System

Abstract

An object of a system according to an embodiment is to immediately provide the latest information to a person passing through a door.SOLUTION: A system includes a detection unit, an acquisition unit, and a display unit. The detection unit detects a person passing through the door. The acquisition unit acquires latest information related to the person detected by the detection unit. The display unit displays the information acquired by the acquisition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of being unable to immediately provide the latest information to people passing through the door, making it inconvenient.

[0005] The system according to the embodiment aims to provide the latest information instantly to anyone passing through the door. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, an acquisition unit, and a display unit. The detection unit detects a person passing through a door. The acquisition unit acquires the latest information related to the person detected by the detection unit. The display unit displays the information acquired by the acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide immediate, up-to-date information to anyone passing through the door. [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) An information provision system according to an embodiment of the present invention cooperates with a generation AI to provide the latest information to people passing by. The information provision system detects people passing by and acquires and displays the latest information related to the person. For example, the information provision system may instantly display weather forecasts, traffic information, and the like to support users going out. For example, the information provision system uses a sensor to detect people passing through a door. The sensor detects the person's movement and determines when the person will pass through the door. For example, infrared sensors or cameras can be used to detect the person's movement. Next, the information provision system uses a generation AI to acquire the latest information related to the detected person. The generation AI collects and analyzes the latest data, such as weather forecasts and traffic information, from the Internet. For example, the information provision system can acquire current weather and traffic congestion information in real time. The acquired information is displayed on a display of the information provision system. The display is installed inside or outside the door in a location that is easily visible to people passing by. For example, the display could be installed on the top or side of the door. This allows users to instantly check the latest information when passing through the door. For example, users can check the weather forecast before going out to decide whether to bring an umbrella, or check traffic information to select the optimal route. The information provision system can also learn the user's past behavioral history and provide individually customized information. For example, it can provide more appropriate information based on the means of transportation and destinations frequently used by a particular user. This allows the information provision system to provide the user with the latest information and support their outings. For example, by providing the user with the latest information quickly and accurately when they walk through a door, it improves user convenience. Furthermore, by learning the user's past behavioral history, it is possible to provide individually customized information and provide more appropriate support.

[0029] An information provision system according to an embodiment includes a detection unit, an acquisition unit, and a display unit. The detection unit detects a passing person. For example, an infrared sensor or a camera can be used to detect the passing person. The detection unit detects human movement using, for example, an infrared sensor. The detection unit can also detect human movement using a camera. For example, an infrared sensor detects human movement using infrared light and determines whether a person is present within a detection range. A camera analyzes video to detect human movement and determine whether a person is present within a specific area. The acquisition unit uses a generation AI to acquire the latest information related to the person detected by the detection unit. The acquisition unit collects and analyzes the latest data, such as weather forecasts and traffic information, from the Internet. For example, the generation AI acquires weather forecast data and analyzes current weather and forecasts. The generation AI can also acquire traffic information data and analyze current traffic conditions and congestion information. Furthermore, the generation AI can acquire and analyze data such as news and event information. For example, the generation AI collects and analyzes the latest information from news sites and event calendars. The display unit displays the information acquired by the acquisition unit. The display unit is installed, for example, inside or outside the door, in a position that is easily visible to passersby. The display unit is installed, for example, above the door, in a position where passersby naturally look. The display unit can also be installed on the side of the door, in a position that is easily visible to passersby as they pass. Furthermore, the display unit can adjust the brightness and contrast of the display to display information that is easy to see. For example, the display unit automatically adjusts the brightness of the display according to the ambient brightness to make the information easy to see. This allows the information provision system according to the embodiment to provide passersby with the latest information and support them in going out. For example, when a user passes through a door, the user can instantly check the latest information, such as a weather forecast or traffic information. Furthermore, the information provision system can learn the user's past behavior history and provide individually customized information, thereby providing more appropriate support.

[0030] The detection unit can detect human movement using an infrared sensor or a camera. Examples of infrared sensors include sensors that use infrared rays to detect human movement. An infrared sensor detects human movement by emitting infrared rays and detecting their reflection. For example, when a person enters its detection range, the infrared sensor changes the reflection of the infrared rays, and the infrared sensor detects the human movement by detecting this change. Examples of cameras include cameras that analyze video to detect human movement. The camera analyzes the video in real time to determine whether a person is present in a specific area. For example, the camera detects movement in the video and analyzes whether the movement is human movement. Thus, human movement can be accurately detected using an infrared sensor or a camera. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input video data acquired by a camera to a generation AI and cause the generation AI to execute a process of detecting human movement from the video data.

[0031] The acquisition unit can collect and analyze the latest weather forecast or traffic information data from the Internet. Weather forecast data includes, for example, data from the Japan Meteorological Agency and data from private weather services. The acquisition unit can, for example, acquire data from the Japan Meteorological Agency and analyze the current weather and forecast. The acquisition unit can also acquire data from private weather services and analyze detailed weather information. Traffic information data includes, for example, data from a traffic control center and data from a navigation service. The acquisition unit can, for example, acquire data from a traffic control center and analyze current traffic conditions and congestion information. The acquisition unit can also acquire data from a navigation service and analyze optimal route information. In this way, by collecting and analyzing the latest data from the Internet, it is possible to provide the latest information. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input weather forecast or traffic information data acquired from the Internet to the generation AI and have the generation AI analyze the data.

[0032] The display unit can be installed inside or outside the door and positioned at a location that is easy for passersby to see. An easy-to-see location includes, for example, eye level or an angle that provides good visibility. The display unit can be installed, for example, at the top of the door and positioned at a location where passersby naturally look. The display unit can also be installed on the side of the door and positioned at a location that is easy for passersby to see as they pass. Furthermore, the display unit can adjust the brightness and contrast of the display to display information that is easy to see. For example, the display unit can automatically adjust the brightness of the display according to the ambient brightness to make the information easy to see. As a result, by placing the display unit at a location that is easy to see, passersby can easily check the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can have the generation AI adjust the brightness and contrast of the display.

[0033] The acquisition unit can learn the user's past behavioral history and provide individually tailored information. The individually tailored information includes, for example, the user's interests and past behavioral history. For example, the acquisition unit can learn the user's past behavioral history and provide information based on the user's frequently used means of transportation and destinations. The acquisition unit can also provide related news and event information based on the user's interests. For example, the acquisition unit can learn data on places the user has visited in the past and means of transportation used by the user and provide related information. This allows the system to provide more appropriate information by learning the user's past behavioral history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past behavioral history data into a generation AI and have the generation AI learn the data and provide information.

[0034] The acquisition unit can acquire and analyze weather forecast or traffic information data using an API. Examples of APIs include a REST API and a SOAP API. The acquisition unit can acquire and analyze weather forecast data using a REST API, for example. The acquisition unit can also acquire and analyze traffic information data using a SOAP API. For example, the acquisition unit can acquire weather forecast data using an API from the Japan Meteorological Agency and analyze the current weather and forecast. The acquisition unit can also acquire traffic information data using an API from a traffic control center and analyze current traffic conditions and congestion information. In this way, by using an API, data can be acquired and analyzed efficiently. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired through the API into a generation AI and have the generation AI analyze the data.

[0035] The acquisition unit can learn the user's past behavioral history using a cloud-based database. Examples of cloud-based databases include AWS (registered trademark), Google (registered trademark), and Azure (registered trademark). The acquisition unit can learn the user's past behavioral history using, for example, an AWS database. The acquisition unit can also learn the behavioral history using a Google Cloud database. For example, the acquisition unit can learn the user's behavioral history data stored in an AWS database and provide related information. The acquisition unit can also learn data stored in a Google Cloud database and provide information based on the user's interests. This allows the cloud-based database to be used to efficiently learn the behavioral history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the behavioral history data acquired from the cloud-based database into a generation AI and cause the generation AI to learn the data.

[0036] The detection unit can analyze the height and body type of a passerby and select an appropriate detection method. The detection unit, for example, measures the height of the passerby and adjusts the height of the sensor appropriately. For example, the detection unit can measure the height of the passerby using a camera and adjust the height of the sensor. The detection unit can also analyze the body type of the passerby and optimize the detection range. For example, the detection unit can analyze the body type of the passerby using a camera and adjust the detection range. The detection unit can also apply different detection algorithms based on the body type of the passerby. For example, the detection unit selects an optimal detection algorithm depending on the body type of the passerby. This improves detection accuracy by selecting an optimal detection method depending on the height and body type of the passerby. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input image data of the passerby acquired by a camera to a generation AI and have the generation AI analyze the height and body type.

[0037] The detection unit can analyze the movement patterns of passersby and detect abnormal movement. The detection unit, for example, analyzes the walking patterns of passersby and detects abnormal movement. For example, the detection unit can analyze the walking patterns of passersby using a camera and detect abnormal movement. The detection unit can also analyze the hand movements of passersby and detect unnatural movement. For example, the detection unit can analyze the hand movements of passersby using a camera and detect unnatural movement. The detection unit can also analyze the posture of passersby and detect abnormal posture. For example, the detection unit can analyze the posture of passersby using a camera and detect abnormal posture. In this way, abnormal movement can be detected by analyzing the movement patterns of passersby. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input movement data of passersby acquired by a camera to the generation AI and cause the generation AI to analyze the movement patterns.

[0038] The detection unit can identify the clothing and belongings of passersby and perform detection based on specific conditions. For example, the detection unit can identify the clothing of passersby and detect specific colors and designs. For example, the detection unit can use a camera to identify the clothing of passersby and detect specific colors and designs. The detection unit can also identify the belongings of passersby and detect specific items. For example, the detection unit can use a camera to identify the belongings of passersby and detect specific items. The detection unit can also detect abnormal situations based on the clothing and belongings of passersby. For example, the detection unit can use a camera to identify the clothing and belongings of passersby and detect abnormal situations. In this way, by identifying the clothing and belongings of passersby, detection based on specific conditions is possible. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input image data of passersby acquired by a camera into a generation AI and have the generation AI identify the clothing and belongings.

[0039] The detection unit can identify the age and gender of passersby and perform detection based on specific conditions. The detection unit, for example, can identify the age of passersby and perform detection for a specific age group. For example, the detection unit can use a camera to identify the age of passersby and perform detection for a specific age group. The detection unit can also identify the gender of passersby and perform detection for a specific gender. For example, the detection unit can use a camera to identify the gender of passersby and perform detection for a specific gender. The detection unit can also detect abnormal situations based on the age and gender of passersby. For example, the detection unit can use a camera to identify the age and gender of passersby and detect abnormal situations. This enables detection based on specific conditions based on the age and gender of passersby. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input image data of passersby acquired by a camera to a generation AI and have the generation AI perform age and gender identification.

[0040] The detection unit can measure the walking speed of a passerby and detect abnormal speeds. The detection unit, for example, measures the walking speed of a passerby and detects abnormally fast speeds. For example, the detection unit can measure the walking speed of a passerby using a camera and detect abnormally fast speeds. The detection unit can also measure the walking speed of a passerby and detect abnormally slow speeds. For example, the detection unit can measure the walking speed of a passerby using a camera and detect abnormally slow speeds. The detection unit can also measure the walking speed of a passerby and issue a warning if the walking speed exceeds a normal speed range. For example, the detection unit can measure the walking speed of a passerby using a camera and issue a warning if the walking speed exceeds a normal speed range. In this way, abnormal speeds can be detected by measuring the walking speed of a passerby. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input walking data of a passerby acquired by a camera to the generation AI and cause the generation AI to analyze the walking speed.

[0041] The detection unit can recognize the faces of passersby and identify specific people. The detection unit, for example, recognizes the faces of passersby and identifies specific people. For example, the detection unit uses a camera to recognize the faces of passersby and identifies specific people. The detection unit can also recognize the faces of passersby and identify previously registered people. For example, the detection unit uses a camera to recognize the faces of passersby and identify previously registered people. The detection unit can also recognize the faces of passersby and issue a warning based on specific conditions. For example, the detection unit uses a camera to recognize the faces of passersby and issue a warning based on specific conditions. In this way, specific people can be identified by recognizing the faces of passersby. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input facial data of passersby acquired by a camera to the generation AI and cause the generation AI to perform facial recognition.

[0042] The acquisition unit can acquire appropriate information by taking into account the current location information of passersby. The acquisition unit, for example, acquires the nearest weather forecast based on the current location of the passersby. For example, the acquisition unit acquires the current location of the passersby using GPS and acquires the nearest weather forecast. The acquisition unit can also acquire nearest traffic information based on the current location of the passersby. For example, the acquisition unit acquires the current location of the passersby using Wi-Fi location information and acquires the nearest traffic information. The acquisition unit can also acquire surrounding event information based on the current location of the passersby. For example, the acquisition unit acquires the current location of the passersby using GPS and acquires surrounding event information. This makes it possible to provide optimal information by taking into account the current location information of the passersby. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit may input GPS or Wi-Fi location information to the generation AI and cause the generation AI to analyze the location information.

[0043] The acquisition unit can analyze the past behavior history of passersby and prioritize acquisition of highly relevant information. The acquisition unit, for example, acquires information on frequently used means of transportation based on the past behavior history of the passersby. For example, the acquisition unit analyzes the past behavior history of passersby using log data and acquires information on frequently used means of transportation. The acquisition unit can also acquire weather forecasts for frequently visited places based on the past behavior history of the passersby. For example, the acquisition unit analyzes the past behavior history of passersby using sensor information and acquires weather forecasts for frequently visited places. The acquisition unit can also acquire related news based on the past behavior history of the passersby. For example, the acquisition unit analyzes the past behavior history of passersby using log data and acquires related news. In this way, highly relevant information can be provided by analyzing the past behavior history of passersby. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input log data and sensor information to a generation AI and cause the generation AI to analyze the behavior history.

[0044] The acquisition unit can provide appropriate information by referring to the schedule information of passersby. The acquisition unit, for example, refers to the schedule of the passersby and acquires traffic information related to the schedule. For example, the acquisition unit uses a calendar app to acquire the schedule information of the passersby and acquires traffic information related to the schedule. The acquisition unit can also refer to the schedule of the passersby and acquire a weather forecast related to the schedule. For example, the acquisition unit uses a timetable to acquire the schedule information of the passersby and acquires the weather forecast related to the schedule. The acquisition unit can also refer to the schedule of the passersby and acquire news related to the schedule. For example, the acquisition unit uses a calendar app to acquire the schedule information of the passersby and acquires news related to the schedule. In this way, more appropriate information can be provided by referring to the schedule information of the passersby. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data from a calendar app or a timetable to the generation AI and cause the generation AI to analyze the schedule information.

[0045] The acquisition unit can acquire appropriate information by taking into account device information of passersby. For example, the acquisition unit acquires the nearest weather forecast based on location information of the passerby's smartphone. For example, the acquisition unit acquires the passerby's current location using GPS information from the smartphone and acquires the nearest weather forecast. The acquisition unit can also acquire health-related information based on data from the passerby's smartwatch. For example, the acquisition unit acquires health-related information using heart rate data from the smartwatch. The acquisition unit can also acquire related news based on the passerby's tablet usage history. For example, the acquisition unit acquires related news using tablet usage history data. This makes it possible to provide optimal information by taking into account the passerby's device information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data from the smartphone or smartwatch to the generation AI and cause the generation AI to analyze the device information.

[0046] The acquisition unit can analyze the social media activities of passersby and acquire related information. The acquisition unit can acquire information on related places based on the passersby's social media check-in history, for example. For example, the acquisition unit can use social media check-in data to acquire information on places the passersby have previously visited. The acquisition unit can also analyze the passersby's social media posts to acquire related news. For example, the acquisition unit can use social media post data to acquire news related to the passersby's interests. The acquisition unit can also acquire related event information by referring to the activities of the passersby's friends on social media. For example, the acquisition unit can use social media friend data to acquire event information in which the passersby's friends will be attending. This makes it possible to provide related information by analyzing the passersby's social media activities. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input social media data into a generation AI and have the generation AI analyze the data.

[0047] The acquisition unit can customize the acquisition method by reflecting the past feedback of passersby. The acquisition unit, for example, prioritizes acquisition of preferred information based on the past feedback of passersby. For example, the acquisition unit analyzes the past feedback of passersby using survey results and prioritizes acquisition of preferred information. The acquisition unit can also exclude unnecessary information based on the past feedback of passersby. For example, the acquisition unit analyzes the past feedback of passersby using user comments and excludes unnecessary information. The acquisition unit can also adjust the frequency of information acquisition based on the past feedback of passersby. For example, the acquisition unit analyzes the past feedback of passersby using survey results and adjusts the frequency of information acquisition. This makes it possible to provide more appropriate information by reflecting the past feedback of passersby. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input survey results and user comments into a generation AI and cause the generation AI to analyze the feedback.

[0048] The display unit can track the gaze of passersby and select an appropriate display position. The display unit, for example, tracks the gaze of passersby and displays information at their line of sight. For example, the display unit can use an eye-tracking device to track the gaze of passersby and display information at their line of sight. The display unit can also track the gaze of passersby and move information in accordance with their line of sight. For example, the display unit can use a camera to track the gaze of passersby and move information in accordance with their line of sight. The display unit can also track the gaze of passersby and display important information at a position where their line of sight is concentrated. For example, the display unit can use an eye-tracking device to track the gaze of passersby and display important information at a position where their line of sight is concentrated. In this way, the optimal display position can be selected by tracking the gaze of passersby. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input gaze data acquired by an eye-tracking device or a camera into a generation AI and cause the generation AI to analyze the gaze.

[0049] The display unit can adjust the display height and angle according to the height and body type of a passerby. The display unit, for example, measures the height of a passerby and sets an optimal display height. For example, the display unit uses a camera to measure the height of a passerby and sets an optimal display height. The display unit can also analyze the body type of a passerby and set an optimal display angle. For example, the display unit uses a camera to analyze the body type of a passerby and set an optimal display angle. The display unit can also automatically adjust the display position based on the height and body type of a passerby. For example, the display unit uses a camera to measure the height and body type of a passerby and automatically adjust the display position. This improves the visibility of information by adjusting the display height and angle according to the height and body type of a passerby. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the height and body type of a passerby obtained by a camera into a generation AI and have the generation AI adjust the display position.

[0050] The display unit can provide optimal display content by referring to the past display history of passersby. The display unit, for example, prioritizes displaying frequently viewed information based on the past display history of passersby. For example, the display unit uses log data to analyze the past display history of passersby and prioritizes displaying frequently viewed information. The display unit can also exclude unnecessary information based on the past display history of passersby. For example, the display unit uses a user's operation history to analyze the past display history of passersby and exclude unnecessary information. The display unit can also customize display content based on the past display history of passersby. For example, the display unit uses log data to analyze the past display history of passersby and customize the display content. This makes it possible to provide more appropriate information by referring to the past display history of passersby. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input log data and a user's operation history to a generation AI and cause the generation AI to analyze the display history.

[0051] The display unit can select an appropriate display method by taking into account device information of passersby. For example, if the passerby is using a smartphone, the display unit provides a display method that matches the screen size. For example, the display unit provides a display method optimized for the smartphone screen size. Furthermore, if the passerby is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, the display unit provides a display method optimized for the tablet screen size. Furthermore, if the passerby is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, the display unit provides a display method optimized for the smartwatch screen size. In this way, the optimal display method can be provided by taking into account device information of passersby. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input device information of the smartphone or tablet into the generation AI and have the generation AI select a display method.

[0052] The display unit can make the display content multilingual according to the language setting of the passerby. The display unit, for example, automatically sets the display content based on the language setting of the passerby's device. For example, the display unit references the language setting of the smartphone and automatically sets the display content. The display unit can also provide a language switching function if the passerby speaks multiple languages. For example, the display unit provides a language switching button in the user interface so that the passerby can easily switch languages. The display unit can also provide the display content in a specific language if the passerby selects that language. For example, the display unit provides the display content based on the language selected by the user. This allows the display content to be multilingual according to the passerby's language setting, thereby accommodating a wider range of users. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the device's language setting data into a generation AI and have the generation AI perform multilingual support for the display content.

[0053] The display unit can customize the display content according to the passerby's current task. For example, if the passerby is about to go out, the display unit prioritizes displaying a weather forecast and traffic information. For example, the display unit references schedule information and detects that the passerby is about to go out, and prioritizes displaying the weather forecast and traffic information. The display unit can also display related news and email notifications if the passerby is at work. For example, the display unit references app usage status and detects that the passerby is at work, and displays related news and email notifications. The display unit can also display relaxing content if the passerby is on a break. For example, the display unit references schedule information and detects that the passerby is on a break, and displays relaxing content. This allows the display content to be customized according to the passerby's current task, thereby providing more appropriate information. Some or all of the above-described processing on the display unit may be performed using, or without, AI. For example, the display unit can input schedule information and app usage data into a generation AI and have the generation AI customize the display content.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The information provision system may further include an advertisement provision unit. The advertisement provision unit may provide personalized advertisements based on the user's interests. For example, the advertisement provision unit may display relevant advertisements based on products the user has searched for in the past. The advertisement provision unit may also use the user's location information to provide advertisements for nearby stores and services. For example, if the user is in a specific area, the advertisement provision unit may display sales information for stores in that area. Furthermore, the advertisement provision unit may select the optimal advertisement format taking into account the user's device information. For example, if the user is using a smartphone, the advertisement provision unit may display advertisements optimized for mobile devices. In this way, the advertisement provision unit may provide advertisements that are beneficial to the user.

[0056] The information providing system may further include an emergency notification unit. The emergency notification unit can quickly provide emergency information to the user. For example, in the event of a natural disaster such as an earthquake or typhoon, the emergency notification unit immediately conveys that information to the user. The emergency notification unit can also provide information on evacuation routes and evacuation locations based on the user's current location. For example, if the user is in a specific area, information on evacuation locations in that area is displayed. Furthermore, the emergency notification unit can send push notifications to the user's device to ensure that important information is delivered. For example, an emergency notification can be sent to a smartphone so that the user can immediately check the information. As a result, using the emergency notification unit allows the user to respond quickly and appropriately.

[0057] The information provision system may further include a learning support unit. The learning support unit may support the user's learning activities. For example, if the user is studying a particular subject, the learning support unit may provide related learning materials and reference materials. The learning support unit may also monitor the user's learning progress and provide appropriate feedback. For example, after the user solves a problem, the learning support unit may display the percentage of correct answers and explanations. Furthermore, the learning support unit may provide a customized learning plan according to the user's learning style. For example, if the user prefers visual learning, the learning support unit may provide learning materials that include a lot of visual content. In this way, the learning support unit may enable the user to study effectively.

[0058] The information providing system may further include a travel support unit. The travel support unit can support the user's travel planning. For example, when the user selects a travel destination, the travel support unit provides information on recommended tourist spots and accommodations. The travel support unit can also learn the user's past travel history and propose personalized travel plans. For example, the travel support unit can suggest new travel destinations based on places the user has visited in the past. Furthermore, the travel support unit can provide information on nearby tourist spots and restaurants based on the user's current location. For example, when the user is in a specific area, information on tourist spots in that area is displayed. As a result, using the travel support unit allows the user to enjoy a more fulfilling trip.

[0059] The information provision system may further include an energy management unit. The energy management unit can monitor the user's energy consumption and support efficient energy use. For example, it can monitor the user's home power consumption in real time and provide advice on saving energy. The energy management unit can also analyze the user's past energy consumption data and propose an optimal energy usage plan. For example, it can identify time periods during which the user previously consumed a lot of power and propose ways to save energy during those time periods. Furthermore, the energy management unit can use the user's device information to optimize energy consumption. For example, it can link smart home devices and automatically adjust energy consumption. As a result, using the energy management unit allows the user to use energy efficiently.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The detection unit detects people passing through the door. An infrared sensor or camera can be used to detect people passing through. For example, an infrared sensor uses infrared light to detect human movement and determine whether there is a person within the detection range. A camera analyzes video to detect human movement and determine whether there is a person within a specific area. Step 2: The acquisition unit acquires the latest information related to the person detected by the detection unit. The acquisition unit uses the generation AI to collect and analyze the latest data, such as weather forecasts and traffic information, from the Internet. For example, the generation AI acquires weather forecast data and analyzes the current weather and forecast. The generation AI can also acquire traffic information data and analyze current traffic conditions and congestion information. Furthermore, the generation AI can acquire and analyze data such as news and event information. Step 3: The display unit displays the information acquired by the acquisition unit. The display unit is installed inside or outside the door, in a position that is easy for people passing by to see. For example, the display unit can be installed on the top or side of the door, in a position where people passing by will naturally look. The display unit can also adjust the brightness and contrast of the display to make the information easier to see. For example, the display brightness can be automatically adjusted according to the ambient brightness to make the information easier to see.

[0062] (Example 2) An information provision system according to an embodiment of the present invention cooperates with a generation AI to provide the latest information to people passing by. The information provision system detects people passing by and acquires and displays the latest information related to the person. For example, the information provision system may instantly display weather forecasts, traffic information, and the like to support users going out. For example, the information provision system uses a sensor to detect people passing through a door. The sensor detects the person's movement and determines when the person will pass through the door. For example, infrared sensors or cameras can be used to detect the person's movement. Next, the information provision system uses a generation AI to acquire the latest information related to the detected person. The generation AI collects and analyzes the latest data, such as weather forecasts and traffic information, from the Internet. For example, the information provision system can acquire current weather and traffic congestion information in real time. The acquired information is displayed on a display of the information provision system. The display is installed inside or outside the door in a location that is easily visible to people passing by. For example, the display could be installed on the top or side of the door. This allows users to instantly check the latest information when passing through the door. For example, users can check the weather forecast before going out to decide whether to bring an umbrella, or check traffic information to select the optimal route. The information provision system can also learn the user's past behavioral history and provide individually customized information. For example, it can provide more appropriate information based on the means of transportation and destinations frequently used by a particular user. This allows the information provision system to provide the user with the latest information and support their outings. For example, by providing the user with the latest information quickly and accurately when they walk through a door, it improves user convenience. Furthermore, by learning the user's past behavioral history, it is possible to provide individually customized information and provide more appropriate support.

[0063] An information provision system according to an embodiment includes a detection unit, an acquisition unit, and a display unit. The detection unit detects a passing person. For example, an infrared sensor or a camera can be used to detect the passing person. The detection unit detects human movement using, for example, an infrared sensor. The detection unit can also detect human movement using a camera. For example, an infrared sensor detects human movement using infrared light and determines whether a person is present within a detection range. A camera analyzes video to detect human movement and determine whether a person is present within a specific area. The acquisition unit uses a generation AI to acquire the latest information related to the person detected by the detection unit. The acquisition unit collects and analyzes the latest data, such as weather forecasts and traffic information, from the Internet. For example, the generation AI acquires weather forecast data and analyzes current weather and forecasts. The generation AI can also acquire traffic information data and analyze current traffic conditions and congestion information. Furthermore, the generation AI can acquire and analyze data such as news and event information. For example, the generation AI collects and analyzes the latest information from news sites and event calendars. The display unit displays the information acquired by the acquisition unit. The display unit is installed, for example, inside or outside the door, in a position that is easily visible to passersby. The display unit is installed, for example, above the door, in a position where passersby naturally look. The display unit can also be installed on the side of the door, in a position that is easily visible to passersby as they pass. Furthermore, the display unit can adjust the brightness and contrast of the display to display information that is easy to see. For example, the display unit automatically adjusts the brightness of the display according to the ambient brightness to make the information easy to see. This allows the information provision system according to the embodiment to provide passersby with the latest information and support them in going out. For example, when a user passes through a door, the user can instantly check the latest information, such as a weather forecast or traffic information. Furthermore, the information provision system can learn the user's past behavior history and provide individually customized information, thereby providing more appropriate support.

[0064] The detection unit can detect human movement using an infrared sensor or a camera. Examples of infrared sensors include sensors that use infrared rays to detect human movement. An infrared sensor detects human movement by emitting infrared rays and detecting their reflection. For example, when a person enters its detection range, the infrared sensor changes the reflection of the infrared rays, and the infrared sensor detects the human movement by detecting this change. Examples of cameras include cameras that analyze video to detect human movement. The camera analyzes the video in real time to determine whether a person is present in a specific area. For example, the camera detects movement in the video and analyzes whether the movement is human movement. Thus, human movement can be accurately detected using an infrared sensor or a camera. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input video data acquired by a camera to a generation AI and cause the generation AI to execute a process of detecting human movement from the video data.

[0065] The acquisition unit can collect and analyze the latest weather forecast or traffic information data from the Internet. Weather forecast data includes, for example, data from the Japan Meteorological Agency and data from private weather services. The acquisition unit can, for example, acquire data from the Japan Meteorological Agency and analyze the current weather and forecast. The acquisition unit can also acquire data from private weather services and analyze detailed weather information. Traffic information data includes, for example, data from a traffic control center and data from a navigation service. The acquisition unit can, for example, acquire data from a traffic control center and analyze current traffic conditions and congestion information. The acquisition unit can also acquire data from a navigation service and analyze optimal route information. In this way, by collecting and analyzing the latest data from the Internet, it is possible to provide the latest information. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input weather forecast or traffic information data acquired from the Internet to the generation AI and have the generation AI analyze the data.

[0066] The display unit can be installed inside or outside the door and positioned at a location that is easy for passersby to see. An easy-to-see location includes, for example, eye level or an angle that provides good visibility. The display unit can be installed, for example, at the top of the door and positioned at a location where passersby naturally look. The display unit can also be installed on the side of the door and positioned at a location that is easy for passersby to see as they pass. Furthermore, the display unit can adjust the brightness and contrast of the display to display information that is easy to see. For example, the display unit can automatically adjust the brightness of the display according to the ambient brightness to make the information easy to see. As a result, by placing the display unit at a location that is easy to see, passersby can easily check the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can have the generation AI adjust the brightness and contrast of the display.

[0067] The acquisition unit can learn the user's past behavioral history and provide individually tailored information. The individually tailored information includes, for example, the user's interests and past behavioral history. For example, the acquisition unit can learn the user's past behavioral history and provide information based on the user's frequently used means of transportation and destinations. The acquisition unit can also provide related news and event information based on the user's interests. For example, the acquisition unit can learn data on places the user has visited in the past and means of transportation used by the user and provide related information. This allows the system to provide more appropriate information by learning the user's past behavioral history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past behavioral history data into a generation AI and have the generation AI learn the data and provide information.

[0068] The acquisition unit can acquire and analyze weather forecast or traffic information data using an API. Examples of APIs include a REST API and a SOAP API. The acquisition unit can acquire and analyze weather forecast data using a REST API, for example. The acquisition unit can also acquire and analyze traffic information data using a SOAP API. For example, the acquisition unit can acquire weather forecast data using an API from the Japan Meteorological Agency and analyze the current weather and forecast. The acquisition unit can also acquire traffic information data using an API from a traffic control center and analyze current traffic conditions and congestion information. In this way, by using an API, data can be acquired and analyzed efficiently. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data acquired through the API into a generation AI and have the generation AI analyze the data.

[0069] The acquisition unit can learn the user's past behavioral history using a cloud-based database. Examples of cloud-based databases include AWS, Google Cloud, and Azure. The acquisition unit can learn the user's past behavioral history using, for example, an AWS database. The acquisition unit can also learn the behavioral history using a Google Cloud database. For example, the acquisition unit can learn the user's behavioral history data stored in an AWS database and provide related information. The acquisition unit can also learn data stored in a Google Cloud database and provide information based on the user's interests. This allows the behavioral history to be learned efficiently by using a cloud-based database. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the behavioral history data acquired from the cloud-based database into a generation AI and cause the generation AI to learn the data.

[0070] The detection unit can estimate the emotion of a passerby and adjust the detection accuracy based on the estimated emotion. For example, the detection unit captures the facial expression of a passerby with a camera and estimates the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on changes in facial expression and adjusts the detection accuracy. The detection unit can also record the passerby's voice and estimate the emotion using voice analysis technology. For example, the detection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the detection accuracy. The detection unit can also collect the passerby's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on heart rate fluctuations and adjusts the detection accuracy. This enables more accurate detection by adjusting the detection accuracy according to the passerby's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input image data of passersby captured by a camera to the generation AI, and have the generation AI estimate the emotions.

[0071] The detection unit can analyze the height and body type of a passerby and select an appropriate detection method. The detection unit, for example, measures the height of the passerby and adjusts the height of the sensor appropriately. For example, the detection unit can measure the height of the passerby using a camera and adjust the height of the sensor. The detection unit can also analyze the body type of the passerby and optimize the detection range. For example, the detection unit can analyze the body type of the passerby using a camera and adjust the detection range. The detection unit can also apply different detection algorithms based on the body type of the passerby. For example, the detection unit selects an optimal detection algorithm depending on the body type of the passerby. This improves detection accuracy by selecting an optimal detection method depending on the height and body type of the passerby. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input image data of the passerby acquired by a camera to a generation AI and have the generation AI analyze the height and body type.

[0072] The detection unit can analyze the movement patterns of passersby and detect abnormal movement. The detection unit, for example, analyzes the walking patterns of passersby and detects abnormal movement. For example, the detection unit can analyze the walking patterns of passersby using a camera and detect abnormal movement. The detection unit can also analyze the hand movements of passersby and detect unnatural movement. For example, the detection unit can analyze the hand movements of passersby using a camera and detect unnatural movement. The detection unit can also analyze the posture of passersby and detect abnormal posture. For example, the detection unit can analyze the posture of passersby using a camera and detect abnormal posture. In this way, abnormal movement can be detected by analyzing the movement patterns of passersby. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input movement data of passersby acquired by a camera to the generation AI and cause the generation AI to analyze the movement patterns.

[0073] The detection unit can identify the clothing and belongings of passersby and perform detection based on specific conditions. For example, the detection unit can identify the clothing of passersby and detect specific colors and designs. For example, the detection unit can use a camera to identify the clothing of passersby and detect specific colors and designs. The detection unit can also identify the belongings of passersby and detect specific items. For example, the detection unit can use a camera to identify the belongings of passersby and detect specific items. The detection unit can also detect abnormal situations based on the clothing and belongings of passersby. For example, the detection unit can use a camera to identify the clothing and belongings of passersby and detect abnormal situations. In this way, by identifying the clothing and belongings of passersby, detection based on specific conditions is possible. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input image data of passersby acquired by a camera into a generation AI and have the generation AI identify the clothing and belongings.

[0074] The detection unit can estimate the emotion of a passerby and adjust the detection timing based on the estimated emotion. For example, the detection unit captures the facial expression of a passerby with a camera and estimates the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on changes in facial expression and adjusts the detection timing. The detection unit can also record the passerby's voice and estimate the emotion using voice analysis technology. For example, the detection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the detection timing. The detection unit can also collect the passerby's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the detection unit calculates an emotion score based on heart rate fluctuations and adjusts the detection timing. This allows for more appropriate timing of detection by adjusting the detection timing according to the passerby's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input image data of passersby captured by a camera to the generation AI, and have the generation AI estimate the emotions.

[0075] The detection unit can identify the age and gender of passersby and perform detection based on specific conditions. The detection unit, for example, can identify the age of passersby and perform detection for a specific age group. For example, the detection unit can use a camera to identify the age of passersby and perform detection for a specific age group. The detection unit can also identify the gender of passersby and perform detection for a specific gender. For example, the detection unit can use a camera to identify the gender of passersby and perform detection for a specific gender. The detection unit can also detect abnormal situations based on the age and gender of passersby. For example, the detection unit can use a camera to identify the age and gender of passersby and detect abnormal situations. This enables detection based on specific conditions based on the age and gender of passersby. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input image data of passersby acquired by a camera to a generation AI and have the generation AI perform age and gender identification.

[0076] The detection unit can measure the walking speed of a passerby and detect abnormal speeds. The detection unit, for example, measures the walking speed of a passerby and detects abnormally fast speeds. For example, the detection unit can measure the walking speed of a passerby using a camera and detect abnormally fast speeds. The detection unit can also measure the walking speed of a passerby and detect abnormally slow speeds. For example, the detection unit can measure the walking speed of a passerby using a camera and detect abnormally slow speeds. The detection unit can also measure the walking speed of a passerby and issue a warning if the walking speed exceeds a normal speed range. For example, the detection unit can measure the walking speed of a passerby using a camera and issue a warning if the walking speed exceeds a normal speed range. In this way, abnormal speeds can be detected by measuring the walking speed of a passerby. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input walking data of a passerby acquired by a camera to the generation AI and cause the generation AI to analyze the walking speed.

[0077] The detection unit can recognize the faces of passersby and identify specific people. The detection unit, for example, recognizes the faces of passersby and identifies specific people. For example, the detection unit uses a camera to recognize the faces of passersby and identifies specific people. The detection unit can also recognize the faces of passersby and identify previously registered people. For example, the detection unit uses a camera to recognize the faces of passersby and identify previously registered people. The detection unit can also recognize the faces of passersby and issue a warning based on specific conditions. For example, the detection unit uses a camera to recognize the faces of passersby and issue a warning based on specific conditions. In this way, specific people can be identified by recognizing the faces of passersby. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or may be performed without using AI. For example, the detection unit can input facial data of passersby acquired by a camera to the generation AI and cause the generation AI to perform facial recognition.

[0078] The acquisition unit can estimate the emotions of passersby and adjust the type of information to be acquired based on the estimated emotions. For example, the acquisition unit captures the facial expressions of passersby with a camera and estimates their emotions using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expressions and adjusts the type of information to be acquired. The acquisition unit can also record the voice of passersby and estimate their emotions using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the type of information to be acquired. The acquisition unit can also collect biometric data of passersby (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on fluctuations in heart rate and adjusts the type of information to be acquired. This allows the type of information to be adjusted according to the emotions of passersby, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit may input image data of passersby captured by a camera into the generation AI and cause the generation AI to estimate emotions.

[0079] The acquisition unit can acquire appropriate information by taking into account the current location information of passersby. The acquisition unit, for example, acquires the nearest weather forecast based on the current location of the passersby. For example, the acquisition unit acquires the current location of the passersby using GPS and acquires the nearest weather forecast. The acquisition unit can also acquire nearest traffic information based on the current location of the passersby. For example, the acquisition unit acquires the current location of the passersby using Wi-Fi location information and acquires the nearest traffic information. The acquisition unit can also acquire surrounding event information based on the current location of the passersby. For example, the acquisition unit acquires the current location of the passersby using GPS and acquires surrounding event information. This makes it possible to provide optimal information by taking into account the current location information of the passersby. Some or all of the above-described processing by the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit may input GPS or Wi-Fi location information to the generation AI and cause the generation AI to analyze the location information.

[0080] The acquisition unit can analyze the past behavior history of passersby and prioritize acquisition of highly relevant information. The acquisition unit, for example, acquires information on frequently used means of transportation based on the past behavior history of the passersby. For example, the acquisition unit analyzes the past behavior history of passersby using log data and acquires information on frequently used means of transportation. The acquisition unit can also acquire weather forecasts for frequently visited places based on the past behavior history of the passersby. For example, the acquisition unit analyzes the past behavior history of passersby using sensor information and acquires weather forecasts for frequently visited places. The acquisition unit can also acquire related news based on the past behavior history of the passersby. For example, the acquisition unit analyzes the past behavior history of passersby using log data and acquires related news. In this way, highly relevant information can be provided by analyzing the past behavior history of passersby. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input log data and sensor information to a generation AI and cause the generation AI to analyze the behavior history.

[0081] The acquisition unit can provide appropriate information by referring to the schedule information of passersby. The acquisition unit, for example, refers to the schedule of the passersby and acquires traffic information related to the schedule. For example, the acquisition unit uses a calendar app to acquire the schedule information of the passersby and acquires traffic information related to the schedule. The acquisition unit can also refer to the schedule of the passersby and acquire a weather forecast related to the schedule. For example, the acquisition unit uses a timetable to acquire the schedule information of the passersby and acquires the weather forecast related to the schedule. The acquisition unit can also refer to the schedule of the passersby and acquire news related to the schedule. For example, the acquisition unit uses a calendar app to acquire the schedule information of the passersby and acquires news related to the schedule. In this way, more appropriate information can be provided by referring to the schedule information of the passersby. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data from a calendar app or a timetable to the generation AI and cause the generation AI to analyze the schedule information.

[0082] The acquisition unit can estimate the emotions of passersby and determine the priority of information to be acquired based on the estimated emotions. For example, the acquisition unit captures the facial expressions of passersby with a camera and estimates their emotions using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expressions and determines the priority of information to be acquired. The acquisition unit can also record the passersby's voice and estimate their emotions using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of information to be acquired. The acquisition unit can also collect the passersby's biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on fluctuations in heart rate and determines the priority of information to be acquired. This allows for more appropriate information to be provided by determining the priority of information to be acquired according to the passersby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit may input image data of passersby captured by a camera into the generation AI and cause the generation AI to estimate emotions.

[0083] The acquisition unit can acquire appropriate information by taking into account device information of passersby. For example, the acquisition unit acquires the nearest weather forecast based on location information of the passerby's smartphone. For example, the acquisition unit acquires the passerby's current location using GPS information from the smartphone and acquires the nearest weather forecast. The acquisition unit can also acquire health-related information based on data from the passerby's smartwatch. For example, the acquisition unit acquires health-related information using heart rate data from the smartwatch. The acquisition unit can also acquire related news based on the passerby's tablet usage history. For example, the acquisition unit acquires related news using tablet usage history data. This makes it possible to provide optimal information by taking into account the passerby's device information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data from the smartphone or smartwatch to the generation AI and cause the generation AI to analyze the device information.

[0084] The acquisition unit can analyze the social media activities of passersby and acquire related information. The acquisition unit can acquire information on related places based on the passersby's social media check-in history, for example. For example, the acquisition unit can use social media check-in data to acquire information on places the passersby have previously visited. The acquisition unit can also analyze the passersby's social media posts to acquire related news. For example, the acquisition unit can use social media post data to acquire news related to the passersby's interests. The acquisition unit can also acquire related event information by referring to the activities of the passersby's friends on social media. For example, the acquisition unit can use social media friend data to acquire event information in which the passersby's friends will be attending. This makes it possible to provide related information by analyzing the passersby's social media activities. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input social media data into a generation AI and have the generation AI analyze the data.

[0085] The acquisition unit can customize the acquisition method by reflecting the past feedback of passersby. The acquisition unit, for example, prioritizes acquisition of preferred information based on the past feedback of passersby. For example, the acquisition unit analyzes the past feedback of passersby using survey results and prioritizes acquisition of preferred information. The acquisition unit can also exclude unnecessary information based on the past feedback of passersby. For example, the acquisition unit analyzes the past feedback of passersby using user comments and excludes unnecessary information. The acquisition unit can also adjust the frequency of information acquisition based on the past feedback of passersby. For example, the acquisition unit analyzes the past feedback of passersby using survey results and adjusts the frequency of information acquisition. This makes it possible to provide more appropriate information by reflecting the past feedback of passersby. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input survey results and user comments into a generation AI and cause the generation AI to analyze the feedback.

[0086] The display unit can estimate the emotions of passersby and adjust the display content based on the estimated emotions. For example, the display unit captures the facial expressions of passersby with a camera and estimates their emotions using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on changes in facial expressions and adjusts the display content. The display unit can also record the voice of passersby and estimate their emotions using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display content. The display unit can also collect biometric data of passersby (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on heart rate fluctuations and adjusts the display content. This allows the display content to be adjusted according to the emotions of passersby, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input image data of passersby captured by a camera into the generation AI, and have the generation AI estimate their emotions.

[0087] The display unit can track the gaze of passersby and select an appropriate display position. The display unit, for example, tracks the gaze of passersby and displays information at their line of sight. For example, the display unit can use an eye-tracking device to track the gaze of passersby and display information at their line of sight. The display unit can also track the gaze of passersby and move information in accordance with their line of sight. For example, the display unit can use a camera to track the gaze of passersby and move information in accordance with their line of sight. The display unit can also track the gaze of passersby and display important information at a position where their line of sight is concentrated. For example, the display unit can use an eye-tracking device to track the gaze of passersby and display important information at a position where their line of sight is concentrated. In this way, the optimal display position can be selected by tracking the gaze of passersby. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input gaze data acquired by an eye-tracking device or a camera into a generation AI and cause the generation AI to analyze the gaze.

[0088] The display unit can adjust the display height and angle according to the height and body type of a passerby. The display unit, for example, measures the height of a passerby and sets an optimal display height. For example, the display unit uses a camera to measure the height of a passerby and sets an optimal display height. The display unit can also analyze the body type of a passerby and set an optimal display angle. For example, the display unit uses a camera to analyze the body type of a passerby and set an optimal display angle. The display unit can also automatically adjust the display position based on the height and body type of a passerby. For example, the display unit uses a camera to measure the height and body type of a passerby and automatically adjust the display position. This improves the visibility of information by adjusting the display height and angle according to the height and body type of a passerby. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the height and body type of a passerby obtained by a camera into a generation AI and have the generation AI adjust the display position.

[0089] The display unit can provide optimal display content by referring to the past display history of passersby. The display unit, for example, prioritizes displaying frequently viewed information based on the past display history of passersby. For example, the display unit uses log data to analyze the past display history of passersby and prioritizes displaying frequently viewed information. The display unit can also exclude unnecessary information based on the past display history of passersby. For example, the display unit uses a user's operation history to analyze the past display history of passersby and exclude unnecessary information. The display unit can also customize display content based on the past display history of passersby. For example, the display unit uses log data to analyze the past display history of passersby and customize the display content. This makes it possible to provide more appropriate information by referring to the past display history of passersby. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input log data and a user's operation history to a generation AI and cause the generation AI to analyze the display history.

[0090] The display unit can estimate the emotions of passersby and adjust the timing of display based on the estimated emotions. For example, the display unit captures the facial expressions of passersby with a camera and estimates their emotions using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on changes in facial expressions and adjusts the timing of display. The display unit can also record the voice of passersby and estimate their emotions using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of display. The display unit can also collect biometric data of passersby (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on heart rate fluctuations and adjusts the timing of display. This allows information to be provided at a more appropriate time by adjusting the timing of display according to the emotions of passersby. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input image data of passersby captured by a camera into the generation AI, and have the generation AI estimate their emotions.

[0091] The display unit can select an appropriate display method by taking into account device information of passersby. For example, if the passerby is using a smartphone, the display unit provides a display method that matches the screen size. For example, the display unit provides a display method optimized for the smartphone screen size. Furthermore, if the passerby is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, the display unit provides a display method optimized for the tablet screen size. Furthermore, if the passerby is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, the display unit provides a display method optimized for the smartwatch screen size. In this way, the optimal display method can be provided by taking into account device information of passersby. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input device information of the smartphone or tablet into the generation AI and have the generation AI select a display method.

[0092] The display unit can make the display content multilingual according to the language setting of the passerby. The display unit, for example, automatically sets the display content based on the language setting of the passerby's device. For example, the display unit references the language setting of the smartphone and automatically sets the display content. The display unit can also provide a language switching function if the passerby speaks multiple languages. For example, the display unit provides a language switching button in the user interface so that the passerby can easily switch languages. The display unit can also provide the display content in a specific language if the passerby selects that language. For example, the display unit provides the display content based on the language selected by the user. This allows the display content to be multilingual according to the passerby's language setting, thereby accommodating a wider range of users. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the device's language setting data into a generation AI and have the generation AI perform multilingual support for the display content.

[0093] The display unit can customize the display content according to the passerby's current task. For example, if the passerby is about to go out, the display unit prioritizes displaying a weather forecast and traffic information. For example, the display unit references schedule information and detects that the passerby is about to go out, and prioritizes displaying the weather forecast and traffic information. The display unit can also display related news and email notifications if the passerby is at work. For example, the display unit references app usage status and detects that the passerby is at work, and displays related news and email notifications. The display unit can also display relaxing content if the passerby is on a break. For example, the display unit references schedule information and detects that the passerby is on a break, and displays relaxing content. This allows the display content to be customized according to the passerby's current task, thereby providing more appropriate information. Some or all of the above-described processing on the display unit may be performed using, or without, AI. For example, the display unit can input schedule information and app usage data into a generation AI and have the generation AI customize the display content. === Hard Collateral 1-1 === Each of the multiple elements including the above-described detection unit, acquisition unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit can detect a passing person using the camera 42 or an infrared sensor of the smart device 14. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and collects and analyzes the latest information from the Internet using a generation AI. The display unit can display information on the display 40A of the smart device 14. For example, the detection unit can also be realized by the specific processing unit 290 of the data processing device 12, and the acquisition unit can also be realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned detection unit, acquisition unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit can detect a passing person using the camera 42 or an infrared sensor of the smart glasses 214. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and collects and analyzes the latest information from the Internet using a generation AI. The display unit can display information on the display of the smart glasses 214. For example, the detection unit is also realized by the specific processing unit 290 of the data processing device 12, and the acquisition unit is also realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned detection unit, acquisition unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the detection unit can detect a passing person using the camera 42 or an infrared sensor of the headset type terminal 314. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and collects and analyzes the latest information from the Internet using a generation AI. The display unit can display information on the display 343 of the headset type terminal 314. For example, the detection unit is also realized by the specific processing unit 290 of the data processing device 12, and the acquisition unit is also realized by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned detection unit, acquisition unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit can detect a passing person using the camera 42 or an infrared sensor of the robot 414. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and collects and analyzes the latest information from the Internet using a generative AI. The display unit can display information on the display of the robot 414. For example, the detection unit is also realized by the specific processing unit 290 of the data processing device 12, and the acquisition unit is also realized by the control unit 46A of the robot 414.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The information provision system may further include a voice recognition unit. The voice recognition unit can analyze the voices of passersby in real time and provide information based on voice commands. For example, if a user says, "Tell me the weather forecast," the voice recognition unit analyzes the command and causes the acquisition unit to acquire weather forecast information. The voice recognition unit can also analyze the tone and speed of the user's voice to infer emotions. For example, if the user is in a hurry, the voice recognition unit can infer the user's emotions and cause the acquisition unit to quickly provide information. Furthermore, the voice recognition unit can support multiple languages ​​and can respond appropriately even if the user speaks in different languages. As a result, using the voice recognition unit allows the user to acquire information more intuitively.

[0096] The information provision system may further include a virtual assistant unit. The virtual assistant unit can provide information through dialogue with the user. For example, when the user asks, "What are your plans for today?", the virtual assistant unit obtains schedule information and conveys it to the user. The virtual assistant unit can also learn the user's past dialogue history and provide more personalized information. For example, it can provide information about places the user frequently visits preferentially. Furthermore, the virtual assistant unit can estimate the user's emotions and engage in dialogue in an appropriate tone. For example, if the user is tired, the virtual assistant unit can speak to the user in a gentle tone. This allows the user to obtain information in a more natural manner by using the virtual assistant unit.

[0097] The information providing system may further include a feedback collection unit. The feedback collection unit can collect feedback from users and use it to improve the system. For example, when a user rates the provided information, the feedback collection unit records the rating and reflects it in the acquisition unit. The feedback collection unit can also analyze user comments and identify areas for improvement in the system. For example, when a user comments, "I want more detailed weather information," the feedback collection unit conveys that request to the acquisition unit. Furthermore, the feedback collection unit can estimate the user's emotions and more accurately understand the content of the feedback. For example, if the user is dissatisfied, the feedback collection unit can estimate the user's emotions and reflect them in improving the system. In this way, using the feedback collection unit allows the system to better adapt to the user's needs.

[0098] The information provision system may further include a health management unit. The health management unit can collect the user's health data and provide appropriate information. For example, it can monitor the user's heart rate and number of steps and provide advice according to the user's health condition. The health management unit can also analyze the user's diet and exercise records to support healthy lifestyle habits. For example, when the user inputs a diet record, the health management unit analyzes the data and suggests a balanced diet. Furthermore, the health management unit can estimate the user's emotions and provide stress management advice. For example, if the user is feeling stressed, the health management unit can suggest relaxation methods. As a result, using the health management unit allows the user to live a healthier life.

[0099] The information providing system may further include an entertainment providing unit. The entertainment providing unit may provide entertainment content based on the user's interests. For example, when a user wants to listen to music, the entertainment providing unit may create a playlist that matches the user's preferences. The entertainment providing unit may also learn the user's past viewing history and provide movie and TV drama recommendations. For example, it may provide information about new movies based on the genres the user frequently watches. Furthermore, the entertainment providing unit may estimate the user's emotions and provide content that matches the user's mood at that time. For example, if the user wants to relax, the entertainment providing unit may suggest relaxing music or videos. As a result, by using the entertainment providing unit, the user can spend a more fulfilling time.

[0100] The information provision system may further include an advertisement provision unit. The advertisement provision unit may provide personalized advertisements based on the user's interests. For example, the advertisement provision unit may display relevant advertisements based on products the user has searched for in the past. The advertisement provision unit may also use the user's location information to provide advertisements for nearby stores and services. For example, if the user is in a specific area, the advertisement provision unit may display sales information for stores in that area. Furthermore, the advertisement provision unit may select the optimal advertisement format taking into account the user's device information. For example, if the user is using a smartphone, the advertisement provision unit may display advertisements optimized for mobile devices. In this way, the advertisement provision unit may provide advertisements that are beneficial to the user.

[0101] The information providing system may further include an emergency notification unit. The emergency notification unit can quickly provide emergency information to the user. For example, in the event of a natural disaster such as an earthquake or typhoon, the emergency notification unit immediately conveys that information to the user. The emergency notification unit can also provide information on evacuation routes and evacuation locations based on the user's current location. For example, if the user is in a specific area, information on evacuation locations in that area is displayed. Furthermore, the emergency notification unit can send push notifications to the user's device to ensure that important information is delivered. For example, an emergency notification can be sent to a smartphone so that the user can immediately check the information. As a result, using the emergency notification unit allows the user to respond quickly and appropriately.

[0102] The information provision system may further include a learning support unit. The learning support unit may support the user's learning activities. For example, if the user is studying a particular subject, the learning support unit may provide related learning materials and reference materials. The learning support unit may also monitor the user's learning progress and provide appropriate feedback. For example, after the user solves a problem, the learning support unit may display the percentage of correct answers and explanations. Furthermore, the learning support unit may provide a customized learning plan according to the user's learning style. For example, if the user prefers visual learning, the learning support unit may provide learning materials that include a lot of visual content. In this way, the learning support unit may enable the user to study effectively.

[0103] The information providing system may further include a travel support unit. The travel support unit can support the user's travel planning. For example, when the user selects a travel destination, the travel support unit provides information on recommended tourist spots and accommodations. The travel support unit can also learn the user's past travel history and propose personalized travel plans. For example, the travel support unit can suggest new travel destinations based on places the user has visited in the past. Furthermore, the travel support unit can provide information on nearby tourist spots and restaurants based on the user's current location. For example, when the user is in a specific area, information on tourist spots in that area is displayed. As a result, using the travel support unit allows the user to enjoy a more fulfilling trip.

[0104] The information provision system may further include an energy management unit. The energy management unit can monitor the user's energy consumption and support efficient energy use. For example, it can monitor the user's home power consumption in real time and provide advice on saving energy. The energy management unit can also analyze the user's past energy consumption data and propose an optimal energy usage plan. For example, it can identify time periods during which the user previously consumed a lot of power and propose ways to save energy during those time periods. Furthermore, the energy management unit can use the user's device information to optimize energy consumption. For example, it can link smart home devices and automatically adjust energy consumption. As a result, using the energy management unit allows the user to use energy efficiently.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The detection unit detects people passing through the door. An infrared sensor or camera can be used to detect people passing through. For example, an infrared sensor uses infrared light to detect human movement and determine whether there is a person within the detection range. A camera analyzes video to detect human movement and determine whether there is a person within a specific area. Step 2: The acquisition unit acquires the latest information related to the person detected by the detection unit. The acquisition unit uses the generation AI to collect and analyze the latest data, such as weather forecasts and traffic information, from the Internet. For example, the generation AI acquires weather forecast data and analyzes the current weather and forecast. The generation AI can also acquire traffic information data and analyze current traffic conditions and congestion information. Furthermore, the generation AI can acquire and analyze data such as news and event information. Step 3: The display unit displays the information acquired by the acquisition unit. The display unit is installed inside or outside the door, in a position that is easy for people passing by to see. For example, the display unit can be installed on the top or side of the door, in a position where people passing by will naturally look. The display unit can also adjust the brightness and contrast of the display to make the information easier to see. For example, the display brightness can be automatically adjusted according to the ambient brightness to make the information easier to see.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 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.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0121] 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.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] 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.

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

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

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

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

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

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

[0141] The data processing system 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 detection unit that detects people passing through the door; an acquisition unit that acquires the latest information related to the person detected by the detection unit; a display unit that displays the information acquired by the acquisition unit. A system characterized by:

2. The detection unit Detecting human movement using an infrared sensor or camera 2. The system of claim 1.

3. The acquisition unit Collect and analyze the latest weather or traffic data from the Internet 2. The system of claim 1.

4. The display unit It is installed inside or outside the door and positioned so that it is easily visible to passersby.

2. The system of claim 1.

5. The acquisition unit Learns about a user's past behavior and provides personalized information 2. The system of claim 1.

6. The acquisition unit Use APIs to retrieve and analyze weather or traffic data 2. The system of claim 1.

7. The acquisition unit Uses a cloud-based database to learn users' past behavioral history 2. The system of claim 1.

8. The detection unit Estimate the emotions of passersby and adjust the detection accuracy based on the estimated emotions.

2. The system of claim 1.

Images