voice and speaking style. For example, the generation AI analyzes changes in the volume and tone of voice to identify intimidating behavior. The suspicious person detection unit also analyzes the speed at which the AI ​​speaks and the choice of words to detect intimidating behavior. For example, the generation AI identifies intimidating behavior based on an analysis of voice frequency and changes in volume. This makes it possible to analyze the tone of voice and speaking style of a suspicious person and detect intimidating behavior.

[0032] The suspicious person detection unit can analyze environmental sounds and detect abnormal sounds. In the suspicious person detection unit, for example, the generation AI collects audio data from a microphone and analyzes environmental sounds. For example, the generation AI identifies sounds such as screams and breaking glass. In addition, the suspicious person detection unit can detect abnormal sounds. For example, the generation AI analyzes ambient noise and specific sound sources and identifies abnormal sounds. This allows the surrounding environmental sounds to be analyzed and abnormal sounds to be detected.

[0033] The suspicious person determination unit can record behavior so that it can be provided to family members or the police later. In the suspicious person determination unit, for example, the generation AI collects data from cameras and sensors and records behavior. For example, the generation AI records behavior in a specific area or approach behavior toward a specific person. The suspicious person determination unit also provides the data recorded by the generation AI to family members or the police. For example, the generation AI saves video and records audio, and transmits the data. This allows the behavior of a suspicious person to be recorded and provided to family members or the police later.

[0034] The route guidance unit can analyze the user's movement history and prioritize guidance to frequently visited places. In the route guidance unit, for example, the generation AI collects GPS data and analyzes the user's movement history. For example, the generation AI identifies places that the user frequently visits. In addition, the route guidance unit prioritizes guidance to places that the generation AI frequently visits. For example, the generation AI provides guidance based on frequently visited places and the user's preferences. This allows the user's past movement history to be analyzed and guidance to frequently visited places prioritized.

[0035] The route guidance unit provides information about surrounding landmarks and stores during route guidance, making it easier for the user to understand their current location. In the route guidance unit, for example, the generation AI analyzes map data and provides information about surrounding landmarks and stores. For example, the generation AI provides information about landmarks and stores that the user will pass by. In addition, the route guidance unit provides information that the generation AI uses to make it easier for the user to understand their current location. For example, the generation AI identifies important landmarks and stores and guides the user to them. This makes it easier for the user to understand their current location by providing information about surrounding landmarks and stores during route guidance.

[0036] In addition to providing route guidance when the child gets lost, the route guidance unit can also provide real-time information on public transportation and suggest the optimal means of transportation. In the route guidance unit, for example, the generation AI collects traffic data and provides real-time information on public transportation. For example, the generation AI provides the next bus or train schedule. In addition, in the route guidance unit, the generation AI suggests the optimal means of transportation. For example, the generation AI suggests the public transportation that is most suitable for the user's destination. This allows the system to provide real-time information on public transportation and suggest the optimal means of transportation in addition to providing route guidance when the child gets lost.

[0037] The route guidance unit can analyze the user's walking speed and fatigue level during route guidance and suggest rest points. In the route guidance unit, for example, the generation AI collects data from a pedometer and heart rate monitor and analyzes the user's walking speed and fatigue level. For example, the generation AI detects changes in the user's walking speed and increases in heart rate. In addition, the route guidance unit has the generation AI suggest rest points. For example, the generation AI makes a suggestion such as "Let's take a break at the next park." This allows the user's walking speed and fatigue level to be analyzed during route guidance and suggest rest points.

[0038] When detecting danger, the danger detection unit also analyzes environmental data such as the surrounding temperature and humidity, and can notify the user of the risk of abnormal weather or disaster. In the danger detection unit, for example, the generation AI collects data from sensors and analyzes environmental data such as the surrounding temperature and humidity. For example, the generation AI detects sudden increases in temperature or changes in humidity. In addition, the danger detection unit identifies and notifies the user of the risk of abnormal weather or disaster. For example, the generation AI analyzes weather data and identifies the risk of abnormal weather. This allows the generation AI to also analyze environmental data such as the surrounding temperature and humidity when detecting danger, and can notify the user of the risk of abnormal weather or disaster.

[0039] The danger detection unit can automatically send the user's location information to family members or emergency contacts when it detects danger. In the danger detection unit, for example, the generation AI collects GPS data and identifies the user's location information. For example, if the user is in a dangerous situation, the generation AI automatically sends the location information to family members or emergency contacts. The danger detection unit also issues a notification when the generation AI sends the location information. For example, it issues a notification such as "The user is in a dangerous situation. Location information will be sent." This allows the user's location information to be automatically sent to family members or emergency contacts when danger is detected.

[0040] In addition to detecting danger, the danger detection unit can analyze the movements of people in the vicinity and detect abnormal behavior in a group. In the danger detection unit, for example, the generation AI collects data from cameras and sensors and analyzes the movements of people in the vicinity. For example, the generation AI detects when a particular person moves around abnormally in a group. In the danger detection unit, the generation AI identifies abnormal behavior. For example, the generation AI evaluates the degree of deviation from normal behavior patterns and identifies abnormal behavior. This makes it possible to not only detect danger, but also analyze the movements of people in the vicinity and detect abnormal behavior in a group.

[0041] The danger detection unit can monitor the user's health condition when it detects danger and issue a warning if there is an abnormality. For example, the generation AI in the danger detection unit collects data from heart rate and blood pressure monitors and monitors the user's health condition. For example, the generation AI detects sudden increases in heart rate and fluctuations in blood pressure. The danger detection unit also identifies abnormalities and issues a warning. For example, the generation AI issues a warning such as, "Your heart rate is rising suddenly. Please be careful." This allows the system to monitor the user's health condition when it detects danger and issue a warning if there is an abnormality.

[0042] The support unit for visually impaired people analyzes surrounding audio information when used by a visually impaired person and can emphasize important information when conveying it. For example, the generation AI in the support unit for visually impaired people collects audio data from a microphone and analyzes surrounding audio information. For example, the generation AI identifies car horns and people calling out to others. In addition, the support unit for visually impaired people has the generation AI emphasize important information when conveying it. For example, the generation AI emphasizes car horns and people calling out to others. This allows the support unit for visually impaired people to analyze surrounding audio information when used by a visually impaired person and can emphasize important information when conveying it.

[0043] The support unit for visually impaired people provides tactile feedback when used by visually impaired people, and can notify them of the surrounding situation through vibrations and temperature changes. For example, the generation AI provides tactile feedback using a vibration motor or temperature sensor. For example, the generation AI warns them by vibrating when an obstacle approaches. The support unit for visually impaired people also uses temperature changes to notify them of the surrounding situation. For example, the generation AI collects temperature data using a temperature sensor and warns them of temperature changes. This allows the support unit for visually impaired people to provide tactile feedback when used by visually impaired people, and can notify them of the surrounding situation through vibrations and temperature changes.

[0044] In addition to supporting the visually impaired, the visually impaired support unit can add a function to convert surrounding audio information into text and display it for the hearing impaired. In this support unit for the visually impaired, for example, the generation AI uses voice recognition technology to convert audio data into text. For example, the generation AI displays surrounding conversations and warning sounds as text. The visually impaired support unit also provides a device on which the generation AI can display the text. For example, the generation AI displays the text on a smartphone or tablet. This allows for the addition of a function to convert surrounding audio information into text and display it for the hearing impaired, in addition to supporting the visually impaired.

[0045] The support unit for visually impaired people can analyze the movements of people around them when used by a visually impaired person and provide warnings to reduce the risk of contact. For example, the generation AI collects data from cameras and sensors and analyzes the movements of people around them. For example, the generation AI detects when a specific person approaches a visually impaired person. The support unit for visually impaired people can also identify the risk of contact and provide warnings. For example, the generation AI issues a warning such as, "There is a person ahead. Please be careful." This allows the support unit for visually impaired people to analyze the movements of people around them and provide warnings to reduce the risk of contact when used by a visually impaired person.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The monitoring tool can also be equipped with a health management unit. The health management unit can monitor the user's health condition and issue a warning if there is an abnormality. For example, the generative AI collects data from a heart rate and blood pressure monitor and analyzes the user's health condition. It can detect a sudden increase in heart rate or a fluctuation in blood pressure and issue a warning such as, "Your heart rate is rising rapidly. Please be careful." The health management unit can also notify family members or medical institutions of the user's health condition. This allows the user's health condition to be monitored in real time and a prompt response can be made if there is an abnormality.

[0048] The monitoring tool can also be equipped with an energy management unit. The energy management unit can monitor the device's remaining battery level and prompt charging as necessary. For example, the generative AI can collect battery data and send a notification such as "The battery is low. Please charge it" when the remaining charge is low. The energy management unit can also automatically switch the device into energy-saving mode. This can extend the device's battery life and enable longer use.

[0049] The monitoring tool can further include a learning support unit. The learning support unit can monitor the user's learning status and provide appropriate learning content. For example, the generative AI can analyze the user's learning history and identify weak areas. It can provide supplementary content for weak areas and make suggestions such as "Let's review this subject again." The learning support unit can also notify family members and teachers of the user's learning progress. This can effectively support the user's learning.

[0050] The monitoring tool can further be equipped with a security camera linking unit. The security camera linking unit can link with surrounding security cameras to detect abnormal behavior. For example, the generative AI can collect video data from security cameras and analyze abnormal behavior. It can detect suspicious activity in a specific area and issue a warning such as, "Suspicious activity has been detected. Please be careful." The security camera linking unit can also record abnormal behavior and later provide it to family members or the police. This allows it to link with surrounding security cameras and quickly detect abnormal behavior.

[0051] The monitoring tool can also be equipped with an emergency response unit, which can respond quickly when a user experiences an emergency. For example, the generative AI can monitor the user's location and health status to detect an emergency. If the user collapses, it can issue a notification such as, "An emergency has occurred. Call an ambulance." The emergency response unit can also automatically send the user's location information to family members and emergency contacts. This allows for a quick response and appropriate assistance when a user experiences an emergency.

[0052] The monitoring tool can further include an environmental adaptation unit. The environmental adaptation unit can provide appropriate advice depending on the user's surrounding environment. For example, the generative AI collects and analyzes environmental data such as temperature, humidity, and noise level. If the user is in a hot environment, the AI ​​can provide advice such as "Don't forget to stay hydrated." The environmental adaptation unit can also suggest health management tips tailored to the user's living environment. This allows the tool to provide appropriate advice tailored to the user's surrounding environment, supporting a comfortable lifestyle.

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

[0054] Step 1: The suspicious person detection unit uses behavioral patterns and facial recognition technology to identify suspicious individuals. For example, the generation AI collects data from cameras and sensors and analyzes behavioral patterns and facial features. It also compares this data with past data to detect abnormal behavior and analyzes audio data to detect intimidating speech and behavior. Intimidating speech and behavior can be identified by analyzing behavior such as repeatedly coming and going to a specific area, abnormally approaching a specific person, and changes in voice volume and tone. Step 2: The route guidance section provides route guidance in a conversational format. For example, the generation AI collects information about the user's destination and calculates the optimal route. It can also analyze the user's past travel history and prioritize frequently visited places. It provides information about surrounding landmarks and stores, making it easier for the user to understand their current location. For example, it asks a question such as "Where do you want to go?" and if the user answers "I want to go to the station," it calculates the optimal route and provides guidance such as "Go straight and turn right at the next corner." Step 3: The danger detection unit detects dangers such as traffic lights, cars, and bicycles, and issues an audio alert. For example, the generative AI collects data from cameras and sensors and analyzes the movement of traffic lights, cars, and bicycles. It can also analyze environmental data such as the surrounding temperature and humidity to notify users of abnormal weather and disaster risks. The user's location information can also be automatically sent to family members and emergency contacts. For example, when the traffic light turns red or a car is approaching, an audio alert is issued, such as "The traffic light is red. Please stop" or "A car is approaching. Be careful."

[0055] (Example 2) The monitoring tool according to an embodiment of the present invention is a system that watches over loved ones nearby and provides assistance through the voices of family members. This system has the functions of identifying suspicious individuals using behavioral patterns and face recognition technology, calling out to them using the voices of family members, providing conversational route guidance, and detecting dangers such as traffic lights, cars, and bicycles and providing voice alerts. This allows the monitoring tool to watch over loved ones nearby and provide assistance through the voices of family members.

[0056] The monitoring tool according to the embodiment includes a suspicious person determination unit, a route guidance unit, and a danger detection unit. The suspicious person determination unit determines suspicious persons using behavioral patterns and facial recognition technology. For example, the suspicious person determination unit uses a generation AI to collect data from cameras and sensors and analyze behavioral patterns and facial features. The suspicious person determination unit can also detect abnormal behavior by comparing the data with past data. The suspicious person determination unit can also analyze audio data and detect intimidating behavior. For example, the generation AI can detect behavior such as repeatedly traveling to a specific area or abnormally approaching a specific person. The generation AI can also analyze changes in voice volume and tone to identify intimidating behavior. The route guidance unit provides route guidance in a conversational format. For example, the route guidance unit uses the generation AI to collect information about the user's destination and calculate the optimal route. The route guidance unit can also analyze the user's past travel history and prioritize frequently visited locations. The route guidance unit can also provide information about surrounding landmarks and stores to help users understand their current location. For example, the AI ​​can ask questions such as "Where do you want to go?" If the user answers "I want to go to the station," it can calculate the optimal route and provide guidance such as "Go straight and turn right at the next corner." The AI ​​can also collect GPS data to identify places frequently visited by the user. The AI ​​can also analyze map data to identify important landmarks and stores. The danger detection unit can detect dangers such as traffic lights, cars, and bicycles and provide audio alerts. For example, the AI ​​can collect data from cameras and sensors and analyze the movement of traffic lights, cars, and bicycles. The AI ​​can also analyze environmental data such as ambient temperature and humidity to notify users of abnormal weather or disaster risks. The AI ​​can also automatically send the user's location information to family members and emergency contacts. For example, when a traffic light turns red or a car is approaching, the generative AI will issue a voice warning such as "The traffic light is red. Please stop" or "A car is approaching. Be careful." The generative AI also collects data from sensors and detects sudden increases in temperature or changes in humidity.The generative AI also collects GPS data to identify location information. This allows the monitoring tool to keep an eye on loved ones nearby and provide support through voice calls from family members. For example, the output unit displays the grading results to students and teachers via a web or mobile application. If students or teachers prefer paper feedback, they can print the results using a printer. Sending the results via email provides quick feedback by sending the results directly to students and parents.

[0057] The suspicious person discrimination unit can analyze behavioral patterns in real time and compare them with past data to detect abnormal behavior. In the suspicious person discrimination unit, for example, the generation AI collects data from cameras and sensors and analyzes behavioral patterns in real time. For example, the generation AI detects behavior such as repeatedly moving back and forth to a specific area or abnormally approaching a specific person. In addition, the suspicious person discrimination unit detects abnormal behavior by comparing it with past behavioral data. For example, the generation AI evaluates the degree of deviation from normal behavior patterns and identifies abnormal behavior. This makes it possible to analyze the behavior of suspicious people in real time and detect abnormal behavior.

[0058] The suspicious person detection unit analyzes the tone of voice and speaking style to detect intimidating behavior. For example, the generation AI collects audio data and analyzes the tone of voice and speaking style. For example, the generation AI analyzes changes in the volume and tone of voice to identify intimidating behavior. The suspicious person detection unit also analyzes the speed at which the AI ​​speaks and the choice of words to detect intimidating behavior. For example, the generation AI identifies intimidating behavior based on an analysis of voice frequency and changes in volume. This makes it possible to analyze the tone of voice and speaking style of a suspicious person and detect intimidating behavior.

[0059] The suspicious person detection unit uses the emotion estimation function to detect anxiety or fear in the user and can instantly make a reassuring call in the voice of a family member. For example, the generation AI in the suspicious person detection unit analyzes the user's facial expressions and voice and uses the emotion estimation function to detect anxiety or fear. For example, the generation AI detects facial tension and trembling voice to identify anxiety or fear. The suspicious person detection unit also uses the generation AI to instantly make a reassuring call in the voice of a family member. For example, it provides a message in the voice of a family member such as, "Are you OK? Did something happen?" This allows the unit to detect anxiety or fear in the user and reassure them with the voice of a family member.

[0060] The suspicious person detection unit can analyze environmental sounds and detect abnormal sounds. In the suspicious person detection unit, for example, the generation AI collects audio data from a microphone and analyzes environmental sounds. For example, the generation AI identifies sounds such as screams and breaking glass. In addition, the suspicious person detection unit can detect abnormal sounds. For example, the generation AI analyzes ambient noise and specific sound sources and identifies abnormal sounds. This allows the surrounding environmental sounds to be analyzed and abnormal sounds to be detected.

[0061] The suspicious person determination unit can record behavior so that it can be provided to family members or the police later. In the suspicious person determination unit, for example, the generation AI collects data from cameras and sensors and records behavior. For example, the generation AI records behavior in a specific area or approach behavior toward a specific person. The suspicious person determination unit also provides the data recorded by the generation AI to family members or the police. For example, the generation AI saves video and records audio, and transmits the data. This allows the behavior of a suspicious person to be recorded and provided to family members or the police later.

[0062] The suspicious person discrimination unit uses the emotion estimation function to analyze the emotions of surrounding people and can identify people who are behaving abnormally in a group. In the suspicious person discrimination unit, for example, the generation AI analyzes facial expressions and voices and uses the emotion estimation function to analyze the emotions of surrounding people. For example, the generation AI identifies when a specific person in a group is abnormally nervous. In addition, the suspicious person discrimination unit uses the generation AI to identify people who are behaving abnormally. For example, the generation AI evaluates the degree of deviation from normal behavior patterns and identifies abnormal behavior. This makes it possible to analyze the emotions of surrounding people and identify people who are behaving abnormally in a group.

[0063] The route guidance unit can analyze the user's movement history and prioritize guidance to frequently visited places. In the route guidance unit, for example, the generation AI collects GPS data and analyzes the user's movement history. For example, the generation AI identifies places that the user frequently visits. In addition, the route guidance unit prioritizes guidance to places that the generation AI frequently visits. For example, the generation AI provides guidance based on frequently visited places and the user's preferences. This allows the user's past movement history to be analyzed and guidance to frequently visited places prioritized.

[0064] The route guidance unit provides information about surrounding landmarks and stores during route guidance, making it easier for the user to understand their current location. In the route guidance unit, for example, the generation AI analyzes map data and provides information about surrounding landmarks and stores. For example, the generation AI provides information about landmarks and stores that the user will pass by. In addition, the route guidance unit provides information that the generation AI uses to make it easier for the user to understand their current location. For example, the generation AI identifies important landmarks and stores and guides the user to them. This makes it easier for the user to understand their current location by providing information about surrounding landmarks and stores during route guidance.

[0065] The route guidance unit can use the emotion estimation function to provide detailed guidance and encouraging words when the user feels anxious. For example, the generation AI analyzes facial expressions and voice, and uses the emotion estimation function to provide detailed guidance and encouraging words when the user feels anxious. For example, the generation AI provides encouraging words such as, "It's okay. You'll soon reach your destination." The route guidance unit also provides detailed guidance to reduce the user's anxiety. For example, the generation AI provides detailed route directions and information about surrounding facilities. This allows the user to receive detailed guidance and encouraging words when they feel anxious.

[0066] In addition to providing route guidance when the child gets lost, the route guidance unit can also provide real-time information on public transportation and suggest the optimal means of transportation. In the route guidance unit, for example, the generation AI collects traffic data and provides real-time information on public transportation. For example, the generation AI provides the next bus or train schedule. In addition, in the route guidance unit, the generation AI suggests the optimal means of transportation. For example, the generation AI suggests the public transportation that is most suitable for the user's destination. This allows the system to provide real-time information on public transportation and suggest the optimal means of transportation in addition to providing route guidance when the child gets lost.

[0067] The route guidance unit can analyze the user's walking speed and fatigue level during route guidance and suggest rest points. In the route guidance unit, for example, the generation AI collects data from a pedometer and heart rate monitor and analyzes the user's walking speed and fatigue level. For example, the generation AI detects changes in the user's walking speed and increases in heart rate. In addition, the route guidance unit has the generation AI suggest rest points. For example, the generation AI makes a suggestion such as "Let's take a break at the next park." This allows the user's walking speed and fatigue level to be analyzed during route guidance and suggest rest points.

[0068] The route guidance unit can use the emotion estimation function to provide information on tourist spots and events that the user may be interested in. For example, the generation AI analyzes facial expressions and voice, and uses the emotion estimation function to provide information on tourist spots and events that the user may be interested in. For example, the generation AI identifies places and events that the user has shown interest in. The route guidance unit also provides information on tourist spots and events through the generation AI. For example, the generation AI provides the date, time, location, and content of the event. This allows the system to provide information on tourist spots and events that the user may be interested in.

[0069] When detecting danger, the danger detection unit also analyzes environmental data such as the surrounding temperature and humidity, and can notify the user of the risk of abnormal weather or disaster. In the danger detection unit, for example, the generation AI collects data from sensors and analyzes environmental data such as the surrounding temperature and humidity. For example, the generation AI detects sudden increases in temperature or changes in humidity. In addition, the danger detection unit identifies and notifies the user of the risk of abnormal weather or disaster. For example, the generation AI analyzes weather data and identifies the risk of abnormal weather. This allows the generation AI to also analyze environmental data such as the surrounding temperature and humidity when detecting danger, and can notify the user of the risk of abnormal weather or disaster.

[0070] The danger detection unit can automatically send the user's location information to family members or emergency contacts when it detects danger. In the danger detection unit, for example, the generation AI collects GPS data and identifies the user's location information. For example, if the user is in a dangerous situation, the generation AI automatically sends the location information to family members or emergency contacts. The danger detection unit also issues a notification when the generation AI sends the location information. For example, it issues a notification such as "The user is in a dangerous situation. Location information will be sent." This allows the user's location information to be automatically sent to family members or emergency contacts when danger is detected.

[0071] The danger detection unit can use the emotion estimation function to instantly provide a reassuring voice message when the user senses danger. For example, the generation AI analyzes facial expressions and voice, and uses the emotion estimation function to provide a reassuring voice message when the user senses danger. For example, the generation AI provides a message such as "It's okay. Help will be on its way soon." The danger detection unit also provides a voice message to reduce the user's anxiety. For example, the generation AI provides a message in the voice of a family member or words of encouragement. This allows the user to instantly provide a reassuring voice message when they sense danger.

[0072] In addition to detecting danger, the danger detection unit can analyze the movements of people in the vicinity and detect abnormal behavior in a group. In the danger detection unit, for example, the generation AI collects data from cameras and sensors and analyzes the movements of people in the vicinity. For example, the generation AI detects when a particular person moves around abnormally in a group. In the danger detection unit, the generation AI identifies abnormal behavior. For example, the generation AI evaluates the degree of deviation from normal behavior patterns and identifies abnormal behavior. This makes it possible to not only detect danger, but also analyze the movements of people in the vicinity and detect abnormal behavior in a group.

[0073] The danger detection unit can monitor the user's health condition when it detects danger and issue a warning if there is an abnormality. For example, the generation AI in the danger detection unit collects data from heart rate and blood pressure monitors and monitors the user's health condition. For example, the generation AI detects sudden increases in heart rate and fluctuations in blood pressure. The danger detection unit also identifies abnormalities and issues a warning. For example, the generation AI issues a warning such as, "Your heart rate is rising suddenly. Please be careful." This allows the system to monitor the user's health condition when it detects danger and issue a warning if there is an abnormality.

[0074] The danger detection unit can use the emotion estimation function to guide the user to the nearest safe place or evacuation shelter when the user feels danger. For example, the generation AI analyzes facial expressions and voice, and uses the emotion estimation function to guide the user to the nearest safe place or evacuation shelter when the user feels danger. For example, the generation AI may provide guidance such as, "There is a shelter nearby. Please head there." The danger detection unit also provides the generation AI with information to ensure the user's safety. For example, the generation AI may provide guidance on the location and route of the evacuation shelter. This allows the generation AI to guide the user to the nearest safe place or evacuation shelter when the user feels danger.

[0075] The support unit for visually impaired people analyzes surrounding audio information when used by a visually impaired person and can emphasize important information when conveying it. For example, the generation AI in the support unit for visually impaired people collects audio data from a microphone and analyzes surrounding audio information. For example, the generation AI identifies car horns and people calling out to others. In addition, the support unit for visually impaired people has the generation AI emphasize important information when conveying it. For example, the generation AI emphasizes car horns and people calling out to others. This allows the support unit for visually impaired people to analyze surrounding audio information when used by a visually impaired person and can emphasize important information when conveying it.

[0076] The support unit for visually impaired people provides tactile feedback when used by visually impaired people, and can notify them of the surrounding situation through vibrations and temperature changes. For example, the generation AI provides tactile feedback using a vibration motor or temperature sensor. For example, the generation AI warns them by vibrating when an obstacle approaches. The support unit for visually impaired people also uses temperature changes to notify them of the surrounding situation. For example, the generation AI collects temperature data using a temperature sensor and warns them of temperature changes. This allows the support unit for visually impaired people to provide tactile feedback when used by visually impaired people, and can notify them of the surrounding situation through vibrations and temperature changes.

[0077] The visually impaired support unit can use the emotion estimation function to provide reassuring voice messages to visually impaired people when they feel anxious. For example, the generation AI analyzes facial expressions and voice, and uses the emotion estimation function to provide reassuring voice messages to visually impaired people when they feel anxious. For example, the generation AI provides messages such as "It's okay. Help will be on its way soon." The visually impaired support unit also provides voice messages to reduce the anxiety of visually impaired people. For example, the generation AI provides messages in the voices of family members or words of encouragement. This makes it possible to provide reassuring voice messages to visually impaired people when they feel anxious.

[0078] In addition to supporting the visually impaired, the visually impaired support unit can add a function to convert surrounding audio information into text and display it for the hearing impaired. In this support unit for the visually impaired, for example, the generation AI uses voice recognition technology to convert audio data into text. For example, the generation AI displays surrounding conversations and warning sounds as text. The visually impaired support unit also provides a device on which the generation AI can display the text. For example, the generation AI displays the text on a smartphone or tablet. This allows for the addition of a function to convert surrounding audio information into text and display it for the hearing impaired, in addition to supporting the visually impaired.

[0079] The support unit for visually impaired people can analyze the movements of people around them when used by a visually impaired person and provide warnings to reduce the risk of contact. For example, the generation AI collects data from cameras and sensors and analyzes the movements of people around them. For example, the generation AI detects when a specific person approaches a visually impaired person. The support unit for visually impaired people can also identify the risk of contact and provide warnings. For example, the generation AI issues a warning such as, "There is a person ahead. Please be careful." This allows the support unit for visually impaired people to analyze the movements of people around them and provide warnings to reduce the risk of contact when used by a visually impaired person.

[0080] The visually impaired support unit uses the emotion estimation function to provide information on places and events that visually impaired people are likely to be interested in, making going out more enjoyable. For example, the generation AI analyzes facial expressions and voice, and uses the emotion estimation function to provide information on places and events that visually impaired people are likely to be interested in. For example, the generation AI identifies places and events that visually impaired people are interested in. In addition, the visually impaired support unit uses the generation AI to provide information on places and events. For example, the generation AI provides the date, time, location, and content of the event. This allows the visually impaired to be provided with information on places and events that visually impaired people are likely to be interested in, making going out more enjoyable.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The monitoring tool can also be equipped with a health management unit. The health management unit can monitor the user's health condition and issue a warning if there is an abnormality. For example, the generative AI collects data from a heart rate and blood pressure monitor and analyzes the user's health condition. It can detect a sudden increase in heart rate or a fluctuation in blood pressure and issue a warning such as, "Your heart rate is rising rapidly. Please be careful." The health management unit can also notify family members or medical institutions of the user's health condition. This allows the user's health condition to be monitored in real time and a prompt response can be made if there is an abnormality.

[0083] The monitoring tool can also be equipped with an energy management unit. The energy management unit can monitor the device's remaining battery level and prompt charging as necessary. For example, the generative AI can collect battery data and send a notification such as "The battery is low. Please charge it" when the remaining charge is low. The energy management unit can also automatically switch the device into energy-saving mode. This can extend the device's battery life and enable longer use.

[0084] The monitoring tool can further include a learning support unit. The learning support unit can monitor the user's learning status and provide appropriate learning content. For example, the generative AI can analyze the user's learning history and identify weak areas. It can provide supplementary content for weak areas and make suggestions such as "Let's review this subject again." The learning support unit can also notify family members and teachers of the user's learning progress. This can effectively support the user's learning.

[0085] The monitoring tool can also use emotion estimation to monitor the user's stress level and suggest ways to relax. For example, the generative AI can analyze facial expressions and voice to estimate the user's stress level. When stress levels rise, the tool can suggest things like, "Take a deep breath and relax." The emotion estimation function can also be used to provide relaxing music or guided meditations tailored to the user's preferences. This can help reduce the user's stress and support their physical and mental health.

[0086] The monitoring tool can also use emotion estimation to provide messages to motivate users. For example, the generation AI can analyze facial expressions and voice to estimate the user's motivation level. When motivation drops, it can provide encouraging messages such as "Do your best! You can do it!". The emotion estimation function can also be used to monitor the user's progress toward achieving their goals and provide feedback according to their level of achievement. This helps maintain the user's motivation and support them in achieving their goals.

[0087] The monitoring tool can also use emotion estimation functions to provide entertainment content that matches the user's emotions. For example, the generative AI can analyze facial expressions and voice to estimate the user's emotions. If the user is feeling sad, it can make suggestions such as, "Would you like to watch an uplifting movie?" The emotion estimation function can also be used to provide music and videos that match the user's preferences. This can provide entertainment content that matches the user's emotions and improve their mood.

[0088] The monitoring tool can also use an emotion estimation function to suggest exercise programs that correspond to the user's emotions. For example, the generation AI can analyze facial expressions and voice to estimate the user's emotions. If the user is feeling stressed, it can make suggestions such as, "Why not try some relaxing yoga?" The emotion estimation function can also be used to provide exercise programs that match the user's physical condition and mood. This makes it possible to suggest exercise programs that correspond to the user's emotions and support their physical and mental health.

[0089] The monitoring tool can further be equipped with a security camera linking unit. The security camera linking unit can link with surrounding security cameras to detect abnormal behavior. For example, the generative AI can collect video data from security cameras and analyze abnormal behavior. It can detect suspicious activity in a specific area and issue a warning such as, "Suspicious activity has been detected. Please be careful." The security camera linking unit can also record abnormal behavior and later provide it to family members or the police. This allows it to link with surrounding security cameras and quickly detect abnormal behavior.

[0090] The monitoring tool can also be equipped with an emergency response unit, which can respond quickly when a user experiences an emergency. For example, the generative AI can monitor the user's location and health status to detect an emergency. If the user collapses, it can issue a notification such as, "An emergency has occurred. Call an ambulance." The emergency response unit can also automatically send the user's location information to family members and emergency contacts. This allows for a quick response and appropriate assistance when a user experiences an emergency.

[0091] The monitoring tool can further include an environmental adaptation unit. The environmental adaptation unit can provide appropriate advice depending on the user's surrounding environment. For example, the generative AI collects and analyzes environmental data such as temperature, humidity, and noise level. If the user is in a hot environment, the AI ​​can provide advice such as "Don't forget to stay hydrated." The environmental adaptation unit can also suggest health management tips tailored to the user's living environment. This allows the tool to provide appropriate advice tailored to the user's surrounding environment, supporting a comfortable lifestyle.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The suspicious person detection unit uses behavioral patterns and facial recognition technology to identify suspicious individuals. For example, the generation AI collects data from cameras and sensors and analyzes behavioral patterns and facial features. It also compares this data with past data to detect abnormal behavior and analyzes audio data to detect intimidating speech and behavior. Intimidating speech and behavior can be identified by analyzing behavior such as repeatedly coming and going to a specific area, abnormally approaching a specific person, and changes in voice volume and tone. Step 2: The route guidance section provides route guidance in a conversational format. For example, the generation AI collects information about the user's destination and calculates the optimal route. It can also analyze the user's past travel history and prioritize frequently visited places. It provides information about surrounding landmarks and stores, making it easier for the user to understand their current location. For example, it asks a question such as "Where do you want to go?" and if the user answers "I want to go to the station," it calculates the optimal route and provides guidance such as "Go straight and turn right at the next corner." Step 3: The danger detection unit detects dangers such as traffic lights, cars, and bicycles, and issues an audio alert. For example, the generative AI collects data from cameras and sensors and analyzes the movement of traffic lights, cars, and bicycles. It can also analyze environmental data such as the surrounding temperature and humidity to notify users of abnormal weather and disaster risks. The user's location information can also be automatically sent to family members and emergency contacts. For example, when the traffic light turns red or a car is approaching, an audio alert is issued, such as "The traffic light is red. Please stop" or "A car is approaching. Be careful."

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 suspicious person discrimination unit that discriminates suspicious people using behavioral patterns and face recognition technology; A route guidance section that provides route guidance in a conversational format; It is equipped with a danger detection unit that detects dangers such as traffic lights, cars, and bicycles and notifies you with voice. A system characterized by:

2. The suspicious person determination unit The behavioral patterns are analyzed in real time and compared with past data to detect abnormal behavior.

2. The system of claim 1.

3. The route guidance unit Analyze the user's movement history and prioritize guidance to places the user frequently visits 2. The system of claim 1.

4. The danger detection unit When detecting the danger, the system also analyzes environmental data such as the surrounding temperature and humidity, and notifies users of the risk of abnormal weather or disasters.

2. The system of claim 1.

5. The suspicious person determination unit Detects the user's anxiety or fear and immediately makes a reassuring call in the voice of a family member 2. The system of claim 1.

6. The route guidance unit Providing detailed guidance and words of encouragement when users feel anxious 2. The system of claim 1.

7. The danger detection unit When the user senses danger, a voice message is provided to instantly reassure the user.

2. The system of claim 1.

8. The Visually Impaired Support Department Providing a reassuring voice message when the visually impaired person feels anxious 2. The system of claim 1.

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