System

Abstract

An object of the system according to the embodiment is to specify a location from a conversation or a moving image and provide necessary information.SOLUTION: A system according to an embodiment includes a conversation analysis unit, a video analysis unit, a position identification unit, and a notification unit. The conversation analysis unit analyzes a conversation content. The moving image analysis unit analyzes a moving image. The position specification unit specifies a location based on the information analyzed by the conversation analysis unit and the moving image analysis unit. The notification part notifies the location specified by the position specification part.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] With conventional technology, it was difficult to pinpoint a person's location from conversations or video, which was particularly important in emergencies or while on the move.

[0005] The system according to the embodiment aims to identify a location from conversations and videos and provide necessary information. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation analysis unit, a video analysis unit, a location identification unit, and a notification unit. The conversation analysis unit analyzes the content of the conversation. The video analysis unit analyzes the video. The location identification unit identifies a location based on information analyzed by the conversation analysis unit and the video analysis unit. The notification unit notifies the location identified by the location identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify a location from conversations and videos and provide necessary information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The location identification system according to an embodiment of the present invention is a system that uses LINE phone conversations and videos to identify the location of a person who is unable to locate themselves, and provides the person and the person talking with their destination. This allows the person to quickly and accurately identify their location and provide that information to the other party.

[0029] A location identification system according to an embodiment includes a conversation analysis unit, a video analysis unit, a position identification unit, and a notification unit. The conversation analysis unit analyzes the content of a conversation. For example, the conversation analysis unit converts a voice conversation into text and analyzes the content. The conversation analysis unit can also analyze the content of text chat. The conversation analysis unit can also understand the context of a conversation and extract important information. For example, the conversation analysis unit converts a voice conversation into text using speech recognition technology and analyzes the text using natural language processing technology. In the case of text chat, the conversation analysis unit directly analyzes the content of the chat and extracts important keywords and phrases. The video analysis unit analyzes video. For example, the video analysis unit analyzes real-time video and recognizes scenery and objects appearing in the video. The video analysis unit can also analyze recorded video and understand the content of the video. The video analysis unit can also identify specific locations and landmarks in the video. For example, the video analysis unit analyzes real-time video using computer vision technology and recognizes scenery and objects appearing in the video. In the case of recorded video, the video analysis unit analyzes the content of the video frame by frame and identifies specific places and landmarks. The location determination unit determines the location based on the information analyzed by the conversation analysis unit and the video analysis unit. For example, the location determination unit determines the location by combining the content of the conversation and the scenery captured in the video. The location determination unit can also determine the location by referring to the surrounding audio environment and past movement history. The location determination unit can also determine the location using GPS signals or Wi-Fi signals. For example, the location determination unit analyzes the content of the conversation and the scenery captured in the video and determines the location based on that information. When referring to the surrounding audio environment and past movement history, the location determination unit determines the location using voice recognition technology or a database. When using GPS signals or Wi-Fi signals, the location determination unit analyzes the signal strength and location information to determine the location. The notification unit notifies the user of the location determined by the location determination unit. For example, the notification unit notifies the user of the location by sending a push notification. The notification unit can also send an email notification or an SMS notification. The notification unit can also display location information through web applications or mobile applications.For example, the notification unit may send a push notification to quickly notify the user of their location. In the case of an email or SMS notification, the notification unit may send the location information to a designated contact. In the case of displaying the location information through a web or mobile application, the notification unit may display the location information in real time so that the user can check it. This allows the location identification system according to the embodiment to quickly and accurately identify the user's location and provide that information to the other party. This system is useful in various situations, such as when a user gets lost in a tourist spot or when evacuating during a disaster.

[0030] The location determination unit can determine a location by analyzing the audio environment. For example, the location determination unit analyzes the surrounding audio environment in addition to the content of conversations and videos. For example, it identifies station announcements and car sounds and determines a location based on the audio information. This improves accuracy by utilizing not only visual information but also audio information. The location determination unit also analyzes the surrounding audio environment in real time and detects specific audio patterns. For example, it identifies specific station announcements and specific car sounds and determines a location based on the identified audio patterns. The location determination unit also uses voice recognition technology to analyze the surrounding audio environment and determine a location based on specific audio patterns. For example, it identifies specific station announcements and specific car sounds and determines a location based on the identified audio patterns. This improves accuracy by analyzing the audio environment.

[0031] The location identification unit can predict the current location by referring to the party's past movement history. The location identification unit, for example, stores the party's past movement history in a database and develops an algorithm to predict the current location based on that data. For example, the current location is predicted based on places frequently visited in the past. The location identification unit also introduces an algorithm that analyzes the past movement history and detects specific patterns. For example, the current location is predicted based on places visited on specific days of the week or times of day. The location identification unit also analyzes the party's past movement history in real time to build a system that predicts the current location. For example, the current location is predicted based on past data and the information is provided. In this way, the current location can be predicted more accurately by referring to the past movement history.

[0032] The location determination unit can determine a location using information from surrounding Wi-Fi signals and Bluetooth devices. The location determination unit, for example, analyzes surrounding Wi-Fi signals and determines a location based on that information. For example, when connected to a specific Wi-Fi network, the location is determined based on the network's location information. The location determination unit also analyzes Bluetooth device information and determines a location based on that information. For example, when a specific Bluetooth device is nearby, the location is determined based on the device's location information. The location determination unit also combines and analyzes information from Wi-Fi signals and Bluetooth devices to build a location determination system. For example, multiple information sources can be integrated to improve the accuracy of location determination. As a result, the accuracy of location determination can be improved by using information from Wi-Fi signals and Bluetooth devices.

[0033] The notification unit can automatically notify nearby friends and family after determining the location. For example, the notification unit builds a system that automatically notifies nearby friends and family of the location after determining the location. For example, it automatically sends a message to a specific contact. The notification unit also adds a function to automatically notify nearby friends and family after determining the location. For example, it shares the location information through a specific app. The notification unit also develops a system that automatically notifies nearby friends and family after determining the location. For example, it automatically sends a notification when a specific condition is met. This enables a quick response by automatically notifying after determining the location.

[0034] The video analysis unit can identify a travel route by analyzing fragmented information from GPS signals in addition to the scenery captured in the video. For example, the video analysis unit builds a system that identifies a travel route by combining and analyzing the scenery captured in the video and fragmented information from GPS signals. For example, the current location is identified based on landmarks captured in the video and the GPS signal. The video analysis unit also analyzes fragmented information from GPS signals and develops an algorithm that identifies a travel route based on that information. For example, the travel route is estimated based on intermittent GPS signals. The video analysis unit also analyzes the scenery captured in the video and fragmented information from GPS signals in real time to develop a system that identifies a travel route. For example, the video and GPS signals are integrated to identify a travel route. This improves the accuracy of identifying a travel route by analyzing fragmented information from GPS signals.

[0035] The position identification unit can identify the means of transportation by analyzing speed and acceleration data during movement. The position identification unit, for example, analyzes speed data during movement and builds a system that identifies the means of transportation based on that information. For example, if the speed is above a certain level, it determines that the person is traveling by car. The position identification unit also analyzes acceleration data and develops an algorithm that identifies the means of transportation based on that information. For example, it identifies traveling by foot or bicycle based on acceleration patterns. The position identification unit also analyzes speed and acceleration data during movement in real time and develops a system that identifies the means of transportation. For example, it integrates speed and acceleration data to identify the means of transportation. In this way, the means of transportation can be identified by analyzing the speed and acceleration data.

[0036] The location determination unit can provide a combination of surrounding traffic information. The location determination unit, for example, builds a system that provides a combination of surrounding traffic information to determine a location while moving. For example, it proposes an optimal route based on congestion information and accident information. The location determination unit also collects surrounding traffic information in real time and develops an algorithm to determine a location based on that information. For example, it analyzes traffic information to determine the current location. The location determination unit also develops a system that provides a combination of surrounding traffic information to determine a location while moving. For example, it proposes an optimal route based on traffic information. In this way, by combining traffic information, the accuracy of determining a location while moving is improved.

[0037] The location identification unit can provide information on nearby tourist spots and events in real time. The location identification unit, for example, builds a system that provides information on nearby tourist spots and events in real time while traveling. For example, it suggests nearby tourist spots based on the current location. The location identification unit also collects information on nearby tourist spots and events in real time and develops an algorithm that makes suggestions based on that information. For example, it provides event information based on the current location. The location identification unit also develops a system that provides information on nearby tourist spots and events in real time while traveling. For example, it suggests the optimal route based on the tourist spot and event information. In this way, providing tourist spot and event information in real time increases the enjoyment of traveling.

[0038] The location identification unit can introduce temporary sharing settings that are valid only during specific time periods or locations. For example, the location identification unit builds a system that introduces temporary sharing settings that are valid only during specific time periods or locations when sharing location information. For example, location information is shared only during specific time periods. The location identification unit also develops a system that introduces temporary sharing settings and shares location information only at specific locations. For example, location information is shared only when arriving at a specific location. The location identification unit also develops an algorithm that introduces temporary sharing settings when sharing location information. For example, location information is shared only during specific time periods or locations. In this way, by introducing temporary sharing settings, location information can be shared while ensuring security.

[0039] The location identification unit can limit the parties with whom location information is shared, and provide information only to specific, trusted parties. The location identification unit, for example, builds a system that limits the parties with whom location information is shared, and provides information only to specific, trusted parties. For example, location information is shared only with pre-set, trusted contacts. The location identification unit also adds a function to provide information only to specific, trusted parties when sharing location information. For example, location information is shared only with specific contacts. The location identification unit also develops an algorithm that limits the parties with whom location information is shared, and provides information only to specific, trusted parties. For example, location information is shared only with trusted parties. This makes it possible to ensure security by providing information only to trusted parties.

[0040] The location identification unit can confirm safety by using footage from surrounding security cameras. For example, when sharing location information, the location identification unit builds a system that confirms safety by using footage from surrounding security cameras. For example, it analyzes security camera footage to confirm safety. The location identification unit also collects footage from surrounding security cameras in real time and develops an algorithm that confirms safety based on that information. For example, it analyzes security camera footage to confirm safety. The location identification unit also develops a system that confirms safety by using footage from surrounding security cameras when sharing location information. For example, it confirms safety based on security camera footage. In this way, safety can be confirmed by using security camera footage.

[0041] The location identification unit can display a prompt to confirm the consent of the parties before sharing the location information. For example, the location identification unit may build a system that displays a prompt to confirm the consent of the parties before sharing the location information. For example, a confirmation message may be displayed before sharing the location information. Furthermore, the location identification unit may develop an algorithm that displays a prompt to confirm the consent of the parties and performs confirmation before sharing the location information. For example, consent may be confirmed before sharing the location information. Furthermore, the location identification unit may develop a system that displays a prompt to confirm the consent of the parties before sharing the location information. For example, a confirmation message may be displayed before sharing the location information. This allows the consent to be confirmed, thereby protecting the privacy of the parties.

[0042] The location identification unit can analyze the parties' past visit history and preferences in addition to the conversation content and video, and suggest the most suitable shops and tourist spots. For example, the location identification unit can analyze the parties' past visit history in addition to the conversation content and video, and build a system that suggests the most suitable shops and tourist spots based on that information. For example, suggestions can be made based on places visited in the past. The location identification unit can also analyze the parties' preferences and develop an algorithm that suggests the most suitable shops and tourist spots based on that information. For example, it can suggest appropriate places based on preferences. The location identification unit can also analyze the parties' past visit history and preferences in addition to the conversation content and video, and suggest the most suitable shops and tourist spots based on that information. For example, suggestions can be made based on past data. In this way, the most suitable shops and tourist spots can be suggested by analyzing the parties' past visit history and preferences.

[0043] The location identification unit can display reviews and ratings of suggested shops and tourist spots in real time. The location identification unit, for example, builds a system that displays reviews and ratings of suggested shops and tourist spots in real time. For example, it displays user ratings and comments. The location identification unit also collects reviews and ratings of shops and tourist spots in real time and develops an algorithm to make suggestions based on that information. For example, it prioritizes suggestions of places with high ratings. The location identification unit also adds a function to display reviews and ratings of suggested shops and tourist spots in real time. For example, it displays user ratings and comments. This allows for better choices by displaying reviews and ratings in real time.

[0044] The location identification unit can display the congestion status of the nearest shops and tourist spots in real time. For example, the location identification unit builds a system that displays the congestion status of the nearest shops and tourist spots in real time. For example, it displays the degree of congestion. The location identification unit also collects the congestion status of shops and tourist spots in real time and develops an algorithm that makes suggestions based on that information. For example, it suggests the optimal location based on the degree of congestion. The location identification unit also adds a function that displays the congestion status of the nearest shops and tourist spots in real time. For example, it displays the degree of congestion. By displaying the congestion status in real time, the user can take action to avoid crowds.

[0045] The location identification unit can provide coupons and discount information for suggested shops and tourist spots. The location identification unit, for example, builds a system that provides coupons and discount information for suggested shops and tourist spots. For example, it displays coupon codes. The location identification unit also collects coupon and discount information for shops and tourist spots in real time and develops an algorithm to make suggestions based on that information. For example, it suggests the best location based on the discount information. The location identification unit also adds a function to provide coupons and discount information for suggested shops and tourist spots. For example, it displays coupon codes. As a result, the satisfaction of the parties involved is improved by providing coupons and discount information.

[0046] The location identification unit can analyze surrounding sounds to identify the current location. The location identification unit, for example, analyzes surrounding sounds during a disaster and builds a system that identifies the current location based on that information. For example, the current location is identified by analyzing sirens and people's screams. The location identification unit also collects surrounding sounds in real time and develops an algorithm that identifies the current location based on that information. For example, the current location is identified by analyzing sirens and people's screams. The location identification unit also analyzes surrounding sounds during a disaster and develops a system that identifies the current location based on that information. For example, the current location is identified by analyzing sirens and people's screams. In this way, the current location can be identified by analyzing surrounding sounds during a disaster.

[0047] The location identification unit can introduce an algorithm that refers to past disaster data and provides the safest evacuation route. The location identification unit, for example, refers to past disaster data and develops an algorithm that provides the safest evacuation route based on that information. For example, it proposes the optimal evacuation route based on past data. The location identification unit also analyzes past disaster data and builds a system that provides the safest evacuation route based on that information. For example, it proposes the optimal evacuation route based on past data. The location identification unit also introduces an algorithm that refers to past disaster data and provides the safest evacuation route based on that information. For example, it proposes the optimal evacuation route based on past data. In this way, the safest route can be provided by referring to past disaster data.

[0048] The location identification unit can display the congestion status of nearby evacuation shelters in real time. The location identification unit, for example, builds a system that displays the congestion status of nearby evacuation shelters in real time during a disaster. For example, it displays the degree of congestion at the evacuation shelter. The location identification unit also collects the congestion status of evacuation shelters in real time and develops an algorithm that makes suggestions based on that information. For example, it suggests the most suitable evacuation shelter based on the degree of congestion. The location identification unit also adds a function that displays the congestion status of nearby evacuation shelters in real time during a disaster. For example, it displays the degree of congestion at the evacuation shelter. This allows the most suitable evacuation shelter to be selected by displaying the congestion status in real time.

[0049] The location identification unit can provide a combination of surrounding traffic information and road conditions. The location identification unit, for example, builds a system that combines surrounding traffic information and road conditions when proposing evacuation routes. For example, it proposes an optimal route based on congestion information and road closure information. The location identification unit also collects surrounding traffic information and road conditions in real time and develops an algorithm that proposes evacuation routes based on that information. For example, it analyzes traffic information and proposes an optimal evacuation route. The location identification unit also develops a system that combines surrounding traffic information and road conditions when proposing evacuation routes. For example, it proposes an optimal evacuation route based on traffic information. In this way, the optimal evacuation route can be provided by combining traffic information and road conditions.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The location determination unit can also determine a location by analyzing the ambient temperature and humidity. For example, it estimates the current location based on weather data for a specific area. The location determination unit also analyzes changes in temperature and humidity in real time and develops an algorithm to determine the location based on that information. For example, it estimates the current location by identifying locations where the temperature changes rapidly. The location determination unit also combines temperature and humidity data with other information and analyzes it to build a system to determine the location. For example, it combines weather data with GPS signals to determine the location. In this way, utilizing weather data improves the accuracy of location determination.

[0052] The notification unit can also provide information on nearby emergency shelters and medical facilities after determining a user's location. For example, it notifies the user of the location of nearby shelters in the event of a disaster. The notification unit also builds a system that provides information on nearby medical facilities after determining a user's location. For example, it notifies the user of the location of the nearest hospital or clinic. The notification unit also adds a function to provide information on nearby emergency shelters and medical facilities after determining a user's location. For example, it can automatically send a notification when certain conditions are met. This enables a rapid response in an emergency.

[0053] The location determination unit can also determine a location using information from surrounding Wi-Fi signals and Bluetooth devices. For example, it analyzes surrounding Wi-Fi signals and determines a location based on that information. For example, if a user is connected to a specific Wi-Fi network, it determines a location based on the network's location information. The location determination unit also analyzes Bluetooth device information and determines a location based on that information. For example, if a specific Bluetooth device is nearby, it determines a location based on the device's location information. The location determination unit also combines and analyzes information from Wi-Fi signals and Bluetooth devices to build a location determination system. For example, it integrates multiple information sources to improve the accuracy of location determination. As a result, the accuracy of location determination can be improved by using information from Wi-Fi signals and Bluetooth devices.

[0054] The location identification unit can also analyze speed and acceleration data during movement to identify the means of transportation. For example, a system is constructed that analyzes speed data during movement and identifies the means of transportation based on that information. For example, if the speed is above a certain level, it is determined that the person is traveling by car. The location identification unit also analyzes acceleration data and develops an algorithm that identifies the means of transportation based on that information. For example, it identifies movement by walking or bicycle based on acceleration patterns. The location identification unit also analyzes speed and acceleration data during movement in real time to develop a system that identifies the means of transportation. For example, it integrates speed and acceleration data to identify the means of transportation. This makes it possible to identify the means of transportation by analyzing speed and acceleration data.

[0055] The location determination unit can also provide information on surrounding traffic in combination. For example, a system is constructed that provides information on surrounding traffic in combination with information on location while moving. For example, the optimal route is proposed based on congestion information and accident information. The location determination unit also collects surrounding traffic information in real time and develops an algorithm to determine location based on that information. For example, the current location is determined by analyzing traffic information. The location determination unit also develops a system that provides information on surrounding traffic in combination with information on location while moving. For example, the optimal route is proposed based on traffic information. In this way, by combining traffic information, the accuracy of determining location while moving is improved.

[0056] The location identification unit can also provide information on nearby tourist spots and events in real time. For example, a system is constructed that provides information on nearby tourist spots and events in real time while the user is on the move. For example, nearby tourist spots are suggested based on the user's current location. The location identification unit also collects information on nearby tourist spots and events in real time and develops an algorithm that makes suggestions based on that information. For example, event information is provided based on the user's current location. The location identification unit also develops a system that provides information on nearby tourist spots and events in real time while the user is on the move. For example, optimal routes are suggested based on tourist spot and event information. This allows the user to enjoy their travel more by providing information on tourist spots and events in real time.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The conversation analysis unit analyzes the content of the conversation. For example, the conversation analysis unit converts voice conversation into text and analyzes the content. The conversation analysis unit can also analyze the content of text chat. Furthermore, the conversation analysis unit can understand the context of the conversation and extract important information. For example, the conversation can be converted into text using voice recognition technology, and the text can be analyzed using natural language processing technology. In the case of text chat, the content of the chat can be directly analyzed and important keywords and phrases can be extracted. Step 2: The video analysis unit analyzes the video. For example, the video analysis unit analyzes real-time video and recognizes scenery and objects in the video. It can also analyze recorded video and understand the content of the video. It can also identify specific places and landmarks in the video. For example, computer vision technology is used to analyze real-time video and recognize scenery and objects in the video. In the case of recorded video, the content of the video is analyzed frame by frame to identify specific places and landmarks. Step 3: The location determination unit determines the location based on the information analyzed by the conversation analysis unit and the video analysis unit. For example, the location is determined by combining the content of the conversation and the scenery shown in the video. The location can also be determined by referring to the surrounding audio environment and past movement history. Furthermore, the location can be determined using GPS signals or Wi-Fi signals. For example, the content of the conversation and the scenery shown in the video are analyzed, and the location is determined based on that information. When referring to the surrounding audio environment and past movement history, the location is determined using voice recognition technology or a database. When using GPS signals or Wi-Fi signals, the location is determined by analyzing the signal strength and location information. Step 4: The notification unit notifies the user of the location determined by the location determination unit. For example, the notification unit may send a push notification to notify the user of the location. It may also send an email or SMS notification. The location information may also be displayed through a web or mobile application. For example, a push notification may be sent to quickly notify the user of the location. In the case of an email or SMS notification, the location information is sent to a specified contact. In the case of displaying the location through a web or mobile application, the location information is displayed in real time so that the user can check it.

[0059] (Example 2) The location identification system according to an embodiment of the present invention is a system that uses LINE phone conversations and videos to identify the location of a person who is unable to locate themselves, and provides the person and the person talking with their destination. This allows the person to quickly and accurately identify their location and provide that information to the other party.

[0060] A location identification system according to an embodiment includes a conversation analysis unit, a video analysis unit, a position identification unit, and a notification unit. The conversation analysis unit analyzes the content of a conversation. For example, the conversation analysis unit converts a voice conversation into text and analyzes the content. The conversation analysis unit can also analyze the content of text chat. The conversation analysis unit can also understand the context of a conversation and extract important information. For example, the conversation analysis unit converts a voice conversation into text using speech recognition technology and analyzes the text using natural language processing technology. In the case of text chat, the conversation analysis unit directly analyzes the content of the chat and extracts important keywords and phrases. The video analysis unit analyzes video. For example, the video analysis unit analyzes real-time video and recognizes scenery and objects appearing in the video. The video analysis unit can also analyze recorded video and understand the content of the video. The video analysis unit can also identify specific locations and landmarks in the video. For example, the video analysis unit analyzes real-time video using computer vision technology and recognizes scenery and objects appearing in the video. In the case of recorded video, the video analysis unit analyzes the content of the video frame by frame and identifies specific places and landmarks. The location determination unit determines the location based on the information analyzed by the conversation analysis unit and the video analysis unit. For example, the location determination unit determines the location by combining the content of the conversation and the scenery captured in the video. The location determination unit can also determine the location by referring to the surrounding audio environment and past movement history. The location determination unit can also determine the location using GPS signals or Wi-Fi signals. For example, the location determination unit analyzes the content of the conversation and the scenery captured in the video and determines the location based on that information. When referring to the surrounding audio environment and past movement history, the location determination unit determines the location using voice recognition technology or a database. When using GPS signals or Wi-Fi signals, the location determination unit analyzes the signal strength and location information to determine the location. The notification unit notifies the user of the location determined by the location determination unit. For example, the notification unit notifies the user of the location by sending a push notification. The notification unit can also send an email notification or an SMS notification. The notification unit can also display location information through web applications or mobile applications.For example, the notification unit may send a push notification to quickly notify the user of their location. In the case of an email or SMS notification, the notification unit may send the location information to a designated contact. In the case of displaying the location information through a web or mobile application, the notification unit may display the location information in real time so that the user can check it. This allows the location identification system according to the embodiment to quickly and accurately identify the user's location and provide that information to the other party. This system is useful in various situations, such as when a user gets lost in a tourist spot or when evacuating during a disaster.

[0061] The location determination unit can determine a location by analyzing the audio environment. For example, the location determination unit analyzes the surrounding audio environment in addition to the content of conversations and videos. For example, it identifies station announcements and car sounds and determines a location based on the audio information. This improves accuracy by utilizing not only visual information but also audio information. The location determination unit also analyzes the surrounding audio environment in real time and detects specific audio patterns. For example, it identifies specific station announcements and specific car sounds and determines a location based on the identified audio patterns. The location determination unit also uses voice recognition technology to analyze the surrounding audio environment and determine a location based on specific audio patterns. For example, it identifies specific station announcements and specific car sounds and determines a location based on the identified audio patterns. This improves accuracy by analyzing the audio environment.

[0062] The location identification unit can predict the current location by referring to the party's past movement history. The location identification unit, for example, stores the party's past movement history in a database and develops an algorithm to predict the current location based on that data. For example, the current location is predicted based on places frequently visited in the past. The location identification unit also introduces an algorithm that analyzes the past movement history and detects specific patterns. For example, the current location is predicted based on places visited on specific days of the week or times of day. The location identification unit also analyzes the party's past movement history in real time to build a system that predicts the current location. For example, the current location is predicted based on past data and the information is provided. In this way, the current location can be predicted more accurately by referring to the past movement history.

[0063] The location identification unit can use the emotion estimation function to analyze the emotional state of the person in question and provide appropriate support. The location identification unit, for example, uses the emotion estimation function to analyze the emotional state of the person in real time. For example, it detects emotions such as anxiety or confusion and provides appropriate support based on that information. The location identification unit also analyzes the emotional state of the person in question and builds a system that provides support according to the specific emotional state. For example, if the person is feeling anxious, it sends a reassuring message. The location identification unit also uses the emotion estimation function to analyze the emotional state of the person in question and provides appropriate support based on that information. For example, if the person is confused, it provides simple instructions. In this way, appropriate support can be provided by analyzing the emotional state.

[0064] The location determination unit can determine a location using information from surrounding Wi-Fi signals and Bluetooth devices. The location determination unit, for example, analyzes surrounding Wi-Fi signals and determines a location based on that information. For example, when connected to a specific Wi-Fi network, the location is determined based on the network's location information. The location determination unit also analyzes Bluetooth device information and determines a location based on that information. For example, when a specific Bluetooth device is nearby, the location is determined based on the device's location information. The location determination unit also combines and analyzes information from Wi-Fi signals and Bluetooth devices to build a location determination system. For example, multiple information sources can be integrated to improve the accuracy of location determination. As a result, the accuracy of location determination can be improved by using information from Wi-Fi signals and Bluetooth devices.

[0065] The notification unit can automatically notify nearby friends and family after determining the location. For example, the notification unit builds a system that automatically notifies nearby friends and family of the location after determining the location. For example, it automatically sends a message to a specific contact. The notification unit also adds a function to automatically notify nearby friends and family after determining the location. For example, it shares the location information through a specific app. The notification unit also develops a system that automatically notifies nearby friends and family after determining the location. For example, it automatically sends a notification when a specific condition is met. This enables a quick response by automatically notifying after determining the location.

[0066] The notification unit can use the emotion estimation function to automatically play messages or music that will reassure the person. For example, the notification unit uses the emotion estimation function to build a system that analyzes the emotional state of the person and automatically plays reassuring messages. For example, if the person is feeling anxious, an encouraging message is played. The notification unit also develops a system that analyzes the emotional state of the person and automatically plays reassuring music. For example, relaxing music is selected and played. The notification unit also uses the emotion estimation function to analyze the emotional state of the person and automatically plays reassuring messages or music based on that information. For example, appropriate content is selected and played depending on the emotional state. In this way, the anxiety of the person can be reduced by automatically playing reassuring messages or music.

[0067] The video analysis unit can identify a travel route by analyzing fragmented information from GPS signals in addition to the scenery captured in the video. For example, the video analysis unit builds a system that identifies a travel route by combining and analyzing the scenery captured in the video and fragmented information from GPS signals. For example, the current location is identified based on landmarks captured in the video and the GPS signal. The video analysis unit also analyzes fragmented information from GPS signals and develops an algorithm that identifies a travel route based on that information. For example, the travel route is estimated based on intermittent GPS signals. The video analysis unit also analyzes the scenery captured in the video and fragmented information from GPS signals in real time to develop a system that identifies a travel route. For example, the video and GPS signals are integrated to identify a travel route. This improves the accuracy of identifying a travel route by analyzing fragmented information from GPS signals.

[0068] The position identification unit can identify the means of transportation by analyzing speed and acceleration data during movement. The position identification unit, for example, analyzes speed data during movement and builds a system that identifies the means of transportation based on that information. For example, if the speed is above a certain level, it determines that the person is traveling by car. The position identification unit also analyzes acceleration data and develops an algorithm that identifies the means of transportation based on that information. For example, it identifies traveling by foot or bicycle based on acceleration patterns. The position identification unit also analyzes speed and acceleration data during movement in real time and develops a system that identifies the means of transportation. For example, it integrates speed and acceleration data to identify the means of transportation. In this way, the means of transportation can be identified by analyzing the speed and acceleration data.

[0069] The location identification unit can use the emotion estimation function to analyze the stress level of the person and suggest a relaxing route. The location identification unit, for example, uses the emotion estimation function to build a system that analyzes the stress level of the person in real time and suggests a relaxing route. For example, if stress is high, a quiet route is suggested. The location identification unit also analyzes the emotional state of the person and develops an algorithm that suggests a route according to the stress level. For example, a scenic route that is relaxing is suggested. The location identification unit also uses the emotion estimation function to analyze the stress level of the person and suggests a relaxing route based on that information. For example, a route with low stress is selected and suggested. In this way, a relaxing route can be suggested by analyzing the stress level.

[0070] The location determination unit can provide a combination of surrounding traffic information. The location determination unit, for example, builds a system that provides a combination of surrounding traffic information to determine a location while moving. For example, it proposes an optimal route based on congestion information and accident information. The location determination unit also collects surrounding traffic information in real time and develops an algorithm to determine a location based on that information. For example, it analyzes traffic information to determine the current location. The location determination unit also develops a system that provides a combination of surrounding traffic information to determine a location while moving. For example, it proposes an optimal route based on traffic information. In this way, by combining traffic information, the accuracy of determining a location while moving is improved.

[0071] The location identification unit can provide information on nearby tourist spots and events in real time. The location identification unit, for example, builds a system that provides information on nearby tourist spots and events in real time while traveling. For example, it suggests nearby tourist spots based on the current location. The location identification unit also collects information on nearby tourist spots and events in real time and develops an algorithm that makes suggestions based on that information. For example, it provides event information based on the current location. The location identification unit also develops a system that provides information on nearby tourist spots and events in real time while traveling. For example, it suggests the optimal route based on the tourist spot and event information. In this way, providing tourist spot and event information in real time increases the enjoyment of traveling.

[0072] The location identification unit can use the emotion estimation function to suggest places and events that the party is likely to be interested in. For example, the location identification unit uses the emotion estimation function to analyze the party's emotional state and build a system that suggests places and events that the party is likely to be interested in. For example, it suggests places where positive emotions are strong. The location identification unit also analyzes the party's emotional state and develops an algorithm that suggests places and events that the party is likely to be interested in. For example, it suggests appropriate places and events depending on the emotional state. The location identification unit also uses the emotion estimation function to analyze the party's emotional state and suggests places and events that the party is likely to be interested in based on that information. For example, it suggests appropriate places and events depending on the emotional state. In this way, suggesting places and events that the party is likely to be interested in increases enjoyment during travel.

[0073] The location identification unit can introduce temporary sharing settings that are valid only during specific time periods or locations. For example, the location identification unit builds a system that introduces temporary sharing settings that are valid only during specific time periods or locations when sharing location information. For example, location information is shared only during specific time periods. The location identification unit also develops a system that introduces temporary sharing settings and shares location information only at specific locations. For example, location information is shared only when arriving at a specific location. The location identification unit also develops an algorithm that introduces temporary sharing settings when sharing location information. For example, location information is shared only during specific time periods or locations. In this way, by introducing temporary sharing settings, location information can be shared while ensuring security.

[0074] The location identification unit can limit the parties with whom location information is shared, and provide information only to specific, trusted parties. The location identification unit, for example, builds a system that limits the parties with whom location information is shared, and provides information only to specific, trusted parties. For example, location information is shared only with pre-set, trusted contacts. The location identification unit also adds a function to provide information only to specific, trusted parties when sharing location information. For example, location information is shared only with specific contacts. The location identification unit also develops an algorithm that limits the parties with whom location information is shared, and provides information only to specific, trusted parties. For example, location information is shared only with trusted parties. This makes it possible to ensure security by providing information only to trusted parties.

[0075] The location identification unit can use the emotion estimation function to automatically send a security message that puts the parties at ease. For example, the location identification unit uses the emotion estimation function to analyze the parties' emotional state and build a system that automatically sends a reassuring security message. For example, if the parties are feeling anxious, a reassuring message is sent. The location identification unit also analyzes the parties' emotional state and develops an algorithm that automatically sends a reassuring security message. For example, an appropriate message is sent depending on the emotional state. The location identification unit also uses the emotion estimation function to analyze the parties' emotional state and automatically sends a reassuring security message based on that information. For example, an appropriate message is sent depending on the emotional state. In this way, the parties' anxiety can be reduced by automatically sending a reassuring security message.

[0076] The location identification unit can confirm safety by using footage from surrounding security cameras. For example, when sharing location information, the location identification unit builds a system that confirms safety by using footage from surrounding security cameras. For example, it analyzes security camera footage to confirm safety. The location identification unit also collects footage from surrounding security cameras in real time and develops an algorithm that confirms safety based on that information. For example, it analyzes security camera footage to confirm safety. The location identification unit also develops a system that confirms safety by using footage from surrounding security cameras when sharing location information. For example, it confirms safety based on security camera footage. In this way, safety can be confirmed by using security camera footage.

[0077] The location identification unit can display a prompt to confirm the consent of the parties before sharing the location information. For example, the location identification unit may build a system that displays a prompt to confirm the consent of the parties before sharing the location information. For example, a confirmation message may be displayed before sharing the location information. Furthermore, the location identification unit may develop an algorithm that displays a prompt to confirm the consent of the parties and performs confirmation before sharing the location information. For example, consent may be confirmed before sharing the location information. Furthermore, the location identification unit may develop a system that displays a prompt to confirm the consent of the parties before sharing the location information. For example, a confirmation message may be displayed before sharing the location information. This allows the consent to be confirmed, thereby protecting the privacy of the parties.

[0078] The location identification unit can use the emotion estimation function to automatically stop location information sharing if the parties feel anxious. For example, the location identification unit uses the emotion estimation function to analyze the emotional state of the parties and build a system that automatically stops location information sharing if the parties feel anxious. For example, location information sharing is stopped if the parties feel anxious. The location identification unit also analyzes the emotional state of the parties and develops an algorithm that automatically stops location information sharing if the parties feel anxious. For example, location information sharing is stopped according to the emotional state. The location identification unit also uses the emotion estimation function to analyze the emotional state of the parties and automatically stops location information sharing if the parties feel anxious based on that information. For example, location information sharing is stopped according to the emotional state. In this way, the privacy of the parties can be protected by automatically stopping location information sharing if the parties feel anxious.

[0079] The location identification unit can analyze the parties' past visit history and preferences in addition to the conversation content and video, and suggest the most suitable shops and tourist spots. For example, the location identification unit can analyze the parties' past visit history in addition to the conversation content and video, and build a system that suggests the most suitable shops and tourist spots based on that information. For example, suggestions can be made based on places visited in the past. The location identification unit can also analyze the parties' preferences and develop an algorithm that suggests the most suitable shops and tourist spots based on that information. For example, it can suggest appropriate places based on preferences. The location identification unit can also analyze the parties' past visit history and preferences in addition to the conversation content and video, and suggest the most suitable shops and tourist spots based on that information. For example, suggestions can be made based on past data. In this way, the most suitable shops and tourist spots can be suggested by analyzing the parties' past visit history and preferences.

[0080] The location identification unit can display reviews and ratings of suggested shops and tourist spots in real time. The location identification unit, for example, builds a system that displays reviews and ratings of suggested shops and tourist spots in real time. For example, it displays user ratings and comments. The location identification unit also collects reviews and ratings of shops and tourist spots in real time and develops an algorithm to make suggestions based on that information. For example, it prioritizes suggestions of places with high ratings. The location identification unit also adds a function to display reviews and ratings of suggested shops and tourist spots in real time. For example, it displays user ratings and comments. This allows for better choices by displaying reviews and ratings in real time.

[0081] The location identification unit can use the emotion estimation function to suggest shops and tourist spots that match the mood of the person involved. For example, the location identification unit uses the emotion estimation function to analyze the emotional state of the person involved and build a system that suggests shops and tourist spots that match the mood based on that information. For example, it can suggest places where people can relax. The location identification unit also analyzes the emotional state of the person involved and develops an algorithm that suggests shops and tourist spots that match the mood based on that information. For example, it can suggest appropriate places depending on the emotional state. The location identification unit also uses the emotion estimation function to analyze the emotional state of the person involved and proposes shops and tourist spots that match the mood based on that information. For example, it can suggest appropriate places depending on the emotional state. In this way, suggestions that match the mood can be made, thereby improving the satisfaction of the person involved.

[0082] The location identification unit can display the congestion status of the nearest shops and tourist spots in real time. For example, the location identification unit builds a system that displays the congestion status of the nearest shops and tourist spots in real time. For example, it displays the degree of congestion. The location identification unit also collects the congestion status of shops and tourist spots in real time and develops an algorithm that makes suggestions based on that information. For example, it suggests the optimal location based on the degree of congestion. The location identification unit also adds a function that displays the congestion status of the nearest shops and tourist spots in real time. For example, it displays the degree of congestion. By displaying the congestion status in real time, the user can take action to avoid crowds.

[0083] The location identification unit can provide coupons and discount information for suggested shops and tourist spots. The location identification unit, for example, builds a system that provides coupons and discount information for suggested shops and tourist spots. For example, it displays coupon codes. The location identification unit also collects coupon and discount information for shops and tourist spots in real time and develops an algorithm to make suggestions based on that information. For example, it suggests the best location based on the discount information. The location identification unit also adds a function to provide coupons and discount information for suggested shops and tourist spots. For example, it displays coupon codes. As a result, the satisfaction of the parties involved is improved by providing coupons and discount information.

[0084] The location identification unit can use the emotion estimation function to suggest places and activities where the person can relax. For example, the location identification unit uses the emotion estimation function to analyze the emotional state of the person and build a system that suggests places and activities where the person can relax based on that information. For example, it suggests places where the person can relax. The location identification unit also analyzes the emotional state of the person and develops an algorithm that suggests places and activities where the person can relax based on that information. For example, it suggests appropriate places and activities depending on the emotional state. The location identification unit also uses the emotion estimation function to analyze the emotional state of the person and suggests places and activities where the person can relax based on that information. For example, it suggests appropriate places and activities depending on the emotional state. In this way, by suggesting places and activities where the person can relax, the satisfaction of the person is improved.

[0085] The location identification unit can analyze surrounding sounds to identify the current location. The location identification unit, for example, analyzes surrounding sounds during a disaster and builds a system that identifies the current location based on that information. For example, the current location is identified by analyzing sirens and people's screams. The location identification unit also collects surrounding sounds in real time and develops an algorithm that identifies the current location based on that information. For example, the current location is identified by analyzing sirens and people's screams. The location identification unit also analyzes surrounding sounds during a disaster and develops a system that identifies the current location based on that information. For example, the current location is identified by analyzing sirens and people's screams. In this way, the current location can be identified by analyzing surrounding sounds during a disaster.

[0086] The location identification unit can introduce an algorithm that refers to past disaster data and provides the safest evacuation route. The location identification unit, for example, refers to past disaster data and develops an algorithm that provides the safest evacuation route based on that information. For example, it proposes the optimal evacuation route based on past data. The location identification unit also analyzes past disaster data and builds a system that provides the safest evacuation route based on that information. For example, it proposes the optimal evacuation route based on past data. The location identification unit also introduces an algorithm that refers to past disaster data and provides the safest evacuation route based on that information. For example, it proposes the optimal evacuation route based on past data. In this way, the safest route can be provided by referring to past disaster data.

[0087] The location identification unit can use the emotion estimation function to analyze the stress level of the person in question and suggest a safe evacuation route. The location identification unit, for example, uses the emotion estimation function to analyze the stress level of the person in question in real time and build a system that suggests a safe evacuation route. For example, if stress is high, a safe and quiet route is suggested. The location identification unit also analyzes the emotional state of the person in question and develops an algorithm that suggests an evacuation route according to the stress level. For example, a scenic route that allows relaxation is suggested. The location identification unit also uses the emotion estimation function to analyze the stress level of the person in question and suggests a safe evacuation route based on that information. For example, a route with low stress is selected and suggested. In this way, a safe evacuation route can be suggested by analyzing the stress level.

[0088] The location identification unit can display the congestion status of nearby evacuation shelters in real time. The location identification unit, for example, builds a system that displays the congestion status of nearby evacuation shelters in real time during a disaster. For example, it displays the degree of congestion at the evacuation shelter. The location identification unit also collects the congestion status of evacuation shelters in real time and develops an algorithm that makes suggestions based on that information. For example, it suggests the most suitable evacuation shelter based on the degree of congestion. The location identification unit also adds a function that displays the congestion status of nearby evacuation shelters in real time during a disaster. For example, it displays the degree of congestion at the evacuation shelter. This allows the most suitable evacuation shelter to be selected by displaying the congestion status in real time.

[0089] The location identification unit can provide a combination of surrounding traffic information and road conditions. The location identification unit, for example, builds a system that combines surrounding traffic information and road conditions when proposing evacuation routes. For example, it proposes an optimal route based on congestion information and road closure information. The location identification unit also collects surrounding traffic information and road conditions in real time and develops an algorithm that proposes evacuation routes based on that information. For example, it analyzes traffic information and proposes an optimal evacuation route. The location identification unit also develops a system that combines surrounding traffic information and road conditions when proposing evacuation routes. For example, it proposes an optimal evacuation route based on traffic information. In this way, the optimal evacuation route can be provided by combining traffic information and road conditions.

[0090] The location identification unit can use the emotion estimation function to automatically play messages or music that will reassure the person. For example, the location identification unit uses the emotion estimation function to analyze the emotional state of the person and build a system that automatically plays reassuring messages. For example, if the person is feeling anxious, an encouraging message is played. The location identification unit also develops a system that analyzes the emotional state of the person and automatically plays reassuring music. For example, relaxing music is selected and played. The location identification unit also uses the emotion estimation function to analyze the emotional state of the person and automatically plays reassuring messages or music based on that information. For example, appropriate content is selected and played depending on the emotional state. In this way, the anxiety of the person can be reduced by automatically playing reassuring messages or music.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The location determination unit can also determine a location by analyzing the ambient temperature and humidity. For example, it estimates the current location based on weather data for a specific area. The location determination unit also analyzes changes in temperature and humidity in real time and develops an algorithm to determine the location based on that information. For example, it estimates the current location by identifying locations where the temperature changes rapidly. The location determination unit also combines temperature and humidity data with other information and analyzes it to build a system to determine the location. For example, it combines weather data with GPS signals to determine the location. In this way, utilizing weather data improves the accuracy of location determination.

[0093] The notification unit can also provide information on nearby emergency shelters and medical facilities after determining a user's location. For example, it notifies the user of the location of nearby shelters in the event of a disaster. The notification unit also builds a system that provides information on nearby medical facilities after determining a user's location. For example, it notifies the user of the location of the nearest hospital or clinic. The notification unit also adds a function to provide information on nearby emergency shelters and medical facilities after determining a user's location. For example, it can automatically send a notification when certain conditions are met. This enables a rapid response in an emergency.

[0094] The location identification unit can use the emotion estimation function to analyze the emotional state of the person in question and provide appropriate support. For example, the emotion estimation function is used to analyze the emotional state of the person in question in real time. For example, emotions such as anxiety and confusion can be detected and appropriate support can be provided based on that information. The location identification unit also analyzes the emotional state of the person in question and builds a system that provides support according to the specific emotional state. For example, if the person is feeling anxious, a reassuring message can be sent. The location identification unit also uses the emotion estimation function to analyze the emotional state of the person in question and provide appropriate support based on that information. For example, if the person is confused, simple instructions can be provided. In this way, appropriate support can be provided by analyzing the emotional state.

[0095] The location determination unit can also determine a location using information from surrounding Wi-Fi signals and Bluetooth devices. For example, it analyzes surrounding Wi-Fi signals and determines a location based on that information. For example, if a user is connected to a specific Wi-Fi network, it determines a location based on the network's location information. The location determination unit also analyzes Bluetooth device information and determines a location based on that information. For example, if a specific Bluetooth device is nearby, it determines a location based on the device's location information. The location determination unit also combines and analyzes information from Wi-Fi signals and Bluetooth devices to build a location determination system. For example, it integrates multiple information sources to improve the accuracy of location determination. As a result, the accuracy of location determination can be improved by using information from Wi-Fi signals and Bluetooth devices.

[0096] The notification unit can use the emotion estimation function to automatically play messages or music that will reassure the person. For example, a system can be constructed that uses the emotion estimation function to analyze the emotional state of the person and automatically play reassuring messages. For example, if the person is feeling anxious, an encouraging message can be played. The notification unit can also develop a system that analyzes the emotional state of the person and automatically play reassuring music. For example, relaxing music can be selected and played. The notification unit can also use the emotion estimation function to analyze the emotional state of the person and automatically play reassuring messages or music based on that information. For example, appropriate content can be selected and played depending on the emotional state. In this way, the anxiety of the person can be reduced by automatically playing reassuring messages or music.

[0097] The location identification unit can also analyze speed and acceleration data during movement to identify the means of transportation. For example, a system is constructed that analyzes speed data during movement and identifies the means of transportation based on that information. For example, if the speed is above a certain level, it is determined that the person is traveling by car. The location identification unit also analyzes acceleration data and develops an algorithm that identifies the means of transportation based on that information. For example, it identifies movement by walking or bicycle based on acceleration patterns. The location identification unit also analyzes speed and acceleration data during movement in real time to develop a system that identifies the means of transportation. For example, it integrates speed and acceleration data to identify the means of transportation. This makes it possible to identify the means of transportation by analyzing speed and acceleration data.

[0098] The location identification unit can use the emotion estimation function to analyze the stress level of the person in question and suggest a relaxing route. For example, a system can be constructed that uses the emotion estimation function to analyze the stress level of the person in question in real time and suggest a relaxing route. For example, if stress is high, a quiet route can be suggested. The location identification unit can also develop an algorithm that analyzes the emotional state of the person in question and suggests a route based on the stress level. For example, a scenic route that is relaxing can be suggested. The location identification unit can also use the emotion estimation function to analyze the stress level of the person in question and suggest a relaxing route based on that information. For example, a route with low stress can be selected and suggested. In this way, a relaxing route can be suggested by analyzing the stress level.

[0099] The location determination unit can also provide information on surrounding traffic in combination. For example, a system is constructed that provides information on surrounding traffic in combination with information on location while moving. For example, the optimal route is proposed based on congestion information and accident information. The location determination unit also collects surrounding traffic information in real time and develops an algorithm to determine location based on that information. For example, the current location is determined by analyzing traffic information. The location determination unit also develops a system that provides information on surrounding traffic in combination with information on location while moving. For example, the optimal route is proposed based on traffic information. In this way, by combining traffic information, the accuracy of determining location while moving is improved.

[0100] The location identification unit can also provide information on nearby tourist spots and events in real time. For example, a system is constructed that provides information on nearby tourist spots and events in real time while the user is on the move. For example, nearby tourist spots are suggested based on the user's current location. The location identification unit also collects information on nearby tourist spots and events in real time and develops an algorithm that makes suggestions based on that information. For example, event information is provided based on the user's current location. The location identification unit also develops a system that provides information on nearby tourist spots and events in real time while the user is on the move. For example, optimal routes are suggested based on tourist spot and event information. This allows the user to enjoy their travel more by providing information on tourist spots and events in real time.

[0101] The location identification unit can use the emotion estimation function to suggest places and events that the person concerned is likely to be interested in. For example, the emotion estimation function is used to analyze the emotional state of the person concerned and build a system that suggests places and events that the person concerned is likely to be interested in. For example, places where positive emotions are strong are suggested. The location identification unit also analyzes the emotional state of the person concerned and develops an algorithm that suggests places and events that the person concerned is likely to be interested in. For example, appropriate places and events are suggested depending on the emotional state. The location identification unit also uses the emotion estimation function to analyze the emotional state of the person concerned and suggests places and events that the person concerned is likely to be interested in based on that information. For example, appropriate places and events are suggested depending on the emotional state. This allows the person concerned to enjoy more while traveling by suggesting places and events that the person concerned is likely to be interested in.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The conversation analysis unit analyzes the content of the conversation. For example, the conversation analysis unit converts voice conversation into text and analyzes the content. The conversation analysis unit can also analyze the content of text chat. Furthermore, the conversation analysis unit can understand the context of the conversation and extract important information. For example, the conversation can be converted into text using voice recognition technology, and the text can be analyzed using natural language processing technology. In the case of text chat, the content of the chat can be directly analyzed and important keywords and phrases can be extracted. Step 2: The video analysis unit analyzes the video. For example, the video analysis unit analyzes real-time video and recognizes scenery and objects in the video. It can also analyze recorded video and understand the content of the video. It can also identify specific places and landmarks in the video. For example, computer vision technology is used to analyze real-time video and recognize scenery and objects in the video. In the case of recorded video, the content of the video is analyzed frame by frame to identify specific places and landmarks. Step 3: The location determination unit determines the location based on the information analyzed by the conversation analysis unit and the video analysis unit. For example, the location is determined by combining the content of the conversation and the scenery shown in the video. The location can also be determined by referring to the surrounding audio environment and past movement history. Furthermore, the location can be determined using GPS signals or Wi-Fi signals. For example, the content of the conversation and the scenery shown in the video are analyzed, and the location is determined based on that information. When referring to the surrounding audio environment and past movement history, the location is determined using voice recognition technology or a database. When using GPS signals or Wi-Fi signals, the location is determined by analyzing the signal strength and location information. Step 4: The notification unit notifies the user of the location determined by the location determination unit. For example, the notification unit may send a push notification to notify the user of the location. It may also send an email or SMS notification. The location information may also be displayed through a web or mobile application. For example, a push notification may be sent to quickly notify the user of the location. In the case of an email or SMS notification, the location information is sent to a specified contact. In the case of displaying the location through a web or mobile application, the location information is displayed in real time so that the user can check it.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 conversation analysis unit that analyzes the content of the conversation; a video analysis unit that analyzes the video; a location identification unit that identifies a location based on information analyzed by the conversation analysis unit and the video analysis unit; a notification unit that notifies the location identified by the location identification unit. A system characterized by:

2. The position identification unit Analyzing the audio environment to determine location 2. The system of claim 1.

3. The position identification unit Predicting the current location of the person by looking at their past movement history 2. The system of claim 1.

4. The position identification unit Analyze the emotional state of the person involved and provide appropriate support 2. The system of claim 1.

5. The position identification unit Uses information from surrounding Wi-Fi signals and Bluetooth devices to determine location 2. The system of claim 1.

6. The notification unit Automatically notify nearby friends and family after locating you 2. The system of claim 1.

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