System

Abstract

An object of a system according to an embodiment is to guide a visually impaired person to safely walk even in a crowd.SOLUTION: A system includes a transmission part, an analysis part, a position specification part, and a notification part. The transmission unit transmits data on the flow of people from the facility side. The analysis unit analyzes the data transmitted by the transmission unit. The position identification unit identifies a current position of the user based on the data analyzed by the analysis unit. The notification unit notifies a safe route based on the current location identified by the location identification unit and the analyzed data.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 lacks adequate guidance for visually impaired people to walk safely in crowds, and there is room for improvement.

[0005] The system according to the embodiment aims to guide visually impaired people so that they can walk safely even in crowds. [Means for solving the problem]

[0006] The system according to the embodiment includes a transmission unit, an analysis unit, a location identification unit, and a notification unit. The transmission unit transmits people flow data from the facility. The analysis unit analyzes the data transmitted by the transmission unit. The location identification unit identifies the user's current location based on the data analyzed by the analysis unit. The notification unit notifies the user of a safe route based on the current location identified by the location identification unit and the analyzed data. [Effects of the Invention]

[0007] The system according to the embodiment can guide visually impaired people to walk safely even in crowds. [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) A guidance system according to an embodiment of the present invention safely guides visually impaired people through crowded areas such as train platforms, sidewalks, and shopping malls. The guidance system transmits people flow data from the facility, receives it via an app, identifies the user's current location, and guides the user to walk safely via a notification device mounted on the white cane or an external device. For example, the guidance system transmits people flow data from the facility. For example, at facilities such as train platforms and shopping malls, cameras and sensors are used to monitor people flow in real time. This data is analyzed by AI to identify congestion and people flow patterns. The analyzed data is then received by the app. The app identifies the user's current location based on the received data. For example, location information technology is used to accurately determine the user's current location. The app then guides the user to walk safely based on the user's current location and the analyzed people flow data via a notification device mounted on the white cane or an external device. For example, the app notifies the user by vibration or audio, and guides the user to a safe route. This allows visually impaired people to travel safely. This allows the guidance system to help visually impaired people walk safely through crowded areas such as train platforms, sidewalks, and shopping malls. For example, by guiding them to routes that avoid crowded areas, the risk of collisions and falls can be reduced. In addition, by understanding the flow of people in real time, the system can always provide safe routes based on the latest information.

[0029] A guidance system according to an embodiment includes a transmitting unit, an analyzing unit, a position identifying unit, and a notifying unit. The transmitting unit transmits data on the flow of people from a facility. For example, the transmitting unit can monitor the flow of people using a camera or a sensor and transmit the data. The analyzing unit analyzes the data transmitted by the transmitting unit. For example, the analyzing unit can analyze the data using AI to understand the congestion situation and patterns of people flow. The position identifying unit identifies the user's current location based on the data analyzed by the analyzing unit. For example, the position identifying unit can accurately identify the user's current location using location information technology. The notifying unit notifies the user of a safe route based on the current location identified by the position identifying unit and the analyzed data. For example, the notifying unit notifies the user by vibration or sound, guiding the user to a safe route. As a result, the guidance system according to an embodiment can provide a safe route so that visually impaired people can travel safely.

[0030] The transmitting unit can monitor the flow of people using a camera or a sensor. The transmitting unit, for example, monitors the flow of people using a camera. For example, the transmitting unit can install a surveillance camera and monitor the flow of people in real time. The transmitting unit can also monitor the flow of people using a sensor. For example, the transmitting unit can install an infrared sensor and detect the movement of people. Furthermore, the transmitting unit can combine multiple cameras or sensors to monitor the flow of people more accurately. As a result, accurate data on the flow of people can be obtained by using the camera or sensor.

[0031] The analysis unit can perform analysis using AI. The analysis unit can analyze data using, for example, deep learning. For example, the analysis unit can analyze patterns of people flow using a deep learning model. The analysis unit can also analyze data using machine learning. For example, the analysis unit can predict congestion situations using a machine learning algorithm. Furthermore, the analysis unit can combine multiple AI technologies to improve the accuracy of the analysis. In this way, the use of AI improves the accuracy of the analysis.

[0032] The position determination unit can determine the user's current location using position information technology. The position determination unit can determine the user's current location using, for example, GPS. For example, the position determination unit can receive GPS signals and determine the user's current location. The position determination unit can also determine the user's current location using Wi-Fi. For example, the position determination unit can determine the user's current location based on location information of a Wi-Fi access point. Furthermore, the position determination unit can also determine the user's current location using Bluetooth (registered trademark). For example, the position determination unit can receive a Bluetooth beacon signal and determine the user's current location. As a result, the user's current location can be accurately determined using position information technology.

[0033] The notification unit can provide notification by vibration or sound and guide the user to a safe route. The notification unit, for example, provides notification by vibration. For example, the notification unit can use a vibration device mounted on a white cane to guide the user to a safe route. The notification unit can also provide notification by sound. For example, the notification unit can use a voice assistant on a smartphone to guide the user to a safe route. Furthermore, the notification unit can provide notification by a combination of vibration and sound. For example, the notification unit can use vibration and sound simultaneously to guide the user to a safe route. This allows visually impaired people to be guided to a safe route by vibration or sound notification.

[0034] The notification unit can provide notification through a notification device mounted on the white cane and an external device. The notification unit provides notification using, for example, a notification device mounted on the white cane. For example, the notification unit can use a vibration device mounted on the white cane to guide the user along a safe route. The notification unit can also provide notification using an external device. For example, the notification unit can use a smartphone or a wearable device to guide the user along a safe route. Furthermore, the notification unit can provide notification by combining a notification device mounted on the white cane and an external device. For example, the notification unit can simultaneously use the vibration device on the white cane and a voice assistant on a smartphone to guide the user along a safe route. This allows visually impaired people to be guided along a safe route through a notification device mounted on the white cane or an external device.

[0035] The guidance system includes a transmission unit that learns patterns of people flow during different time periods and days of the week and selects the optimal transmission timing. For example, the transmission unit increases the data transmission frequency during weekday morning rush hour to provide real-time congestion information. The transmission unit can also increase the data transmission frequency during busy shopping mall hours on weekends to provide safe routes. Furthermore, the transmission unit can reduce the data transmission frequency during quiet nighttime hours to provide the minimum necessary information. This enables more effective information provision by selecting the optimal transmission timing according to different time periods and days of the week. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The guidance system includes a transmission unit that changes the priority of data transmission in response to a specific event or emergency. For example, when an emergency occurs, the transmission unit maximizes the data transmission frequency to provide detailed information in real time. The transmission unit can also increase the data transmission frequency to provide congestion information in real time when a large-scale event is held. Furthermore, the transmission unit can set the data transmission frequency to a medium level in normal times to provide appropriate information. This allows important information to be provided quickly by changing the priority of data transmission in response to a specific event or emergency. Some or all of the above-described processing in the transmission unit may be performed, for example, using AI or without AI.

[0037] The guidance system includes a transmission unit that adjusts the level of detail of data transmission based on the congestion level of a specific area within the facility. For example, in congested areas, the transmission unit transmits detailed data to provide a safe route. In non-congested areas, the transmission unit can also transmit simplified data to provide the minimum necessary information. Furthermore, in areas with a moderate level of congestion, the transmission unit can also transmit data with an appropriate level of detail to provide appropriate information. In this way, by adjusting the level of detail of data transmission based on the congestion level of a specific area within the facility, necessary information can be appropriately provided. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The guidance system includes a transmitter that integrates and transmits data from different sensors within the facility. For example, the transmitter integrates and transmits data from a camera sensor and a temperature sensor to provide congestion status and environmental information. The transmitter can also integrate and transmit data from a sound sensor and a vibration sensor to provide noise levels and ground conditions. Furthermore, the transmitter can integrate and transmit data from an illuminance sensor and a humidity sensor to provide visibility and humidity information. This allows for more comprehensive information to be provided by integrating data from different sensors. Some or all of the above-described processing in the transmitter may be performed using, for example, AI, or may be performed without AI.

[0039] The guidance system includes a transmitting unit that adjusts the content of transmission based on data from outside the facility. For example, the transmitting unit takes into account weather information and prioritizes transmitting indoor routes when it is raining. The transmitting unit can also take into account traffic information and prioritize transmitting routes that avoid congested roads. Furthermore, the transmitting unit can integrate weather and traffic information to transmit the optimal route. This allows for providing more appropriate information by taking into account data from outside the facility. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The guidance system includes a transmission unit that customizes transmission data by reflecting the user's past travel history. The transmission unit transmits, for example, an optimal route based on routes the user has used in the past. The transmission unit can also transmit a route that avoids congestion based on the user's past travel history. Furthermore, the transmission unit can analyze the user's past travel history and transmit the most efficient route. This makes it possible to provide more personalized information by reflecting the user's past travel history. Some or all of the above-described processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI.

[0041] The analysis unit can improve the accuracy of prediction of the current flow of people by referring to past data. The analysis unit, for example, refers to past congestion data to predict the current congestion situation. The analysis unit can also refer to past event data to predict the impact of a current event. Furthermore, the analysis unit can also refer to past weather data to predict the impact of current weather. In this way, by referring to past data, the accuracy of prediction of the current flow of people is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The analysis unit can improve the analysis accuracy by combining different analysis algorithms. The analysis unit can improve the analysis accuracy by combining, for example, a machine learning algorithm and a statistical analysis algorithm. The analysis unit can also improve the analysis accuracy by combining a deep learning algorithm and a rule-based algorithm. Furthermore, the analysis unit can improve the analysis accuracy by combining a clustering algorithm and a regression analysis algorithm. In this way, the analysis accuracy is improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The analysis unit can detect abnormalities in the flow of people in a specific area or time period. For example, the analysis unit detects an abnormality when more people than usual are gathered in a specific area. The analysis unit can also detect an abnormality when fewer people than usual are present during a specific time period. Furthermore, the analysis unit can detect an abnormality when there is a sudden change in the flow of people in a specific area. This enables rapid response by detecting abnormalities in the flow of people in a specific area or time period. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The analysis unit can integrate and analyze different data sources within the facility. For example, the analysis unit can integrate and analyze data from a temperature sensor and a sound sensor to provide information on the occupancy level and the environment. The analysis unit can also integrate and analyze data from an illuminance sensor and a humidity sensor to provide visibility and humidity information. Furthermore, the analysis unit can integrate and analyze data from a camera sensor and a vibration sensor to provide information on the occupancy level and the ground condition. This enables more comprehensive analysis by integrating different data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The analysis unit can adjust the analysis results by taking into account data outside the facility. The analysis unit can, for example, adjust the analysis results for areas where congestion is expected by taking into account event information. The analysis unit can also adjust the analysis results for areas where an emergency is occurring by taking into account news information. Furthermore, the analysis unit can also adjust the analysis results for areas affected by weather by taking into account weather information. In this way, by taking into account data outside the facility, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0046] The analysis unit can customize the analysis results by reflecting the user's past movement history. The analysis unit provides optimal analysis results based on, for example, routes the user has used in the past. The analysis unit can also provide analysis results that avoid congestion based on the user's past movement history. Furthermore, the analysis unit can analyze the user's past movement history and provide the most efficient analysis results. This makes it possible to provide more personalized analysis results by reflecting the user's past movement history. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The location determination unit can improve the accuracy of location determination by combining different location information technologies. The location determination unit can improve the accuracy of location determination by combining, for example, GPS and Wi-Fi data. The location determination unit can also improve the accuracy of location determination by combining Bluetooth and Wi-Fi data. Furthermore, the location determination unit can improve the accuracy of location determination by combining GPS and Bluetooth data. In this way, the accuracy of location determination is improved by combining different location information technologies. Some or all of the above-described processing in the location determination unit may be performed, for example, using AI or without using AI.

[0048] The location identification unit can adjust the frequency of location identification based on the congestion level of a specific area within the facility. For example, in a congested area, the location identification unit can increase the frequency of location identification to provide detailed location information. In a non-congested area, the location identification unit can also decrease the frequency of location identification to provide the minimum necessary location information. Furthermore, in a medium-congested area, the location identification unit can set the frequency of location identification to a medium level to provide appropriate location information. In this way, by adjusting the frequency of location identification based on the congestion level of a specific area within the facility, it is possible to appropriately provide necessary information. Some or all of the above-described processing in the location identification unit may be performed, for example, using AI or without using AI.

[0049] The location identification unit can improve the accuracy of location identification by referring to the user's past movement history. The location identification unit can improve the accuracy of location identification based on, for example, routes used by the user in the past. The location identification unit can also improve the accuracy of location identification to avoid congestion based on the user's past movement history. Furthermore, the location identification unit can analyze the user's past movement history and provide the most efficient accuracy of location identification. In this way, the accuracy of location identification is improved by referring to the user's past movement history. Some or all of the above-mentioned processing in the location identification unit may be performed, for example, using AI or without using AI.

[0050] The location identification unit can identify a location by integrating data from different sensors within the facility. For example, the location identification unit can integrate data from a camera sensor and a temperature sensor to identify a location and provide congestion status and environmental information. The location identification unit can also integrate data from a sound sensor and a vibration sensor to identify a location and provide noise levels and ground conditions. Furthermore, the location identification unit can integrate data from an illuminance sensor and a humidity sensor to identify a location and provide visibility and humidity information. This enables more comprehensive location identification by integrating data from different sensors. Some or all of the above-mentioned processing in the location identification unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The location identification unit can adjust the accuracy of location identification by taking into account data outside the facility. For example, the location identification unit can take into account weather information and prioritize indoor routes when it is raining. The location identification unit can also take traffic information into account to perform location identification that avoids congested roads. Furthermore, the location identification unit can integrate weather and traffic information to perform optimal location identification. In this way, more appropriate location information can be provided by taking into account data outside the facility. Some or all of the above-mentioned processing in the location identification unit may be performed, for example, using AI or without using AI.

[0052] The location identification unit can customize the location identification method by reflecting the user's past movement history. The location identification unit can provide an optimal location identification method based on, for example, routes the user has used in the past. The location identification unit can also provide a location identification method that avoids congestion based on the user's past movement history. Furthermore, the location identification unit can analyze the user's past movement history and provide the most efficient location identification method. In this way, more personalized location information can be provided by reflecting the user's past movement history. Some or all of the above-mentioned processing in the location identification unit may be performed, for example, using AI or without using AI.

[0053] The notification unit can adjust the timing of the notification based on the user's current situation. For example, if the user changes their walking speed, the notification unit adjusts the timing of the notification in real time. The notification unit can also adjust the timing of the notification in real time if the user changes their direction. Furthermore, the notification unit can pause the notification when the user stops and resume it when they start walking again. This allows the notification timing to be adjusted according to the user's current situation, making it possible to provide more appropriate information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0054] The notification unit can improve the effectiveness of the notification by combining different notification means. The notification unit, for example, provides notification by combining vibration and sound to guide a visually impaired person to a safe route. The notification unit can also provide notification by combining vibration and light to guide a visually impaired person to a safe route. Furthermore, the notification unit can provide notification by combining sound and light to guide a visually impaired person to a safe route. In this way, the effectiveness of the notification can be improved by combining different notification means. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0055] The notification unit can improve the accuracy of notifications by referring to the user's past notification history. The notification unit, for example, provides an optimal notification method based on notifications received by the user in the past. The notification unit can also provide a notification method to avoid congestion based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and provide the most efficient notification method. In this way, the accuracy of notifications is improved by referring to the user's past notification history. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The notification unit can adjust the notification content by integrating different data sources within the facility. For example, the notification unit can integrate data from a temperature sensor and a sound sensor to provide a notification and congestion status and environmental information. The notification unit can also integrate data from an illuminance sensor and a humidity sensor to provide a notification and visibility and humidity information. Furthermore, the notification unit can integrate data from a camera sensor and a vibration sensor to provide a notification and congestion status and ground condition. In this way, by integrating different data sources, more comprehensive information can be provided. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0057] The notification unit can adjust the notification content by taking into account data outside the facility. For example, the notification unit can take into account weather information and prioritize indoor routes when it is raining. The notification unit can also take traffic information into account and provide notifications to avoid congested roads. Furthermore, the notification unit can integrate weather and traffic information to provide optimal notifications. In this way, more appropriate information can be provided by taking into account data outside the facility. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0058] The notification unit can customize the notification method by reflecting the user's past movement history. The notification unit can provide the optimal notification method based on, for example, routes the user has used in the past. The notification unit can also provide a notification method for avoiding congestion based on the user's past movement history. Furthermore, the notification unit can analyze the user's past movement history and provide the most efficient notification method. This makes it possible to provide more personalized information by reflecting the user's past movement history. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI, or can be performed without using AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The guidance system includes a notification unit that monitors the user's walking speed in real time and adjusts the timing of notifications according to the speed. For example, if the user is walking fast, the notification unit increases the frequency of notifications to provide prompt guidance. Also, if the user is walking slowly, the notification unit can reduce the frequency of notifications to provide the minimum necessary information. Furthermore, the notification unit can pause notifications when the user stops and resume them when the user starts walking again. This allows the system to provide more appropriate information by adjusting the timing of notifications according to the user's walking speed.

[0061] The guidance system includes a notification unit that customizes the content of notifications by referring to the user's past movement history. The notification unit provides optimal notification content based on, for example, routes the user has used in the past. The notification unit can also provide notification content to avoid congestion based on the user's past movement history. Furthermore, the notification unit can analyze the user's past movement history and provide the most efficient notification content. This makes it possible to provide more personalized information by referring to the user's past movement history.

[0062] The guidance system includes a notification unit that integrates data from different sensors within the facility and adjusts notification content. For example, the notification unit integrates data from a temperature sensor and a sound sensor to provide congestion status and environmental information. The notification unit can also integrate data from an illuminance sensor and a humidity sensor to provide visibility and humidity information. Furthermore, the notification unit can integrate data from a camera sensor and a vibration sensor to provide congestion status and ground conditions. This allows for more comprehensive information to be provided by integrating data from different sensors.

[0063] The guidance system includes a notification unit that adjusts notification content by taking into account data outside the facility. For example, the notification unit takes into account weather information and prioritizes notifying indoor routes when it is raining. The notification unit can also take traffic information into account and provide notifications to avoid congested roads. Furthermore, the notification unit can integrate weather and traffic information to provide optimal notifications. This allows for more appropriate information to be provided by taking into account data outside the facility.

[0064] The guidance system includes a transmission unit that customizes transmission data by reflecting the user's past travel history. The transmission unit transmits, for example, an optimal route based on routes the user has used in the past. The transmission unit can also transmit a route that avoids congestion based on the user's past travel history. Furthermore, the transmission unit can analyze the user's past travel history and transmit the most efficient route. This makes it possible to provide more personalized information by reflecting the user's past travel history.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The transmitter transmits data on the flow of people from the facility. For example, the transmitter can monitor the flow of people using a camera or a sensor and transmit the data. Step 2: The analysis unit analyzes the data sent by the transmission unit. For example, the analysis unit may use AI to analyze the data and understand congestion conditions and patterns of people flow. Step 3: The location determination unit determines the user's current location based on the data analyzed by the analysis unit. For example, the location determination unit can accurately determine the user's current location using location information technology. Step 4: The notification unit notifies the user of a safe route based on the current location identified by the location identification unit and the analyzed data. For example, the notification unit notifies the user by vibration or sound, and guides the user to a safe route.

[0067] (Example 2) A guidance system according to an embodiment of the present invention safely guides visually impaired people through crowded areas such as train platforms, sidewalks, and shopping malls. The guidance system transmits people flow data from the facility, receives it via an app, identifies the user's current location, and guides the user to walk safely via a notification device mounted on the white cane or an external device. For example, the guidance system transmits people flow data from the facility. For example, at facilities such as train platforms and shopping malls, cameras and sensors are used to monitor people flow in real time. This data is analyzed by AI to identify congestion and people flow patterns. The analyzed data is then received by the app. The app identifies the user's current location based on the received data. For example, location information technology is used to accurately determine the user's current location. The app then guides the user to walk safely based on the user's current location and the analyzed people flow data via a notification device mounted on the white cane or an external device. For example, the app notifies the user by vibration or audio, and guides the user to a safe route. This allows visually impaired people to travel safely. This allows the guidance system to help visually impaired people walk safely through crowded areas such as train platforms, sidewalks, and shopping malls. For example, by guiding them to routes that avoid crowded areas, the risk of collisions and falls can be reduced. In addition, by understanding the flow of people in real time, the system can always provide safe routes based on the latest information.

[0068] A guidance system according to an embodiment includes a transmitting unit, an analyzing unit, a position identifying unit, and a notifying unit. The transmitting unit transmits data on the flow of people from a facility. For example, the transmitting unit can monitor the flow of people using a camera or a sensor and transmit the data. The analyzing unit analyzes the data transmitted by the transmitting unit. For example, the analyzing unit can analyze the data using AI to understand the congestion situation and patterns of people flow. The position identifying unit identifies the user's current location based on the data analyzed by the analyzing unit. For example, the position identifying unit can accurately identify the user's current location using location information technology. The notifying unit notifies the user of a safe route based on the current location identified by the position identifying unit and the analyzed data. For example, the notifying unit notifies the user by vibration or sound, guiding the user to a safe route. As a result, the guidance system according to an embodiment can provide a safe route so that visually impaired people can travel safely.

[0069] The transmitting unit can monitor the flow of people using a camera or a sensor. The transmitting unit, for example, monitors the flow of people using a camera. For example, the transmitting unit can install a surveillance camera and monitor the flow of people in real time. The transmitting unit can also monitor the flow of people using a sensor. For example, the transmitting unit can install an infrared sensor and detect the movement of people. Furthermore, the transmitting unit can combine multiple cameras or sensors to monitor the flow of people more accurately. As a result, accurate data on the flow of people can be obtained by using the camera or sensor.

[0070] The analysis unit can perform analysis using AI. The analysis unit can analyze data using, for example, deep learning. For example, the analysis unit can analyze patterns of people flow using a deep learning model. The analysis unit can also analyze data using machine learning. For example, the analysis unit can predict congestion situations using a machine learning algorithm. Furthermore, the analysis unit can combine multiple AI technologies to improve the accuracy of the analysis. In this way, the use of AI improves the accuracy of the analysis.

[0071] The position determination unit can determine the user's current location using position information technology. The position determination unit can determine the user's current location using, for example, GPS. For example, the position determination unit can receive a GPS signal and determine the user's current location. The position determination unit can also determine the user's current location using Wi-Fi. For example, the position determination unit can determine the user's current location based on location information of a Wi-Fi access point. Furthermore, the position determination unit can also determine the user's current location using Bluetooth. For example, the position determination unit can receive a Bluetooth beacon signal and determine the user's current location. As a result, the user's current location can be accurately determined using position information technology.

[0072] The notification unit can provide notification by vibration or sound and guide the user to a safe route. The notification unit, for example, provides notification by vibration. For example, the notification unit can use a vibration device mounted on a white cane to guide the user to a safe route. The notification unit can also provide notification by sound. For example, the notification unit can use a voice assistant on a smartphone to guide the user to a safe route. Furthermore, the notification unit can provide notification by a combination of vibration and sound. For example, the notification unit can use vibration and sound simultaneously to guide the user to a safe route. This allows visually impaired people to be guided to a safe route by vibration or sound notification.

[0073] The notification unit can provide notification through a notification device mounted on the white cane and an external device. The notification unit provides notification using, for example, a notification device mounted on the white cane. For example, the notification unit can use a vibration device mounted on the white cane to guide the user along a safe route. The notification unit can also provide notification using an external device. For example, the notification unit can use a smartphone or a wearable device to guide the user along a safe route. Furthermore, the notification unit can provide notification by combining a notification device mounted on the white cane and an external device. For example, the notification unit can simultaneously use the vibration device on the white cane and a voice assistant on a smartphone to guide the user along a safe route. This allows visually impaired people to be guided along a safe route through a notification device mounted on the white cane or an external device.

[0074] The guidance system includes a transmission unit that estimates a user's emotions and adjusts the frequency of data transmission of the flow of people based on the estimated user emotions. For example, if the user feels anxious, the transmission unit increases the data transmission frequency to provide detailed information in real time. Furthermore, if the user feels relaxed, the transmission unit can reduce the data transmission frequency to provide the minimum necessary information. Furthermore, if the user is in a hurry, the transmission unit can set the data transmission frequency to a medium level to provide quick and appropriate information. This allows for more appropriate information to be provided by adjusting the data transmission frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The guidance system includes a transmission unit that learns patterns of people flow during different time periods and days of the week and selects the optimal transmission timing. For example, the transmission unit increases the data transmission frequency during weekday morning rush hour to provide real-time congestion information. The transmission unit can also increase the data transmission frequency during busy shopping mall hours on weekends to provide safe routes. Furthermore, the transmission unit can reduce the data transmission frequency during quiet nighttime hours to provide the minimum necessary information. This enables more effective information provision by selecting the optimal transmission timing according to different time periods and days of the week. Some or all of the above-mentioned processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI.

[0076] The guidance system includes a transmission unit that changes the priority of data transmission in response to a specific event or emergency. For example, when an emergency occurs, the transmission unit maximizes the data transmission frequency to provide detailed information in real time. The transmission unit can also increase the data transmission frequency to provide congestion information in real time when a large-scale event is held. Furthermore, the transmission unit can set the data transmission frequency to a medium level in normal times to provide appropriate information. This allows important information to be provided quickly by changing the priority of data transmission in response to a specific event or emergency. Some or all of the above-described processing in the transmission unit may be performed, for example, using AI or without AI.

[0077] The guidance system includes a transmission unit that adjusts the level of detail of data transmission based on the congestion level of a specific area within the facility. For example, in congested areas, the transmission unit transmits detailed data to provide a safe route. In non-congested areas, the transmission unit can also transmit simplified data to provide the minimum necessary information. Furthermore, in areas with a moderate level of congestion, the transmission unit can also transmit data with an appropriate level of detail to provide appropriate information. In this way, by adjusting the level of detail of data transmission based on the congestion level of a specific area within the facility, necessary information can be appropriately provided. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The guidance system includes a transmission unit that estimates a user's emotions and determines the priority of data to be transmitted based on the estimated user emotions. For example, if the user feels anxious, the transmission unit prioritizes transmitting safety-related data. Furthermore, if the user feels relaxed, the transmission unit can also prioritize transmitting general congestion status data. Furthermore, if the user is in a hurry, the transmission unit can also prioritize transmitting data related to the shortest route. This allows for more appropriate information to be provided by determining the priority of data to be transmitted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The guidance system includes a transmitter that integrates and transmits data from different sensors within the facility. For example, the transmitter integrates and transmits data from a camera sensor and a temperature sensor to provide congestion status and environmental information. The transmitter can also integrate and transmit data from a sound sensor and a vibration sensor to provide noise levels and ground conditions. Furthermore, the transmitter can integrate and transmit data from an illuminance sensor and a humidity sensor to provide visibility and humidity information. This allows for more comprehensive information to be provided by integrating data from different sensors. Some or all of the above-described processing in the transmitter may be performed using, for example, AI, or may be performed without AI.

[0080] The guidance system includes a transmitting unit that adjusts the content of transmission based on data from outside the facility. For example, the transmitting unit takes into account weather information and prioritizes transmitting indoor routes when it is raining. The transmitting unit can also take into account traffic information and prioritize transmitting routes that avoid congested roads. Furthermore, the transmitting unit can integrate weather and traffic information to transmit the optimal route. This allows for providing more appropriate information by taking into account data from outside the facility. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI.

[0081] The guidance system includes a transmission unit that customizes transmission data by reflecting the user's past travel history. The transmission unit transmits, for example, an optimal route based on routes the user has used in the past. The transmission unit can also transmit a route that avoids congestion based on the user's past travel history. Furthermore, the transmission unit can analyze the user's past travel history and transmit the most efficient route. This makes it possible to provide more personalized information by reflecting the user's past travel history. Some or all of the above-described processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The guidance system includes an analysis unit that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The analysis unit can improve the accuracy of prediction of the current flow of people by referring to past data. The analysis unit, for example, refers to past congestion data to predict the current congestion situation. The analysis unit can also refer to past event data to predict the impact of a current event. Furthermore, the analysis unit can also refer to past weather data to predict the impact of current weather. In this way, by referring to past data, the accuracy of prediction of the current flow of people is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0084] The analysis unit can improve the analysis accuracy by combining different analysis algorithms. The analysis unit can improve the analysis accuracy by combining, for example, a machine learning algorithm and a statistical analysis algorithm. The analysis unit can also improve the analysis accuracy by combining a deep learning algorithm and a rule-based algorithm. Furthermore, the analysis unit can improve the analysis accuracy by combining a clustering algorithm and a regression analysis algorithm. In this way, the analysis accuracy is improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0085] The analysis unit can detect abnormalities in the flow of people in a specific area or time period. For example, the analysis unit detects an abnormality when more people than usual are gathered in a specific area. The analysis unit can also detect an abnormality when fewer people than usual are present during a specific time period. Furthermore, the analysis unit can detect an abnormality when there is a sudden change in the flow of people in a specific area. This enables rapid response by detecting abnormalities in the flow of people in a specific area or time period. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can prioritize displaying analysis results related to safety. Furthermore, if the user is relaxed, the analysis unit can prioritize displaying analysis results related to general congestion conditions. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results related to the shortest route. This allows for more appropriate information to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The analysis unit can integrate and analyze different data sources within the facility. For example, the analysis unit can integrate and analyze data from a temperature sensor and a sound sensor to provide information on the occupancy level and the environment. The analysis unit can also integrate and analyze data from an illuminance sensor and a humidity sensor to provide visibility and humidity information. Furthermore, the analysis unit can integrate and analyze data from a camera sensor and a vibration sensor to provide information on the occupancy level and the ground condition. This enables more comprehensive analysis by integrating different data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The analysis unit can adjust the analysis results by taking into account data outside the facility. The analysis unit can, for example, adjust the analysis results for areas where congestion is expected by taking into account event information. The analysis unit can also adjust the analysis results for areas where an emergency is occurring by taking into account news information. Furthermore, the analysis unit can also adjust the analysis results for areas affected by weather by taking into account weather information. In this way, by taking into account data outside the facility, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI.

[0089] The analysis unit can customize the analysis results by reflecting the user's past movement history. The analysis unit provides optimal analysis results based on, for example, routes the user has used in the past. The analysis unit can also provide analysis results that avoid congestion based on the user's past movement history. Furthermore, the analysis unit can analyze the user's past movement history and provide the most efficient analysis results. This makes it possible to provide more personalized analysis results by reflecting the user's past movement history. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0090] The location identification unit can estimate the user's emotions and adjust the accuracy of location identification based on the estimated user emotions. For example, if the user is feeling anxious, the location identification unit can increase the accuracy of location identification to provide detailed location information. Furthermore, if the user is relaxed, the location identification unit can lower the accuracy of location identification to provide the minimum necessary location information. Furthermore, if the user is in a hurry, the location identification unit can set the accuracy of location identification to a medium level to provide quick and appropriate location information. This allows for more appropriate location information to be provided by adjusting the accuracy of location identification according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The location determination unit can improve the accuracy of location determination by combining different location information technologies. The location determination unit can improve the accuracy of location determination by combining, for example, GPS and Wi-Fi data. The location determination unit can also improve the accuracy of location determination by combining Bluetooth and Wi-Fi data. Furthermore, the location determination unit can improve the accuracy of location determination by combining GPS and Bluetooth data. In this way, the accuracy of location determination is improved by combining different location information technologies. Some or all of the above-described processing in the location determination unit may be performed, for example, using AI or without using AI.

[0092] The location identification unit can adjust the frequency of location identification based on the congestion level of a specific area within the facility. For example, in a congested area, the location identification unit can increase the frequency of location identification to provide detailed location information. In a non-congested area, the location identification unit can also decrease the frequency of location identification to provide the minimum necessary location information. Furthermore, in a medium-congested area, the location identification unit can set the frequency of location identification to a medium level to provide appropriate location information. In this way, by adjusting the frequency of location identification based on the congestion level of a specific area within the facility, it is possible to appropriately provide necessary information. Some or all of the above-described processing in the location identification unit may be performed, for example, using AI or without using AI.

[0093] The location identification unit can improve the accuracy of location identification by referring to the user's past movement history. The location identification unit can improve the accuracy of location identification based on, for example, routes used by the user in the past. The location identification unit can also improve the accuracy of location identification to avoid congestion based on the user's past movement history. Furthermore, the location identification unit can analyze the user's past movement history and provide the most efficient accuracy of location identification. In this way, the accuracy of location identification is improved by referring to the user's past movement history. Some or all of the above-mentioned processing in the location identification unit may be performed, for example, using AI or without using AI.

[0094] The location identification unit can estimate the user's emotions and determine the priority of location identification based on the estimated user emotions. For example, if the user is feeling anxious, the location identification unit can prioritize providing safety-related location information. Furthermore, if the user is relaxed, the location identification unit can prioritize providing general location information. Furthermore, if the user is in a hurry, the location identification unit can prioritize providing location information related to the shortest route. This allows for more appropriate information to be provided by determining the priority of location identification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The location identification unit can identify a location by integrating data from different sensors within the facility. For example, the location identification unit can integrate data from a camera sensor and a temperature sensor to identify a location and provide congestion status and environmental information. The location identification unit can also integrate data from a sound sensor and a vibration sensor to identify a location and provide noise levels and ground conditions. Furthermore, the location identification unit can integrate data from an illuminance sensor and a humidity sensor to identify a location and provide visibility and humidity information. This enables more comprehensive location identification by integrating data from different sensors. Some or all of the above-mentioned processing in the location identification unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The location identification unit can adjust the accuracy of location identification by taking into account data outside the facility. For example, the location identification unit can take into account weather information and prioritize indoor routes when it is raining. The location identification unit can also take traffic information into account to perform location identification that avoids congested roads. Furthermore, the location identification unit can integrate weather and traffic information to perform optimal location identification. In this way, more appropriate location information can be provided by taking into account data outside the facility. Some or all of the above-mentioned processing in the location identification unit may be performed, for example, using AI or without using AI.

[0097] The location identification unit can customize the location identification method by reflecting the user's past movement history. The location identification unit can provide an optimal location identification method based on, for example, routes the user has used in the past. The location identification unit can also provide a location identification method that avoids congestion based on the user's past movement history. Furthermore, the location identification unit can analyze the user's past movement history and provide the most efficient location identification method. In this way, more personalized location information can be provided by reflecting the user's past movement history. Some or all of the above-mentioned processing in the location identification unit may be performed, for example, using AI or without using AI.

[0098] The notification unit can estimate the user's emotions and adjust the notification expression method based on the estimated user's emotions. For example, if the user is feeling anxious, the notification unit can provide the notification in a calm voice. If the user is relaxed, the notification unit can also provide the notification in a cheerful voice. Furthermore, if the user is in a hurry, the notification unit can also provide the notification in a quick and concise voice. This allows for more appropriate information to be provided by adjusting the notification expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] The notification unit can adjust the timing of the notification based on the user's current situation. For example, if the user changes their walking speed, the notification unit adjusts the timing of the notification in real time. The notification unit can also adjust the timing of the notification in real time if the user changes their direction. Furthermore, the notification unit can pause the notification when the user stops and resume it when they start walking again. This allows the notification timing to be adjusted according to the user's current situation, making it possible to provide more appropriate information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0100] The notification unit can improve the effectiveness of the notification by combining different notification means. The notification unit, for example, provides notification by combining vibration and sound to guide a visually impaired person to a safe route. The notification unit can also provide notification by combining vibration and light to guide a visually impaired person to a safe route. Furthermore, the notification unit can provide notification by combining sound and light to guide a visually impaired person to a safe route. In this way, the effectiveness of the notification can be improved by combining different notification means. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0101] The notification unit can improve the accuracy of notifications by referring to the user's past notification history. The notification unit, for example, provides an optimal notification method based on notifications received by the user in the past. The notification unit can also provide a notification method to avoid congestion based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and provide the most efficient notification method. In this way, the accuracy of notifications is improved by referring to the user's past notification history. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0102] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can prioritize safety-related notifications. Furthermore, if the user is relaxed, the notification unit can prioritize notifications about general congestion conditions. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications about the shortest route. This allows more appropriate information to be provided by determining the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The notification unit can adjust the notification content by integrating different data sources within the facility. For example, the notification unit can integrate data from a temperature sensor and a sound sensor to provide a notification and congestion status and environmental information. The notification unit can also integrate data from an illuminance sensor and a humidity sensor to provide a notification and visibility and humidity information. Furthermore, the notification unit can integrate data from a camera sensor and a vibration sensor to provide a notification and congestion status and ground condition. In this way, by integrating different data sources, more comprehensive information can be provided. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0104] The notification unit can adjust the notification content by taking into account data outside the facility. For example, the notification unit can take into account weather information and prioritize indoor routes when it is raining. The notification unit can also take traffic information into account and provide notifications to avoid congested roads. Furthermore, the notification unit can integrate weather and traffic information to provide optimal notifications. In this way, more appropriate information can be provided by taking into account data outside the facility. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0105] The notification unit can customize the notification method by reflecting the user's past movement history. The notification unit can provide the optimal notification method based on, for example, routes the user has used in the past. The notification unit can also provide a notification method for avoiding congestion based on the user's past movement history. Furthermore, the notification unit can analyze the user's past movement history and provide the most efficient notification method. This makes it possible to provide more personalized information by reflecting the user's past movement history. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI, or can be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned transmission unit, analysis unit, location identification unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the transmission unit is realized by either the data processing device 12 or the smart device 14. For example, the transmission unit can monitor the flow of people using the camera 42 or a sensor of the smart device 14 and transmit the data to the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the data transmitted from the transmission unit. The location identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the user's current location based on the analyzed data. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the user by vibration or sound, guiding the user to a safe route. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described transmission unit, analysis unit, location identification unit, and notification unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the transmission unit is realized by either the data processing device 12 or the smart glasses 214. For example, the transmission unit can monitor the flow of people using the camera 42 or a sensor of the smart glasses 214 and transmit the data to the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the data transmitted from the transmission unit. The location identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the user's current location based on the analyzed data. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user by vibration or sound, guiding the user to a safe route. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned transmission unit, analysis unit, position identification unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the transmission unit is realized by either the data processing device 12 or the headset type terminal 314. For example, the transmission unit can monitor the flow of people using the camera 42 or a sensor of the headset type terminal 314 and transmit the data to the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the data transmitted from the transmission unit. The position identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the user's current location based on the analyzed data. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314 and notifies the user by vibration or sound, guiding the user to a safe route. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned transmission unit, analysis unit, position identification unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the transmission unit is realized by either the data processing device 12 or the robot 414. For example, the transmission unit can monitor the flow of people using the camera 42 or a sensor of the robot 414 and transmit the data to the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the data transmitted from the transmission unit. The position identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the user's current location based on the analyzed data. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user by vibration or sound, guiding the user to a safe route.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The guidance system includes a notification unit that estimates a user's emotions and customizes the content of notifications based on the estimated user emotions. For example, if the user feels anxious, the notification unit provides detailed safety information. Furthermore, if the user feels relaxed, the notification unit can also provide general guidance information. Furthermore, if the user is in a hurry, the notification unit can also provide information about the shortest route preferentially. This allows the notification content to be customized according to the user's emotions, thereby providing more appropriate information. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] The guidance system includes a notification unit that monitors the user's walking speed in real time and adjusts the timing of notifications according to the speed. For example, if the user is walking fast, the notification unit increases the frequency of notifications to provide prompt guidance. Also, if the user is walking slowly, the notification unit can reduce the frequency of notifications to provide the minimum necessary information. Furthermore, the notification unit can pause notifications when the user stops and resume them when the user starts walking again. This allows the system to provide more appropriate information by adjusting the timing of notifications according to the user's walking speed.

[0109] The guidance system includes a notification unit that estimates a user's emotion and adjusts the tone of the notification voice based on the estimated user emotion. For example, if the user is feeling anxious, the notification unit may provide the notification in a calm tone. If the user is relaxed, the notification unit may also provide the notification in a bright tone. Furthermore, if the user is in a hurry, the notification unit may provide the notification in a quick and concise tone. This allows for more appropriate information to be provided by adjusting the tone of the notification voice according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The guidance system includes a notification unit that customizes the content of notifications by referring to the user's past movement history. The notification unit provides optimal notification content based on, for example, routes the user has used in the past. The notification unit can also provide notification content to avoid congestion based on the user's past movement history. Furthermore, the notification unit can analyze the user's past movement history and provide the most efficient notification content. This makes it possible to provide more personalized information by referring to the user's past movement history.

[0111] The guidance system includes a notification unit that integrates data from different sensors within the facility and adjusts notification content. For example, the notification unit integrates data from a temperature sensor and a sound sensor to provide congestion status and environmental information. The notification unit can also integrate data from an illuminance sensor and a humidity sensor to provide visibility and humidity information. Furthermore, the notification unit can integrate data from a camera sensor and a vibration sensor to provide congestion status and ground conditions. This allows for more comprehensive information to be provided by integrating data from different sensors.

[0112] The guidance system includes an analysis unit that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] The guidance system includes a notification unit that adjusts notification content by taking into account data outside the facility. For example, the notification unit takes into account weather information and prioritizes notifying indoor routes when it is raining. The notification unit can also take traffic information into account and provide notifications to avoid congested roads. Furthermore, the notification unit can integrate weather and traffic information to provide optimal notifications. This allows for more appropriate information to be provided by taking into account data outside the facility.

[0114] The guidance system includes a transmission unit that estimates a user's emotions and determines the priority of data to be transmitted based on the estimated user emotions. For example, if the user feels anxious, the transmission unit prioritizes transmitting safety-related data. Furthermore, if the user feels relaxed, the transmission unit can also prioritize transmitting general congestion status data. Furthermore, if the user is in a hurry, the transmission unit can also prioritize transmitting data related to the shortest route. This allows for more appropriate information to be provided by determining the priority of data to be transmitted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The guidance system includes a transmission unit that customizes transmission data by reflecting the user's past travel history. The transmission unit transmits, for example, an optimal route based on routes the user has used in the past. The transmission unit can also transmit a route that avoids congestion based on the user's past travel history. Furthermore, the transmission unit can analyze the user's past travel history and transmit the most efficient route. This makes it possible to provide more personalized information by reflecting the user's past travel history.

[0116] The guidance system includes a location identification unit that estimates a user's emotions and adjusts the accuracy of location identification based on the estimated user emotions. For example, if the user feels anxious, the location identification unit increases the accuracy of location identification to provide detailed location information. Furthermore, if the user feels relaxed, the location identification unit can also reduce the accuracy of location identification to provide the minimum necessary location information. Furthermore, if the user is in a hurry, the location identification unit can set the accuracy of location identification to a medium level to provide quick and appropriate location information. This allows for more appropriate location information to be provided by adjusting the accuracy of location identification according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The transmitter transmits data on the flow of people from the facility. For example, the transmitter can monitor the flow of people using a camera or a sensor and transmit the data. Step 2: The analysis unit analyzes the data sent by the transmission unit. For example, the analysis unit may use AI to analyze the data and understand congestion conditions and patterns of people flow. Step 3: The location determination unit determines the user's current location based on the data analyzed by the analysis unit. For example, the location determination unit can accurately determine the user's current location using location information technology. Step 4: The notification unit notifies the user of a safe route based on the current location identified by the location identification unit and the analyzed data. For example, the notification unit notifies the user by vibration or sound, and guides the user to a safe route.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 transmitter that transmits data on the flow of people from the facility; an analysis unit that analyzes the data transmitted by the transmission unit; a location identification unit that identifies a current location of the user based on the data analyzed by the analysis unit; a notification unit that notifies a safe route based on the current location identified by the location identification unit and analyzed data; Equipped with A system characterized by:

2. The transmission unit Use cameras or sensors to monitor people flow 2. The system of claim 1.

3. The analysis unit Analysis using AI 2. The system of claim 1.

4. The position identification unit Use location technology to identify the user's current location 2. The system of claim 1.

5. The notification unit Vibration or audio notifications guide users to a safe route 2. The system of claim 1.

6. The notification unit Notifications are sent via a notification device mounted on the white cane and an external device.

2. The system of claim 1.

7. The transmission unit Estimate user emotions and adjust the frequency of sending data on people flow based on the estimated user emotions.

2. The system of claim 1.

8. The transmission unit Learns traffic patterns at different times and days of the week to determine optimal delivery timing 2. The system of claim 1.

Images